It’s a Tuesday. A founder shares their screen. There’s a slide with a number that went from 34 to 51, arrow pointing up, and underneath it a screenshot of an answer where their company got named first. They’ve been paying between $8k and $40k a month for a year to make that slide exist.

They look at it for a second. Then they say the thing all twenty of them say, and it isn’t “we got scammed.” It’s a bit more careful than that.

“I think we’re doing the work. I just can’t tell if it’s the right work.”

That sentence is why this exists.

Why I’m the one saying it

I didn’t want to be a voice in this market.

Still forever have imposter syndrome. I’m a high school dropout. No degree doing quiet work in the background, no school anybody recognized, no network that arrived free with it. What I had was the willingness to show up and put a number next to my name that nobody in the room could argue with. Credentials were never available to me. Proof was the only currency I had, which is where the rule came from and I held it too hard: those who can’t do, teach. It’s also why a number on a slide that nobody can reproduce makes the hair stand up on my neck.

Then this thing broke, and for most of a year the loudest conversation in the field was whether to call it AEO, GEO or AIO. An industry naming itself before it had learned anything. Meanwhile my team at Webflow was inside it, building and measuring and putting revenue against signups arriving from assistants, with answers to questions nobody in public had thought to ask because we’d been forced to find them to hit a number.

So I swallowed my pride, broke my own rule, and started publishing the work with the results attached whether they were good or not.

What I didn’t expect is that when you become someone people bring things to, they bring you everything. Roadmaps. Scopes of work. Monthly reports. Renewal decks. Dashboards with the password typed into the chat. I read them the way an operator reads them, which is by asking what each line item is supposed to do to the business.

That’s the part that’s hard to sit with. This is not a hit piece. Sometimes the agency is the problem, sometimes the client is, and I’ll take both apart. But I’ve read enough of these documents to say plainly that the average one is not good, and the people signing them are sharp operators with no way to know that yet, because there’s no reference point in a field this young. Some agencies and advisors are exceptional at this. Props to them, and none of what follows is aimed their way. When I’m at capacity I send people to them, which is the only endorsement in this business that costs the endorser anything. Every question below is one they would enjoy being asked.

Where my bias is

Loud and up front, not buried in a footer.

I own a business that does this work. StackedGTM.AI is a media and advisory business, and the companies reading this are the companies I sell to. Which means there’s an obvious version of this article: a list of reasons your agency is failing you, a sympathetic nod, and a phone number at the bottom.

This is not that, and here’s how you hold me to it. There’s no call to action at the end. Every diagnostic below is something you run yourself in an afternoon, with no tools and nobody new on payroll, including me. Several of them will make my own sales conversations harder, because a buyer who has run this test walks in asking much better questions than one who hasn’t.

That’s the trade and I’m making it on purpose. If the standard I’m holding agencies to doesn’t apply to me first, none of it counts.

First, run the calendar

The urgency broke about eighteen months ago. Not the idea, the urgency. Buyers started opening a chat window instead of a search tab, pipeline arrived from places nobody had a dashboard for, GA4 got strange, and every board deck grew a slide about it.

Agencies spent the next two quarters building an offering. Sit with what that actually means. You have a services business with a P&L built on retainers, a delivery team trained on keyword clusters and content briefs, and a sales team fielding a question they cannot answer with “we don’t do that.” So they don’t. You rename the practice, write a point of view, build a dashboard, put AEO on the site, and go sell it.

Contracts got signed about a year ago. The first ninety days worked. Genuinely worked, and I’ll come back to that.

We’re now a year into the doing. The first real cohort of AEO retainers is up for renewal, the founders and CMOs who signed them are being asked what they got, and the honest answer for a lot of them is a great first quarter followed by nine months of a flat line dressed up as volatility.

Most of these engagements didn’t fail. That’s what everybody gets wrong. They succeeded once, and the agency mistook a one-time correction for a repeatable method.

That distinction is the difference between firing somebody and fixing something.

Why the increment died

If your curve went vertical and then went sideways, you’re the pattern, not the outlier. Here’s what sits underneath it.

The first quarter was a correction. A year and a half ago, essentially every company was in a state of basic neglect. Bot access misconfigured. No comparison pages. Entity data a mess: wrong founding year, three different company descriptions, a product renamed a year earlier that half the web still called the old thing. Content that only existed after JavaScript ran. Nobody had done the hygiene because until then there was no reason to.

You hired someone, they fixed it, and you got a step function. Everybody got a step function. It looked like the playbook working. It was the baseline being unoccupied.

Then the room filled up. Over the following few quarters, every vendor in your category hired their own version of your agency and ran the same corrections. Presence stopped being differentiation the moment it became universal. Your absolute position looks similar. Your relative position is gone. A report that tracks only your own trend line will never show you that, and nobody grades on a curve except the market.

The fight moved from retrieval to synthesis, and the playbook only covers retrieval. Nearly all of that first-quarter work was about getting pulled in. Crawlable, structured, present. That layer is table stakes across your whole category now. What’s left is the harder half: what the model says about you once it has everybody’s documents in hand. That gets decided by third-party consensus, by recent real signal, by what your customers say in places you don’t own. Different problem, different levers. Most agencies never built the second half, because the first half is where the deliverables lived.

Vendor-authored content is worth less than it was. This is a pattern read rather than a documented mechanism, so weigh it accordingly, but I’d bet on it. When fifteen vendors all have equally well-optimized owned surfaces making equally confident claims, something has to break the tie, and it won’t be the marketing copy. It’ll be whatever reads as independent. Which makes the core deliverable of most retainers a depreciating asset. Same monthly fee, less bought every quarter.

Volatility gives everyone cover. Because output shifts run to run, a flat trend is indistinguishable from noise unless somebody is sampling properly and wants to know. Six months of nothing gets reported as holding steady in a turbulent environment. That framing isn’t always dishonest. It is always convenient, and the person delivering it may not have noticed which.

And the renewal needs a story. So the pitch becomes expansion. More prompts, more markets, more engines. Horizontal growth of a playbook that stopped compounding, sold to you as ambition. Nobody in that meeting is lying. They’re just never going to be the ones to tell you the engine stalled.

Hold all of that in your head and the list below stops being red flags. It becomes a test of whether your partner has a second act.

One thing before we start. Some of what looks like a bad agency is a bad client. That section is near the bottom and I’d read it before you fire anybody. I’ve watched teams torch a workable partnership because they wanted a quarterly result out of a job that takes three quarters, then go hire somebody worse who promised it faster.

1. They’re a content shop with a new name on the door

Read the scope of work before you read a single report. If the deliverables are twelve articles a month, a few landing pages and a schema audit, you didn’t buy AEO. You bought content marketing with a different acronym on the invoice.

The most expensive misunderstanding in this market, and I’ve watched it burn entire annual budgets: more owned content does not move the answer. Your website is a thin slice of what decides what gets said about you, and it’s the slice with the most reason to be discounted, because the system knows you’re the vendor.

Pull a real answer to a question your buyer would ask and look at where it’s sourcing from. Reddit threads. A review profile. A listicle from a media company you’ve never heard of. A YouTube transcript. A competitor’s comparison page where you’re filed under alternatives. A forum post from 2023. Somebody’s newsletter. Count how many of those you own. Usually one. Often zero.

Now open the last report they sent and count what’s being measured. Pages you published. Mentions on your domain. Your comparison page. Your glossary. They are measuring the one surface that isn’t deciding the outcome.

That mismatch is the business model, not an oversight. A shop built on publishing can only invoice for publishing. The work that moves the answer sits off your domain, in places you don’t control, and it’s slow and awkward and involves talking to people who don’t work for you. It doesn’t fit inside a monthly content calendar, so it never gets proposed.

A real plan has line items that make a content team visibly uncomfortable. Getting into the review corpus with enough recent, specific entries that a model has something usable to pull. Getting practitioners to write about you somewhere that isn’t your blog. Going after the comparison page your competitor owns instead of being dignified about it. Cleaning up the entity mess, meaning the six ways your company gets described across the web, the wrong founding year, the funding round nobody updated, the product you renamed that half the internet still calls the old name.

None of that is content. All of it moves the answer more than your next twelve posts, and that gap widens every quarter.

Ask: “What share of the sources cited across our category’s answers sit on a domain we own, and what’s the plan for the rest?”

If they can’t answer the first half, they have never looked at where the answer comes from. They’ve been optimizing in the dark and reporting on the light.

2. They talk in visibility and never in revenue

This one gets under my skin, probably because I carried a revenue number for a decade. It should get somebody fired, and nobody pushes on it, because the number is right there and it’s going up and it feels like progress.

Let me be careful here, because the lazy version of this critique is wrong. Attribution here is genuinely hard. Some assistants pass a referrer and some don’t, and none of them capture the bigger pattern, which is somebody reading an answer, closing the window, and typing your brand into Google two days later, where it lands in your reporting as branded organic. Anybody selling you deterministic attribution is lying with more confidence than the people selling you visibility.

But hard attribution is not a reason to stop at visibility. Visibility is a leading indicator, and leading indicators are only worth paying for if somebody can name the thing they lead to.

Here’s the chain. Every link is measurable, none are precise, and that’s fine.

• Presence on prompts that have money behind them. Weighted, not total. Share on a question nobody with a budget asks is worth nothing, and an agency that doesn’t weight by buyer intent is grading itself on the easy questions.

• Branded demand. Branded search and direct traffic are the shadow metric for this whole channel. If presence climbs three straight months and branded demand is dead flat, the thesis is wrong or the prompts are. That’s a finding, not an anomaly to bury.

• Self-reported attribution. The crudest instrument available and the most honest one. One free-text question on the demo form, plus one asking whether they used an assistant while researching. Messy, underreported, and still the only place your buyer tells you directly.

• What sales is hearing. Are buyers arriving pre-informed, fluent in the objection handling, already comparing you against two competitors you never sent them to? That moves before anything in the CRM does.

• Win rate and cycle length on deals where an assistant was involved. Rough and directional beats precise and irrelevant.

Then the thing that binds all five together, and the reason most of these engagements can never prove anything either way.

You can’t attribute a deal. You can compare two windows.

Take a clean period before the work started and a matched period after, and read the whole chain across both. Then hold the obvious confounders still: seasonality, paid spend, a funding announcement, a launch, a competitor going quiet. If you changed four things and one number moved, you learned nothing and you should say so.

The stronger version runs a control. Compare your movement against the category’s, or against a set of prompts you deliberately left alone. Category-wide lift is weather. Lift against a flat control is work.

There’s a diagnostic hiding inside all of it. Pre and post requires a before. If nobody captured a baseline at kickoff, whether any of this worked is permanently unanswerable, by anyone, forever. Baseline capture is a day-one deliverable and I have almost never seen one in a scope of work. Go look for yours. If it isn’t there, that tells you what the first ninety days were really for.

Ask: “Show me the baseline you captured at kickoff and the same measures today, with whatever you controlled for. Then, if this works perfectly for twelve months, which number on our P&L moves, roughly how much, and when do I see the first evidence?”

You’re not asking for a guarantee. You’re asking whether they’ve thought past the dashboard. Somebody serious hands you a chain with a confidence level attached and names the links they can’t see. Somebody who hasn’t will tell you attribution is impossible and change the subject back to visibility. Attribution being hard is true. It’s also the most convenient sentence in this industry, and you should notice who reaches for it first.

3. They’ve never shown you a competitor

Go back through a year of reporting and count how many times you saw another vendor’s name attached to a number.

This is a share game. Presence stopped being the metric the moment your whole category became present, and you don’t get a date for when that happened. Nobody announces it. You find out by looking, which is the point. What matters now is the frequency table: across your twenty highest-intent prompts, which vendors get named, how often, and which direction each one is moving.

Without that, your own trend line can’t be read. Flat could mean you stalled. Flat could also mean you held position while two competitors surged past you, which is a much worse situation reported as the identical picture. Up could mean the work is working, or it could mean the category rose together and you paid a retainer for weather.

There’s a second reason this beats any audit. The competitor who keeps getting named on the prompts you lose is running an experiment on your behalf, in public, for free. Go look at what sits behind their answers. It’s almost never their website. It’s a review profile with ninety recent entries, or a founder who has spent two years answering questions in the same three communities, or one comparison page on a publication everyone in your space reads.

Ask: “Show me the vendor frequency table for our top twenty prompts, us against the four competitors we actually lose to, over the last six months. Then tell me what the winner has that we don’t.”

4. Their proof is a screenshot and a score you can’t reproduce

Run the same prompt five times in five fresh chats. You’ll get up to five different answers. Different sources, different order, sometimes a different set of vendors. Reword it slightly and it moves. Logged in versus logged out, it moves. Different day, model version or country, it moves.

So a report leading with one good screenshot isn’t evidence. It’s a lottery ticket somebody found and framed. And this is how these engagements survive a flat year: any agency can produce a favorable screenshot for almost any client on almost any prompt if they’re willing to run it enough times and only show you the winner. I’m not accusing anybody specifically. I’m telling you the tooling makes it trivial and you would never know.

The composite score has the same disease in a nicer suit. Everyone has an index now. Visibility Score, Answer Share, AI Presence. A number out of a hundred that went from 34 to 51. The slide from the top of this piece.

Three questions. What’s the formula. What’s the exact prompt set, and who chose those prompts. Can my team reproduce the number without your help.

If the prompt set is theirs and they won’t hand it over, the metric can’t be falsified. It can only go up, because the people choosing the inputs are the people being graded on the output. I’m not against composite metrics, I’ve built them. But a metric you can’t audit is a story, and you’re paying retainer for stories.

Ask: “What’s your sampling methodology: runs per prompt, models, versions, logged in or out? Send me the raw logs including the losses, and the prompt set behind the index.”

Somebody serious is quietly pleased you asked. Somebody who isn’t will explain why sampling doesn’t matter.

5. Nothing they do is a test

Everything ships everywhere at once. Twelve pages, a schema pass, a technical fix and an outreach push all land in the same six weeks, the number moves, and the report tells you the number moved.

Which of those four did it? Nobody knows. Not you, and here’s the uncomfortable part, not them either.

An agency that never isolates a variable has no idea which of its own tactics works. It has a bundle. Maybe two things in that bundle do all the lifting and the other eight are funded by them. A year in they still can’t tell you which is which, so they keep selling you all ten. That isn’t dishonesty. It’s what happens when nobody ever designed a test.

Real work has a shape you can recognize. A hypothesis written down before anybody started. A subset it gets applied to. Something held back as a control, even a crude one, even just a comparable set of prompts left untouched. A timebox. And a readout capable of returning the word no.

That last part is the whole thing. If no possible outcome would cause them to stop doing an activity, it was never a test. It was a deliverable with a story attached.

Ask: “Name one change you made last quarter, the hypothesis behind it, what you deliberately left alone as a comparison, and what it did. Then name one thing you’ve killed.”

6. They talk about “ranking”

There’s no ranking you can climb. There’s a retrieval step, where documents get pulled and scored against each other, and a synthesis step, where the model decides what to say about the ones it pulled. Two problems, two sets of levers, and your category already commoditized the first.

You can be retrieved and destroyed in the synthesis. Your page gets pulled, and the answer explains that you’re the expensive option for enterprises with a procurement department, sourced from your own pricing page. You were cited into a loss, and every citation-counting report on earth files that as a win.

The word is a tell, but the plan is the proof. Go through the scope of work and sort every line item into the two buckets. Access, structure, crawlability and publishing are all retrieval. Then look at what’s left. If the synthesis column is empty, that’s your gap, and it’s the expensive one, because retrieval is where your competitors already caught you.

The levers on that side are different and they’re slower. What independent sources say when they describe you. How recent that material is. Whether the specific objection that kills your deals has a credible public answer anywhere outside your own domain. Whether the descriptions of your product across the web agree with each other. None of that is a publishing task, which is why it rarely appears next to an owner and a due date.

Ask: “Sort your last two quarters of work into retrieval and synthesis for me. Then show me one case where we got retrieved and the answer still didn’t recommend us, and tell me what you changed.”

7. llms.txt was a deliverable

I want to be fair to the file before I use it as a murder weapon. It costs ten minutes, it’s harmless, it might matter later. Ship it.

But if it showed up as a line item with an owner and a due date and a place in the monthly report, you learned something about how that plan got built.

As of writing, no major assistant has confirmed llms.txt as a meaningful retrieval signal. It’s a proposal, not a standard. What it reliably is: easy to produce, easy to verify, easy to invoice. It photographs beautifully. You can put it on a slide with a green check next to it.

That’s a category, and once you can see it you’ll find it all over your scope of work. Call it the checkable artifact. Things chosen because they can be marked complete inside a billing cycle rather than because they move anything. An llms.txt file. FAQ schema bolted onto pages nobody reads. An “AI readiness audit” that turns out to be a slide deck. A summary paragraph added to two hundred old posts. Every one of those ships, screenshots and closes out. None of them are why you’re absent from the answer.

The reason this category dominates isn’t malice, it’s structural. Retainers need deliverables that complete on a monthly rhythm. The work that moves the answer doesn’t complete inside a month and doesn’t produce an artifact, so it loses to the file. Every time, in every agency, unless somebody deliberately stops it from losing.

Now the part that should make you put your coffee down.

An llms.txt file is a note you leave for a guest you’ve locked out of the building.

I’ve been on more than one call where a company was paying a five-figure monthly retainer to appear in a specific assistant while blocking that assistant’s crawler at the edge, and had been for months, and nobody on either side had checked. The file was shipped. It was in the report. The crawler meant to read it was getting a 403.

So when they tell you the file is done, go the other direction and ask about access at every layer, because there are more layers than people think and they’re owned by different teams. robots.txt is the one everybody checks. Then there’s your CDN’s bot rules, your WAF, and the bot-management product somebody in security bought two years ago without telling marketing. Any one of those can quietly refuse the exact crawlers you’re paying to court.

Then keep going down the fundamentals, in order of what they cost you.

Access. Which crawlers are allowed, at every layer, verified rather than assumed.

Rendering. If your content only exists after JavaScript executes, assume part of the retrieval layer never sees it. This one silently eats entire product sites.

Gating. Your best material, the implementation detail and the pricing logic and the real comparison, is usually behind a form, a login or a cookie wall. A model cannot cite what it cannot read, and your competitor with open docs is eating you alive on exactly the questions that close deals.

Plumbing. Status codes, redirect chains, whether the sitemap reflects reality, whether canonicals point somewhere sensible.

That’s the hierarchy. Access, rendering, gating, plumbing, and the optional files at the very bottom. Anybody working the last item before verifying the first is decorating.

Ask: “Walk me through every layer between the public internet and our content where a crawler could be blocked, and what you found at each one. Then tell me which of our highest-intent pages are gated.”

If they shipped you an llms.txt file and can’t answer that, you’re paying for the note, not the access.

8. Nobody is maintaining anything

Every plan I read is creation. Almost none have a maintenance line, and that gap gets more expensive every month.

Recency carries weight, and everything you own is quietly rotting. Your review profile is impressive and most of it is from 2023, which reads as a company people used to like. The roundup you fought to get into gets refreshed annually and you’re not in the new version. The comparison page that made you look good describes a pricing model you retired. Your docs cover a product two releases back. A third-party page cites you accurately for a feature you’ve since renamed, so now there are two names for the same thing and confidence in neither.

None of that requires anybody to do anything wrong. It happens through pure inertia and it eats a real share of whatever the first year bought you. Some meaningful part of your plateau is gains leaking out the back while somebody publishes new pages at the front.

The work is unglamorous and it’s mostly a list. Who owns review velocity as an ongoing number rather than a one-time push. Who re-pitches the roundups every cycle. Who keeps an inventory of external pages that describe you and gets the wrong ones corrected. Who checks that what the internet says you cost is what you actually cost.

Ask: “What’s on our refresh calendar for the next two quarters, and who owns getting third-party pages updated when the product changes?”

9. They count citations and never read the sentence

Being mentioned is not being recommended.

You can be cited as the expensive one. The one with the learning curve. The one that’s overkill for a team your buyer’s size. The one people migrate off. Every one of those is a citation and every one goes in the report as a win.

Almost nobody reads the sentence. No sentiment layer, no split between recommended and merely listed and actively warned about, no read on what role you play in the story. Just a count going up.

This matters more the deeper into the plateau you are. Once presence is universal in your category, how you get described is the only thing left that moves a deal.

Ask: “Of last month’s mentions, how many recommended us, how many just listed us, how many were negative? Show me the negative ones.”

10. Their prompt set has no buyer in it

If it’s “best [category] software” and forty variations, they’re tracking head terms because head terms are what they know how to track.

Nobody types “best CRM” in the week they’re about to spend eighty grand. They type the thing they’d be slightly embarrassed to ask a salesperson. Is this worth it for a team of twenty. Can it handle our compliance situation. Why do people leave this. What breaks at scale. Which of these two is easier to rip out in a year. What does implementation actually cost, not the sticker price.

Those prompts have a person inside them. They’re where the deal is won and lost, they’re harder to track, so they get skipped.

Every prompt set I’ve seen also stops at turn one, which is the more expensive half. Nobody asks one question and buys. They ask, they get three vendors back, then they push. Which of these is cheaper to run. Which will I regret in a year. What’s the catch with the first one you named.

You can be named in the opening answer and eliminated two turns later. The later turns are where the objection handling lives, where the model reaches past your surface copy for anything it can find about limitations, and where your competitor’s unhappy customers get their say. If your agency has only ever shown you opening answers, they’re reporting on the handshake and calling it the meeting.

One more thing belongs here. Nobody should be spreading effort evenly across five assistants. Your buyers concentrate somewhere. If the plan weights every engine the same, nobody asked where your pipeline comes from.

Ask: “Show me a full three-turn conversation for our two most valuable prompts, on the engine our buyers actually use.”

11. They offered to “handle Reddit”

Stop.

Community moves more of this than almost anything you can buy, which is exactly what makes it dangerous. There’s a bright line between being worth mentioning and manufacturing mentions, and the second one is a brand liability with a long fuse. Communities are very good at catching it. Moderators keep records. Screenshots outlive everybody.

You’re not buying visibility. You’re buying a headline about your company astroturfing, at a discount, on delay.

The legitimate version is slower and barely looks like marketing. Your employees participating as themselves over months. Your customers having something specific and good to say. Showing up where your category already talks and being useful there.

There’s no question to ask on this one. If an agency pitches you the fast version, you already have your answer, and it’s about what they think of your judgment.

12. Their answer to the plateau is more of the same, wider

This is the one you’ll meet in the renewal conversation.

You raise the flat line. They come back with expansion. Double the tracked prompts. Add three more engines. Extend into two more markets. Build a hub for an adjacent category. Every item is more of the exact thing that produced your step function in quarter one and nothing since.

Horizontal expansion of a plateaued playbook isn’t a strategy. It’s a renewal.

Here’s the shape of a real answer, and you can hear it in the first thirty seconds. A second act names something they’re going to stop doing. It moves budget from one column to another rather than adding a column. It includes at least one thing that can fail, and one thing that requires your team rather than theirs. If every item on the new plan is additive, nothing was learned in twelve months, because a year of real work always kills something.

Ask: “Our results were front-loaded. What’s structurally different about your next six months versus your first six?”

If the answer is a bigger version of the same list, you have your answer.

13. They’ve never retired a tactic

Ask which of their own tactics they’ve stopped using in the last year, and why. Then ask for the account where they did the work and the answers didn’t move.

Anybody with real reps has both and gives them up without much prompting, because it’s the most interesting thing they know. This field is a year and a half old. Every serious operator has watched something that worked in early 2025 quietly stop working and has an opinion about why. Nobody has a clean record, nobody has ten years of experience, and if somebody implies otherwise that’s its own answer.

An agency with no losses and nothing retired has no memory or no honesty. Both are expensive.

The test to run before your next call

Twenty minutes, no tools. Do it yourself, don’t delegate it, and don’t tell your agency you’re doing it.

1. Write ten prompts a real buyer would type in the week before they sign. Not category terms. The messy, specific ones they’d be slightly embarrassed to ask a salesperson.

2. Run each five times. Fresh chat every time. Logged out where you can. Note the model and the date.

3. For each answer, log three things. Were you mentioned. Were you recommended, merely listed, or warned about. And every source cited.

4. Tally the sources by domain and work out what share of the cited corpus you own.

Every time I’ve walked a team through this, the owned share comes back small. Usually much smaller than they expected. And the third-party sources are almost never the ones anybody has a plan for.

Then take the tally into your next call and ask three questions.

“What’s our plan for the sources we don’t own?”

“Which number on our P&L is this supposed to move, and when?”

“Our results were front-loaded. What’s structurally different about your next six months?”

The first tells you whether they understand the channel. The second, whether they understand the business. The third, whether they have anything left. You’ll know inside a minute on all three.

Before you fire them, check whether it’s you

I’ve watched this go the wrong direction enough times to put it in writing.

You gave a three-quarter job ninety days. Entity cleanup, review velocity, earned coverage, community presence. None of it lands in a quarter. If you signed expecting a Q1 result, the person who oversold you was in the room. So were you.

You didn’t hand over the keys. No engineering time. No access to product marketing. No permission to touch the review sites or ask customers for anything. No PR support. You hired a partner and gave them a blog. You’re going to get blog results.

You never told them which number mattered. If you never named the outcome, you can’t be furious that they optimized the one they could put on a slide. Visibility became the metric because nobody in the room asked for a better one.

You asked them to fix a reputation problem. This is the uncomfortable center of all of it. These systems are largely summarizing what real people already say about you. If your reviews say onboarding is a nightmare and the answers say onboarding is a nightmare, nothing is broken. No agency alive can talk a model out of a consensus your customers built. That’s a product conversation and a support conversation, and it sits above the agency’s pay grade.

What good actually looks like

Not credentials. There aren’t any yet. It comes down to one thing: will they show you their work?

The good ones hand over raw logs including the runs where you lost. They name the number they’re trying to move and tell you honestly which links in the chain they can’t see. They’ll tell you which of their own tactics they’ve stopped believing in and roughly when it stopped working. They have a written point of view on what they don’t know. They give you homework, because a real plan requires things only your team can do. They’ll tell you when a problem isn’t theirs to solve. And they’ll say “I don’t know, let’s test it” out loud, in front of you, without flinching.

The bad ones sell certainty. In a field this young, certainty is the loudest possible signal that somebody isn’t paying attention.

That list describes how I work, and I wrote it, so apply it to me too. That’s the reason to put a standard in public instead of keeping it in your head.

The easy half of this is finished. Every competitor you have is present, structured, crawlable and confident, and none of that is worth what it was worth last year. What’s left is the slow work of being the vendor that independent people actually recommend, in the places you don’t own, on the questions that cost money.

Some agencies were built for that. Most were built for the ninety days that are already over.

The founder on that Tuesday call told me they couldn’t tell whether they were doing the right work. That was the only thing they were wrong about.

You can tell. It takes twenty minutes.

It’s mid-quarter. You’re staring at a forecast that’s short, a paid budget you’ve already maxed, and a rep who keeps saying the deal is going to land. Somebody suggests a webinar. Somebody else says we should “get more aggressive on content.” You already know neither one closes the gap, and you’ve got about six weeks before you’re the one standing in front of the board explaining it.

I’ve sat in that seat plenty of times, it sucks. So take this as one operator handing another the play I reach for first when search is the lever with the most room to move.

Two things before we start, because you should know what you’re reading.

Almost every citation stat below comes from one place: AirOps' 2026 State of AI Search. 7,500 commercial queries, 217,508 pages, published methodology. It's one of the largest data sets anyone has put out on how these engines actually cite, and I lean on it throughout. What I've added is the field test. I've been running these plays with clients in the wild, so where I've confirmed a pattern in my own work, I say so, and where I'm extrapolating past what they measured, I say that too.

What’s mine is the order, the filters, and one number nobody else has. Three weeks into the current sprint, a client has four opportunities in HubSpot whose first touch traces back to an answer engine, roughly $180K in influenced pipeline, one already in final-stage negotiation. That’s the whole point of the sprint and I’ll show you exactly how we tagged it at the end, including the part that didn’t work cleanly.

The plays are the plays. Here they are.

First, why 30 days is even possible

If this were an SEO gap, I’d tell you to lower your expectations. SEO doesn’t move on a 30 day clock. Most teams wait three to six months to see anything, and closer to a year for numbers they can bring to a forecast review. The reason is structural. The pages sitting in the top 10 organic results are old. Ahrefs found that more than 7 in 10 of them are over three years old, and the average page in the number one spot is closer to five. You wouldn’t be optimizing. You’d be standing in line behind content that’s been compounding trust since before your category existed.

AEO doesn’t work that way, and that gap is the entire opportunity.

Citations don’t require rankings. In AirOps’ data, Kevin Indig’s analysis found that 60% of Google AI Overview citations go to pages that don’t rank in the top 20 organic results at all. That figure is specific to Overviews, and I’ll be straight with you that I haven’t seen an equivalent published number for ChatGPT or Perplexity. What I have seen, repeatedly, in client work is the same behavior: pages we never ranked top 20 for showing up as cited sources in chat answers. Treat the 60% as measured and the cross-engine version as my read from the field.

The mechanism is the same either way. The model isn’t reading a fixed leaderboard. It rebuilds the answer from scratch on every query, pulls a fresh set of pages each time, and rotates brands in and out as it goes. More than half the brands that fall out of an answer are back within two runs. That churn feels like instability when you’re winning. When you’re not in the answer yet, it’s the way in.

So the recovery moves fast for a reason most teams miss. In SEO you have to outlast the incumbents. In AEO you have to out-structure them, and structure is something you can change this week.

One more number makes the whole sprint work, and it’s the one most teams have never looked at.

Your problem is conversion, not discovery

AirOps looked at 7,500 commercial queries and the 217,508 pages ChatGPT pulled in to answer them. Of every page the model retrieved, 15% earned a citation. The rest got read and set aside.

For most teams, the models aren’t failing to find you. They’re finding you and declining to cite you. You’re already in the retrieval pool. You’re losing at the citation layer.

That’s the best news you’ll get all quarter. Closing a conversion gap on pages you already own is fast. Building new authority from nothing is slow. This sprint works because it spends every hour on the first problem and refuses to touch the second.

So one rule before we start. When you’re behind, the reflex is to greenlight a big new content program. Kill that reflex. New pages have to claw their way into the retrieval pool before they earn a single citation, and that’s the one thing 30 days can’t buy. Every play below runs on inventory that’s in the pool right now.

Four plays, in the order I run them.

One caveat before the plays, and it’s a real one. AEO is probabilistic. Run the same query twice and you can get a different set of sources back. Two companies running this identical sprint won’t get identical results. These are the four plays with the highest likelihood of a fast return, and that’s not the same thing as a guarantee.

What you actually get depends on how crowded your category is, how much inventory you already have sitting in the retrieval pool, how fast your team can ship a change, and whether the content is worth citing once a model can finally read it. Structure gets you considered. Quality is what gets you kept. If your comparison page is thin, putting it in a table just makes it easier for a model to see that it’s thin.

Play 1: Reclaim the pages you already get retrieved for

This is the fastest lever in AEO and often isn’t optimized.

Most people hear “refresh your content” and tune out. Since the answer gets rebuilt on every query, a page you’ve genuinely updated gets reconsidered on the very next run, while a stale one keeps getting passed over. So you don’t refresh everything. You refresh the pages that are already retrieved or mentioned but not cited, plus any page that was cited last month and has since dropped. Those pages cleared the hard part. They’re in the pool. They’re one signal away from the citation.

The data behind it: AirOps found that pages untouched for more than three months are over three times more likely to lose their citations, and that 83% of citations on commercial queries land on pages updated within the last year. In SaaS and fintech the window is tighter. Past three months, citation likelihood drops hard.

Here’s the filter I build first. One view: cited equals no, and retrieved or mentioned equals yes. A second view: cited last month equals yes, cited now equals no. Stack them. Sort the whole thing by how commercial the query is, so your pricing, “vs,” and “best” pages rise to the top. That sorted list is your work order for the week. Nothing off that list gets touched.

Per page, this is a 20-minute job. The edits are real. The new date only reflects them:

• Fix any pricing, claims, or numbers that have changed since the page went live.

• Swap the oldest statistic for a current one. New data is the cheapest credibility you can add.

• Break your densest paragraph into a short list the model can lift.

• Now that the page has actually changed, update the publish date to match.

• Resubmit the URL for indexing, then move to the next page.

You’re re-entering rotation.

Then watch that set as a cohort against your untouched pages, and re-pull it every Monday to mark which ones came back. Don’t blend it into your whole-site numbers or the result vanishes into the noise and you won’t be able to defend it to anyone.

Expect the cohort to be lumpy. In the work I’ve run, a chunk comes back inside two weeks, a chunk takes the full month, and some never do. The ones that never come back are usually competing with a third-party roundup that’s simply a better answer to the query than any vendor page could be. That’s Play 4’s problem, not Play 1’s, and knowing which bucket a page is in saves you from refreshing it four more times.

The teams that win this just run more cycles.

Chime took content production from days to minutes per brief, cut total production time 89%, and tripled their AI citations.

Venn ran the same play with a two-person team, took a two-hour process down to 10 minutes, and grew citations 600%.

I did this with my team at Webflow with a ton of success before we built in automations to do it on our behalf for high value pages.

The constraint is never ideas. It's how many cycles you can run before the quarter closes.

Play 2: Rebuild your comparison pages so the model can lift them

Play 1 is the fastest. This is the one that sits closest to a signed deal.

Comparison queries are at the bottom of the funnel. “Tool A vs Tool B.” “Best X for teams like mine.” The buyer already decided to buy something. They’re deciding what. In the AirOps data, comparison pages produced the single strongest citation lift of any commercial page type.

Here’s the part that stings. Your versus pages are almost certainly written as prose, and prose is nearly impossible for a model to quote cleanly in a side-by-side answer. So the model skips your page and pulls the comparison from a competitor or a review site that laid it out in a table. You wrote the page. Someone else gets cited from your own analysis.

Quick test before you rebuild anything. Read your versus page and try to say the verdict in one sentence. “Pick us if X, pick them if Y.” If you can’t do it in one clean line, the model can’t either, and it’ll quote a page that can.

The rebuild, with the thresholds I actually target:

• List every “vs” and “best” page you own.

• Turn the prose into tables. AirOps found three tables on a page earned 26% more citations. Build one for features, one for pricing, one for who each option is right for.

• Put real numbers in the pricing table. On comparison pages, 7 distinct price points earned 16% more citations. Ranges don't count.

• Cut the surrounding sentences short. On shortlist pages, sentences of 10 words or fewer earned 19% more citations.

Write like you expect to be quoted, because that’s the whole transaction.

When I ran growth at Webflow, this is the pattern we proved out. We restructured CMS and comparison content for extraction and pushed answer share on core CMS queries past 60%, with a few hundred incremental citations on top. Same pages, same claims, different structure.

I see it again as an advisory. A fintech I work with was getting retrieved on a dozen comparison queries and cited on almost none. We rebuilt three versus pages with tables and shorter copy, and the first pull after that showed nothing. The tables were there, but the pricing column had shipped inside an image component, so the numbers a model needed to lift weren’t text at all. We rebuilt that column as plain markup, and by the second pull they were cited on most of those queries, without a single new page or backlink. The lesson I took with me: “make it a table” isn’t the instruction. Make it text a model can read is the instruction.

Play 3: Make your pricing and feature pages extractable

This is the most neglected page on most sites and the closest one to revenue, which is a bad thing to leave alone.

A buyer asking an assistant to confirm a price or a feature isn’t browsing. They’re validating before they commit. That’s the moment AI referral earns its comparison to paid search, not because the volume matches yet, but because the intent does. The answer already did the qualifying. The person who clicks through has been pre-sold and arrives ready to talk.

Run the test yourself right now. Ask ChatGPT what your product costs. If it answers from a third-party page instead of yours, or gets the number wrong, you’ve lost your own pricing page. A stranger’s stale roundup is now the source of truth on what you charge. Most teams have never checked.

The reason it happens is design. A slick interactive slider with no extractable numbers gives a model nothing to cite, so it goes somewhere it can read.

The fix:

• Put your real pricing in plain, scannable lists underneath whatever design you want to keep. Tier name, price, seat count, the two or three limits that actually differ between plans. AirOps found pricing and validation pages with 8 list sections earned up to 27% more citations, and that on these pages the price-point effect keeps climbing past 15 distinct numbers for another 11%. That’s a different curve than the comparison pages in Play 2, where the lift peaks around 7. The read: on someone else’s turf you want the comparison legible, on your own page you want it complete.

• Keep it current. This is the page punished hardest for going stale, because it’s the page buyers verify last before they decide.

I caught this one live with a dev tools founder I’m close with. We asked ChatGPT what their Pro plan cost, and it quoted a competitor’s roundup, off by $20 a seat, because their own pricing page was a slider with no readable numbers. The fix took 20 mins to spec and three weeks to ship, stuck behind a design system migration nobody wanted to interrupt. That’s the real constraint on this play, by the way. It’s never the work. It’s the queue. By the next pull after it shipped, the model was quoting them at the right price, and nothing about the page looked different to a human.

This shows up in conversion too. Go! Retail Group rebuilt product pages for clarity at scale and saw a 13% lift in PDP conversion on the first test set. The same clarity that earns the citation closes the buyer.

Play 4: Get into the third-party lists that are already being cited

The first three plays live on your site. This one doesn’t, and it outweighs all three more than most teams want to admit.

AirOps found that 85% of brand mentions in commercial AI search come from third-party pages rather than your own domain, and that a brand is more than six times more likely to be cited through someone else’s page than its own. 90% of those third-party mentions come from listicles, comparison roundups, and review pages. Position isn’t random either. 80% of mentioned brands sit in the top three slots.

So this isn’t “do more PR.” It’s a targeting problem, and the engines will hand you the target list if you ask.

Find the pages. Ask ChatGPT and Perplexity your category question. “Best [category] tools for [your buyer].” Note every source cited. Run five or six phrasings of the same question and count how often each source repeats. The ones that show up in most runs are your target set. Your wishlist of publications is not.

Get added to those specific pages, and push to land in the top three, not buried at number nine. Three routes work, roughly in order of hit rate:

The correction. The page already mentions you and something on it is wrong or old. Highest-converting outreach in the whole play, because you’re doing the author’s job for them. This is the note I actually send: “Hey [name], you have [Brand] in your best-[category]-tools piece. Two of the data points are from 2024. Here’s the current pricing plus a 2026 benchmark stat you can cite, raw numbers attached if useful.” People say yes to that.

The gap. The page doesn’t mention you and shouldn’t, yet. Don’t pitch inclusion. Pitch the missing segment. Roundups get updated when the author realizes they’ve left out a use case their readers keep asking about, so lead with the segment and let your product be the example.

The data. You publish an original number the roundup can cite, and inclusion follows the citation. Slowest route, highest ceiling, and the only one that works on pages that don’t take pitches at all.

Set expectations on timing. Roundups get refreshed on the author’s cycle, not yours. A yes in week one often means a live update in week five or six.

What doesn’t work: mass outreach, paying for placement on low-authority roundups the engines never cite, and asking for the top slot in your first email. So many agencies are pushing this right now and I have yet to see it work.

Verify by re-running the query. Getting added to the page isn’t the outcome. Getting quoted from the page is. If the update went live and the engines still don’t pull you from it, that placement was on a page they don’t trust, and your next hour belongs to a different target.

Then run a community layer beside it. 48% of AI citations come from user-generated sources, and Reddit alone shows up in about one in five answers, almost always on category-level questions. Don’t spam threads. Find the post where someone asks what people are using for the problem you solve. Give a real answer. Name three tools including yours, say what each is good and bad at, link to something useful instead of your pricing page. That honesty is exactly the signal the model rewards, and it’s why the cynical version backfires.

This is the slowest of the four, so start it on day one and let it work while the on-site plays produce the early wins.

Run it like a sprint

Here’s what turns all of this from activity into a number you can defend in a forecast review.

Paid search has a target, a budget, attribution, and a weekly loop. AI search has earned the same treatment. Run it like a channel, or it keeps getting cut like a science project.

Stand up one dashboard with four numbers and review it every week:

Answer share. What percent of the answers to the questions that drive deals include you. This is the target. Pick it and commit.

Dual-signal rate. Not citations alone. AirOps found that brands earning both a mention and a citation in the same answer are 40% more likely to keep reappearing across runs, and that only 28% of answers contain a brand with both. Dual presence is rare and it sticks. It’s the difference between showing up once and owning the answer.

AI referral conversion. Tag the traffic from AI sources and watch how it converts against your other channels. I’m not going to hand you a benchmark here, because the honest truth is that almost nobody has clean AI-referral-to-pipeline numbers yet, and the figures floating around are mostly guesses. The point of this sprint is to make you one of the few teams that can actually answer it. The mechanism is in your favor and the intent arrives pre-qualified, but you prove the number on your own funnel. You don’t borrow mine. This will be a small subset of total AI referral but it’s a helpful directional proxy.

Pipeline influenced. The number leadership actually wants. This is the hop most teams skip and the one that keeps the channel funded. Connect the AI referral traffic to opportunities in your CRM the same way you do for paid. Same caveat as above. This won't be as complete as the attribution we were trained on in older digital channels.

Here’s the one I promised you at the top, in full.

Before the sprint, this client’s AI referral traffic was dumping into the “direct” bucket in GA4, credited to nothing and invisible to the pipeline conversation. Step one wasn’t a content play. It was tagging that traffic and piping it into HubSpot as its own source, the same way a paid campaign gets its own line.

Three weeks in: four opportunities carrying a first touch that traces back to an answer engine, roughly $180K in influenced pipeline, one in final-stage negotiation.

Now the honest caveat, because you’ll hit the same one. Two of those four had prior paid touches, so calling the answer engine “first touch” is a defensible read rather than a clean fact. And the tagging didn’t catch everything from day one. We lost the first several days of referral data before the source rules were right, which means the real number is probably a little higher and I can’t prove it. First-touch attribution on a channel this new is directionally right and precisely wrong, and anyone who tells you otherwise is selling something.

It’s still the number that changes the conversation. That VP of Marketing can sit in a forecast review and point at a deal that started when a buyer asked ChatGPT a question and the answer came back with them in it. Small next to paid. It exists and it has a name.

Budget the work the way you’d budget a paid test. Refresh cycles, page rebuilds, placements, all map to hours and dollars. Put a number on it and bring it to finance as a channel, not a hobby.

What the 30 days looks like

Week one. Build the retrieved-but-not-cited filter and start the refresh sweep, hardest-commercial queries first. Stand up the four-number dashboard the same week so you’re measuring from day one instead of reconstructing it later. Fire the off-site outreach now too, because it’s the one that needs six weeks to pay.

Week two. Rebuild your top five comparison pages and your pricing page for extraction. Run the “ask ChatGPT what we cost” test before and after so you can prove the swing.

Weeks three and four. The refreshed pages re-enter rotation. The rebuilt comparison and pricing pages start getting cited on the queries closest to a sale, and that’s where you watch the referral land and trace it into pipeline. Your Monday cohort report shows the slope.

By the time the board deck gets built, you’re not explaining why the channel is behind. You’re pointing at the climb, and at the first deals that came in through an answer.

You don’t need a new content engine. Get cited for the pages you already own, instrument it like the channel it has become, and let the number prove itself.

That’s the sprint. It’s the same one I run for the companies that pay me to run it. Take it.

And last…I’m going to go through this whole thing live with AirOps on August 5th at 2PM ET. Come join the party.

“Run a narrative synthesis on jasper.ai.”

That points a research tool I built and tells it to read what the market is saying, across Reddit, G2, Capterra, review sites, and forums, then turn the pile into a scored teardown: where the company wins, where it loses, what buyers say in their own words, and what to do about it. I spent the last few months building the tool. The new part, and the reason for this piece, is that I can now run the whole thing from inside Claude, on one line, without opening it and it’s…sick.

Forty-six seconds later I had the read on Jasper. The part that stopped me was not really a complaint or a compliment but a recommendation the tool made on its own: retire the line “Jasper generates content fast.”

Speed is what every AI writing tool sells. It is also the exact ground where free tools have caught up, so leading with speed walks the buyer straight into the comparison that hurts most. The tool did not tell me Jasper is fast. It told me Jasper should stop saying so, and it put the buyer quotes underneath to prove it.

Here is the rest of what came back, from 8 public sources and 10 verbatim buyer quotes, scored against eight competitors:

• Jasper has the strongest community in its category. Its community score, 88, is the highest in the set. The people who like it are loud and loyal.

• Price perception is the weakest thing about it. At 60, it is the lowest score on any dimension and near the bottom of the whole set. The reason is one sentence, repeated across reviews: similar features are free elsewhere.

• The defensible ground is “professional-grade,” not “fast.” The reviewers who rate it highly describe a tool built for real marketing work, not a casual writing aid. That framing is what separates Jasper from the free tier. Speed does not.

I wrote none of that. The tool gathered the sources, scored them, and pulled the quotes, and Claude ran it for me on one line of instruction.

Three months ago the thing that produced that read was a slide in a deck. Two months ago it was a private web page I scrolled through. Last week it became something my assistant runs mid-thought. This piece is how that happened, what Lovable shipped on July 15 to turn it into a weekend build, and why the shift underneath it matters most if your buyers are starting to ask an assistant instead of searching for themselves.

If you read the $300k brand audit piece, this is the sequel. If you did not, you do not need to. That one covered building the research tool. This one covers what changes when you make it callable from the assistant you already work in.

What changed

For most of tech history the thing you shipped was a screen. A person opened it and did the work by hand, because a person was the only thing that could. That constraint is lifting. An assistant can do the same work through the same app, without the screen, once you expose the app’s actions as tools it can call.

My own work shows the arc in three steps.

The deck came first. Static, stale the day it shipped, opened twice and filed. Everyone reading this has built a hundred of them.

The URL was the upgrade I wrote about last time. A private research app built in a weekend, refreshable on demand, with the evidence on the screen for anyone who wanted to scroll it. Far better than the deck. It still waited in a tab for someone to visit.

The tool is the step I want to walk you through. The research app and its five screens still exist for the times someone wants to sit with the analysis. Underneath, that same app is exposed as a set of actions Claude can run. I stopped going to the research. It comes to wherever I am already working.

What Lovable shipped, and how good it is

Lovable spent 2026 wiring MCP through the whole product, in both directions. The coverage tends to blur it into a single announcement. There are three separate pieces, and you build better if you keep them straight.

First, Lovable as a tool your assistant drives. The Lovable MCP server arrived in Research Preview in May and went live as a native Claude integration in June. Connect it once in Claude’s connector settings and Claude can spin up projects, edit code, run SQL, and deploy from the chat you are already in. This is the piece that got written up everywhere. It is good but it is not the piece that changed my work.

Second, outside tools available inside a Lovable build. Since late 2025 the Lovable agent has been able to pull from Notion, Linear, Jira, and custom MCP servers while it builds. Handy, and beside the point today.

Third, the one that matters. On July 15 Lovable shipped Agent integrations. Your published Lovable app now runs inside Claude and ChatGPT. Rather than send someone to your app, you let them use it from the assistant they already have open. They pull information, complete tasks, and take action through the app, without opening it.

The mechanism is an MCP server, and the reason it counts as a launch rather than a config flag is how much Lovable handles. You switch it on for a public project. Lovable reads the app and proposes the set of actions the assistant can call. You edit that list and decide who gets access: anyone, signed-in users only, or paying users only. Lovable hosts the server, runs a security check when you publish, and keeps the server in step with the app. Publish an update and the tools move with it. Sign-in is required by default, so the server stays private unless you open it on purpose.

Look at the early-access examples in Lovable’s own post and the audience comes into focus. Operational apps, mostly. A creator platform lets people handle bids from inside ChatGPT. Sales teams stand up ROI cases, quote generation, and pipeline queries. The one that got my attention: marketers running drafts through their team’s go-to-market brain in Lovable to check how copy lands with buyer personas and holds to brand. That is the category I work in every day, sitting in Lovable’s launch announcement as a lead example.

I should be straight about the timeline, because it doubles as the advice. I built my version by hand before this shipped, standing up a Supabase edge function that speaks MCP on the same backend the app already ran on. It worked and it was worth doing early. Then Agent integrations landed and turned the job into a switch with a scope editor and hosting included. What I hand-built is now a toggle. Use the toggle.

The design decision that matters is that the server tracks the published app. The thing I kept in sync by hand, Lovable keeps in sync for you. App and tools read from one source, so an update to one is an update to both. That is the line between a demo and something I run every day, and it is the reason the manual version I built is now the version I would tell you not to build.

Real limits are at the end (because I do not trust a pitch that skips them)

What I built

The $300k brand audit piece ended on a private research app. A dark, editorial URL, five screens, and an engine underneath that pulled public buyer conversation and clustered it into a narrative. I built one for Clay as the worked example.

That became NarrativeOS, the same engine, hardened. It reads public buyer conversation across Reddit, G2, Capterra, TrustRadius, Hacker News, and comparison content, then produces a briefing, a competitive read, a customer-voice layer, and a strategic layer for any company domain I point it at.

Then I exposed the engine as an MCP server.

The setup is deliberately plain. React on the front, Supabase on the back, the default Lovable stack. The synthesis already ran inside Supabase edge functions, since that is where the calls to the scraping and synthesis models lived and where the keys stay off the client. I added one more edge function that speaks MCP and exposes the engine’s operations as typed tools, with OAuth in front so only I can run my own analyses. The function sits at a URL ending in /functions/v1/mcp. I added it to Claude as a custom connector, signed in once, and the tools showed up in the composer.

The server is one more function on the same backend, reading the same tables. Run a synthesis in the app and Claude can read it. Start one from Claude and the app shows it. One source of truth, reached two ways: a screen for sitting with the analysis, a set of tools for putting it to work.

Everything I just walked through, I did by hand, and as of July 15 you do not have to. Agent integrations does the same job as a native feature. You switch it on for the published project, Lovable proposes the tool scope, you set the actions and the audience, and Lovable hosts and syncs it. Building it the manual way taught me which tools were worth exposing, which is the one thing the toggle cannot do for you. If you are starting now, take the native path and spend the weekend on the engine.

The tools, in plain terms:

• start_narrative_run takes a company domain and runs a full synthesis.

• get_latest_narrative_briefing returns the headline findings from the most recent run.

• get_narrative_run pulls any section of a run: briefing, competitive, customer voice, strategic.

• list_narrative_sources returns the grounding quotes with attribution and links.

• list_narrative_runs lists everything I have run.

• A set of delivery tools that run the sponsor content pipeline, covered below.

Each one used to mean opening the app, clicking into a screen, and reading. Now each one is a sentence.

But why not just ask Claude?

Claude can already search the web and summarize it. So why wrap a research app around it and call that through MCP, instead of typing “what does the market think of Jasper” into the same chat?

Because those are two different things that start in the same box.

Ask Claude cold and you get a brilliant generalist improvising. It runs a quick search, reads a few pages, and writes a tidy summary. Ask again tomorrow and you get a different summary, in a different shape, from different sources, with no score you can compare and no record the first answer existed. Fine for a throwaway question. It breaks the moment the work has to be consistent, sourced, comparable, or repeatable.

NarrativeOS is the process I built once so it never has to improvise again. Under “run a synthesis on jasper.ai” sits a fixed pipeline: parallel scrapes across Reddit, G2, Capterra, TrustRadius, Hacker News, and comparison content, a synthesis step that has to return the same schema every time, competitive scores on the same five dimensions, the AEO question layer with rising percentages, and every quote carried with its source and link. The model underneath is the same one you would be typing into. The process around it is the whole point.

Three things it gives me that a cold chat does not.

It is the same every time. Jasper’s run and Apollo’s run are scored on the same axes, so putting them next to each other means something. Two off-the-cuff Claude answers cannot be compared. Two NarrativeOS runs can.

It remembers. Every run is stored with a timestamp. I re-run a competitor weekly and watch a sentiment cell move across a month, which is the displacement play from the last piece. A cold chat has no memory of last week. It has a fresh guess.

Other people can call it. Once the engine is an MCP with tiered access, a client runs NarrativeOS from their own Claude or ChatGPT and gets my method instead of their own improvisation. “Just ask Claude” gives one person one answer. The tool is a capability I can hand to a room, or sell.

The honest version: if it is a one-off and you do not care about structure or receipts, ask Claude directly and move on. The tool earns its place when the job repeats, has to hold up under challenge, or has to be usable by someone who is not you. That is most real research, which is why I built the process instead of retyping the prompt.

The use cases I run every day

This is the daily use rather than the plumbing, with the real runs behind each case.

1. A full teardown from one line

I am mid-conversation with Claude about a competitor, a prospect, or a category, and I want the market’s read rather than my memory of it. I do not change tabs. I ask for a synthesis on the domain and it runs. I use this in advisory work all of the time and it never fails to impress.

The Jasper run up top is real, done live while I wrote this. One instruction, start_narrative_run on jasper.ai, and forty-six seconds later Claude had the whole read: a briefing, a competitive score across eight rivals, a customer-voice layer, and the strategic layer that produced the “retire the speed message” call. Runs on webflow.com and apollo.io in past weeks came back with completely different shapes, which is the tell that the engine is reading the actual market for each subject and not pattern-matching a template.

2. The evidence, the moment someone pushes back

A finding is worth exactly as much as the quote under it. Research falls apart when someone asks “says who” and you do not have the receipt in hand. This closes that gap.

When a finding matters, I ask Claude for the sources. It runs list_narrative_sources and returns the verbatim quotes with attribution and links. On the Jasper run, the price problem is not a guess. It sits on a G2 review saying the cost feels high when similar features are free elsewhere. And the reason to retire the speed pitch sits right next to a review that praises Jasper for producing general “filler” text, which is the exact framing that drags a premium tool down into the free-tier fight.

Headline claim to the exact sentence a buyer wrote, in one follow-up, without leaving the chat. That is what lets the analysis survive a room full of skeptics.

3. The AEO question map, which is the part I care about most

This is the layer that makes the tool mine rather than a generic sentiment reader. The synthesis goes past how buyers feel to the questions they are asking about a company, the gap behind each answer, and whether the question is climbing. That is the raw material for answer engine optimization: owning the answer an assistant gives when someone asks about you.

Jasper surfaced four:

• “is jasper ai worth the price compared to free tools,” up 52 percent, with buyers stating outright that similar features exist for free and Jasper offering no clear public answer in concrete time-saved terms.

• “how good is jasper ai for long-form blog content,” up 38 percent, where positive reviews praise drafting speed and negative reviews flag shallow output, and no benchmark exists to settle it.

• “does jasper ai understand context for complex marketing copy,” up 29 percent, with multiple reviews flagging context failures and no authoritative answer on where it holds up.

• “can jasper ai maintain brand voice across a whole team,” up 21 percent, praised in passing but with no case study proving it at team scale.

It is a content roadmap and a positioning brief in the same breath. The top one is the whole game. It is the fastest-climbing question about the company, it goes to the exact objection that caps the price score, and there is nothing published to win it.

Here is where I stop reporting what the tool found and tell you what I would do with it, because that gap is the reason I am still in the chair and the engine is not.

The tool is right that the speed message has to go. It is half right on the fix. Professional-grade is not a position, it is a claim every tool in that set can make, and the scores say so: Writer, Anyword, and Copy.ai all sit within a few points of Jasper on workflow power. Leaning on “built for real marketing work” wins nothing an assistant will repeat back.

The move the data is pointing at is the one it cannot make for you. The fastest-climbing question about Jasper is whether it is worth paying for over the free option, and nobody has published the answer. So publish it. Not a landing page, a benchmark. One honest, reproducible comparison of Jasper against the free tools on the work a marketing team cares about most, with the time and the output sitting side by side. When buyers ask an assistant instead of reading ten reviews, the company that ships the comparison owns the citation the assistant reads back, and whoever writes it first sets the terms of every worth-it answer for the next year. Jasper has the community and the workflow scores to win that fight. It is losing it by leaving the question on the table.

That read took me two minutes on top of the forty-six seconds the tool spent handing me the question. That ratio is the point. The engine finds the unanswered question at the speed of a chat. The call on what to do with it is mine, and it stays mine. The tool did not replace the analyst. It deleted the week of grunt work between the question and the answer.

4. Run it without touching the app, then chain it

Once the engine is a tool, it works alongside everything else Claude can do. I do not just get the analysis. I get it inside the task I am already in.

A real sequence from last week: run a synthesis on a prospect, pull the top three findings, then in the same thread ask Claude to draft the opening of a positioning memo from the strategic layer and lay the competitive scores into a table for a deck. The research never sits in a separate tool I copy out of. It lands inside the document I am building. Synthesis, writing, and formatting happen in one place, and the tool call is one step in a longer move.

5. Run the delivery pipeline from the same place

NarrativeOS is more than the research engine. It also runs the content and delivery pipeline behind the sponsor and advisory work at StackedGTM, on the same backend, so I exposed those actions as tools too. I can ask Claude to list deliverables across the boards, move a status, or leave a timestamped note, without opening the dashboard. The system that runs the business turns into something I talk to instead of a stack of tabs.

Why this matters most for AEO

Every builder gets something out of this launch. Every app you ship now sits one switch away from being a set of actions any assistant can run, which used to be a quarter of engineering you greenlit only when it was strategic and now takes a weekend. When the price of a capability falls that far, it stops being a bet and becomes the default. That is real, and it is the version of this story most people will write.

The version that matters to anyone in my world is narrower and sharper.

Your buyer used to visit sites, read reviews, and compare before they talked to you. A growing share of them now ask an assistant and take the answer it gives. That single change does two things at once, and the Jasper run shows both.

First, the company that owns the answer to the rising question wins the buyer before the buyer reaches a single site. Jasper’s most-asked question is climbing 52 percent, and Jasper has nothing published to win it. So the assistant answers on Jasper’s behalf, using whatever the open web says, which right now is that the same features are free somewhere else. The question was going to get answered either way. The only choice a company gets is whether it wrote the answer or let the reviews write it.

Second, the same shift that pulls your buyer into the assistant is what lets your competitive intelligence live there too. The tool that tells you which question is rising now runs in the same place your buyer is asking it. You find the gap and you can move on it in one session, in one thread, without changing tools. Owning the answer and owning the engine that finds the unanswered question now happen in one place, in one thread.

That is the reason I built the tool into the assistant instead of leaving it as a site I visit. Not for the novelty of a callable app. Because the buyer moved into the assistant, and the work that wins the buyer had to move in with them.

The honest limits

I do not trust a build write-up without a limits section, so here is mine.

The feature is five days old. Agent integrations shipped July 15. It handles the sign-in flow and runs a security check on publish, which is what you want, and it is new enough that the setup will shift under you. Work from the current docs, not a write-up from another month, and expect the screens to move while the ideas hold.

Exposure is the thing to get right, because you are handing an app to an assistant and letting it act. Agent integrations gives you the controls for it: sign-in is on by default, the server stays private until you open it, and every publish runs a security check. Use those controls with intent. Decide who can call each tool, keep write actions locked down, and open only what you mean to. Everything I expose is scoped to my own account on purpose.

The synthesis is directional, not a statistically valid sample. Public data is skewed. Unhappy and loud users post more than quiet, satisfied ones. The tool shows the shape of the truth, and you say so when you present it. Running it from Claude does not fill the gaps in the underlying data. It gets you to a directional read faster, which is the whole value and the whole limit in one.

An assistant that can run your tools can run them wrong. The flip side of letting it choose when to call is that it will sometimes choose badly. Build so a wrong call is cheap and reversible. Read tools are safe to hand out. Write tools need caps, cooldowns, and confirmation, which is why my run tool has a daily cap and my delivery tools are scoped tight. Assume a bad call will happen, because in time it will.

Research sits upstream of the customer conversation, not in place of it. The tool shows the patterns. Talking to buyers tells you which patterns mean something. Do not let a one-line path to a synthesized read pull you out of real conversations.

Build one, seriously.

If you built a research app from the last piece, you are most of the way there. The engine already exists. Switch on Agent integrations for the published project, let Lovable propose the scope, set the actions and the audience, and connect it in Claude. The app you already have becomes a set of tools you run from the assistant you already use, with no edge function to write. That is the reason to do this now instead of the way I did.

If you have not built one yet, the order is: build the app, get the engine right, then switch on agent integrations. Do not lead with the MCP. Lead with something worth running. A tool is only as good as the thing underneath it, and a callable version of a weak engine is still weak. Sharpen the engine first. Then hand it to your assistant.

I ran a live teardown of Jasper while writing this, from one line, in under a minute, and it handed me a positioning call I would not have made on my own, with the buyer quotes to back it. Once the deliverable stops being a place you visit and becomes something your assistant runs, the buyer and the work end up in the same room, which is exactly where you want them.

Build the tool. Then tell me what your engine turned up that you did not already know. I read every reply.

Which is funny, given what I did not put in that piece.

When I first heard the title going around LI, I rolled my eyes a bit. My read was that somebody in Clay’s orbit had taken GTM engineering, swapped a word, and shipped a category. I have watched vendors invent job titles to sell software my entire career, and I assumed I knew exactly what this was.

I was half right (which is the most expensive way to be right). I did not wave the role off because I did not understand it. I waved it off because I thought it was my resume with a new label on it.

I came up in marketing operations, which meant I spent my early career inside the machine rather than in front of it. Lead routing, scoring models, the whole path from a form fill to a closed-won deal, instrumented well enough that you could see where it leaked and prove it to someone who did not want to believe you. That was always systems work. The people who were good at it were good because they could hold the entire path in their head at once and tell you which part of it was lying to you.

Then I spent the next fifteen years bolting business judgment onto that. Global digital marketing at FIS across a dozen markets. Scaling Affirm through IPO. Building growth at Webflow. Every one of those jobs was the same exercise at a bigger number: understand the system, find the part that actually moves money, go change it, defend the result to a room that has its own theory.

That combination is my whole career advantage and I have never been shy about it. Not the automation, not the strategy, the fact that I have both when most people pick one. I could read the system and I could read the P&L, so I knew which lever was worth pulling, and I could usually go pull it myself instead of filing a ticket and waiting a quarter.

Which is exactly why the new title did not land for me. Systems thinking plus business judgment plus a willingness to go build it yourself is not a new job. It is the job I have been doing since I was in my twenties.

Then the aha, and it was not that I had the role wrong. I had the scope wrong. It is now the role I am most bullish on in all of marketing.

The title is not inventing anything. It is putting a name on what a stellar marketer already is right now, and what the rest of the function is about to have to become. That combination used to be an edge, the thing a handful of us had because we came up inside the machine instead of in front of it. It is not an edge anymore. It is the baseline for anyone who wants to be excellent at this in three years.

The skill did not change. The ceiling did.

Everything I used to need a team and two quarters for, I can now do alone in an afternoon. I have shipped a product this year with auth, payments, quota enforcement at the database layer and its own MCP server, solo. I built a client’s audit tool in a week that would have been a scoped engagement with a kickoff call. The site, the analytics, the email infrastructure, the launch assets. None of that required me to become an engineer. It required the thing I already had, pointed at tools that finally stopped making me ask permission.

So the constraint moved. It used to be capacity: I knew what to build and had to negotiate for the people to build it. Now capacity is close to free and the only scarce input left is knowing what deserves to exist. I can build almost anything I can specify inside a week, and I am wrong about what to build far more often than I am wrong about how to build it. No tool is coming to fix that.

That is what the role actually is, and why it is not GTM engineering with a new coat of paint. The best marketers I know right now are not specialists anymore. They are a small org compressed into one person, running four things at once: the creativity to come up with something worth making, the judgment to know which of those ideas earns the week, the systems thinking to see how the pieces move each other and where the whole thing leaks, and enough tooling fluency to go make it exist by Friday without asking anyone. That used to be four people, a roadmap and a standing meeting. Now it is one seat, and it is the most leveraged seat on a marketing team.

I think that is where the whole function is going. I also think the people who get there first will be the ones who have already spent years feeling the system underneath the tools, because when the tools change again, and they will, the systems thinking is the part that transfers.

None of this is theoretical either. I was talking with Harvey this week and the role came up on its own, unprompted, because they have one open right now. That is the tell. When a company like that decides it needs this seat before there is a playbook for filling it, the demand is already ahead of the operating knowledge.

The search data says the same thing, louder.

“What is a marketing engineer” is a breakout query on Google Trends in the United States over the past month.

Breakout is a defined term in Trends, not a figure of speech. It means the search grew more than 5,000% against the prior period, the label Google uses when a term is new enough that there was almost no earlier volume to measure against. Translated: a wave of people found out this role exists in the last thirty days and went looking for what it means. “Marketing engineer” on its own is still climbing.

People are hiring for this faster than anyone has worked out how to hire for it. I am writing this to close some of that gap.

Which brings me back to the point I opened with. Most of this category still lives in scattered LinkedIn posts and a few Slack threads, and the operating knowledge stays thin because almost nobody funds the unglamorous parts. Profound is the exception, and the job board and the university are the newest pieces rather than the whole of it. They are the analytics and action layer big teams (Ramp, US Bank, Rippling) use to see how they show up inside AI answers and then go change it. Read that against the definition I opened the last piece with. The marketing engineer closes the loop alone: find the problem, build the fix, ship it, measure whether it worked. Measurement plus the means to act on it is that loop with tooling under it, which is a more useful thing to hand someone than a dashboard they can only read.

None of that infrastructure can make the hire for you.

Scoping the role is easy. Writing the req is easy. The hard part is forty-five minutes across a table from someone with a clean resume, a slick portfolio, and a browser tab open to every AI tool you have heard of, where you have to work out whether they can do the job or whether they are good at describing it.

No role in marketing is harder to read right now, and nobody is to blame for it. The work is new and it will not sit still, so the signals every hiring manager spent a career learning stop predicting whether someone can do the job. A standard loop is not built to catch the difference. It rewards the wrong candidate without anyone meaning to.

This piece is how to build a loop that shows you the work instead. At the bottom you get all of it as a kit you can lift: the screener script, the work-sample brief word for word, the full question bank, the scorecard, and the calibration rules for the debrief. Paste it into Greenhouse and run it Monday.

TLDR of the first piece

If you missed it, the argument in six lines.

• The role is builder and artist in one seat. They close the loop alone: find the problem, build it, ship it, measure it, kill it or scale it. What used to be a cross-functional project is now one person’s week.

• The bar is creative judgment, not technical skill. The tools are easy and getting easier. Knowing what to build is the job.

• They report to the CMO or VP of Marketing. Not ops, not a chief of staff, not a dotted line into engineering. Team of one for the first six months.

• They own what they build and influence everything else. Give them a channel to own and you have built a demand gen manager with extra steps.

• Pay senior IC, not marketing band. $180K to $220K base plus senior IC equity. You are competing with engineers and PMs, not with marketers.

• Two failure modes to design against. They ship nothing under ambiguity, or they ship constantly and nobody uses any of it. The second is a taste failure and it hides the longest.

That first piece was about scoping the role and writing the req. This one is everything between the req and the offer.

One resource before we start. If you are still sourcing, Profound runs the only curated board for this role. Worth a look for two reasons. The live roles (Figma, Stripe, Databricks, Plaid, Legora) tell you who is hiring, which is useful competitive intel when you are trying to close someone. And the posted comp bands run roughly $100K to $296K right now, which is a free market read before you write your own offer.

Why this role is so hard to read in an interview

I have made hundreds of hires. Enough that I can usually read a candidate inside ten minutes, and enough to know which signals I am reading while I do it. Paid marketers, content leads, demand gen managers, ops people, PMMs. For every one of those I walked in knowing what competence looked like from across a table, because I had seen it and its opposite often enough to tell them apart fast.

Most marketing work is legible. I could read it quickly for so long because it holds still. Look at a paid marketer’s accounts, spend, and ROAS and you know inside an hour whether they ran serious programs. A content lead hands you a portfolio and you read it. A demand gen manager walks you through a pipeline model and you can tell if the math holds. The signal sits still long enough to read, and after enough reps you read it without thinking.

The marketing engineer’s work does not sit still. The tools are new. The field has a history you can measure in months, so the vocabulary is young enough that fluency and competence sound identical in conversation. A working demo and a production system look the same on a screen.

So every signal I spent a career learning stops predicting the one thing I care about.

Knowing every tool used to be a strong signal. Cursor, n8n, Clay, v0. Now the tools are easy, so tool fluency and judgment sound the same across a table and only one of them ships work people use. A demo that runs beautifully in the room tells you the happy path works, which tells you nothing about a Tuesday in production, and Tuesday in production is where the job lives. Someone can describe an agent in language that sounds like architecture and fully believe they built one, when what they shipped was a single workflow with a model call in it. They are not lying to you. The distance between those two things is invisible in words. And everyone can talk fluently about creative judgment now, because it is what everyone says the role is about, which means saying it and having it have become impossible to separate in a conversation.

The field is not full of pretenders. The field is new, the best work is hard to see, and the interview methods all of us inherited were built for roles where competence announces itself. It does not announce itself here.

The fakers never bothered me as much as this did: the same blindness that lets a talker through will also lose you someone good.

I have been on the wrong side of this, in the other direction. Early in my career I hired for the tool, because the tool was the only thing I knew how to test for. The person could administer the platform faster than anyone else I interviewed. What they could not do was tell me what the platform should be doing, and I did not find out for two quarters, because somebody who cannot find the leverage still looks busy. This cost me months but also taught me a valuable lesson…I started hiring for marketing judgment first, even when tool knowledge fell slightly short.

So the fix is not a more suspicious interview. It is a more revealing one. Stop asking candidates to describe the work, which even the strong ones find hard, and give them a way to show it.

I have seen this movie before, in marketing ops

Marketing automation arrived, the martech stack went from a handful of tools to thousands, and every team suddenly needed someone who could run the machine. Companies did the obvious thing. They hired for the tool. Can you administer Marketo? Do you know Salesforce? Have you run Eloqua? It worked the way hiring for the tool always works. It filled the seat and missed the gap.

People who could click through the platform were everywhere. The ones worth hiring were rare, for reasons that had nothing to do with the platform. They understood the system. They had judgment about what to build and, more importantly, what to leave alone.

Then the tool knowledge commoditized. Within a few years everyone had it and it stopped being worth anything.

The judgment never commoditized, and neither did the systems thinking underneath it. Operators who had both got more valuable as the stack got more tangled, because someone still had to understand how the pieces moved each other before deciding what the machine should do. The understanding is the part that transfers. Somebody who can see how a system fits together can point that at a stack they have never touched, which is why the good ones stayed good through three generations of tooling while the tool experts got replaced with each one.

The marketing engineer is that person with better tools and a higher ceiling. Swap Marketo and Salesforce for models, agents, and pipelines and the shape is identical. What separates a great one from an expensive one has not moved in fifteen years. It is judgment about what to build, and a feel for the system they are building inside of.

One clarification, because the analogy invites a wrong read. The marketing engineer does not replace marketing ops. Marketing ops is still its own discipline, still holding weight, still keeping your lead flow, routing, attribution, and systems of record from falling over. This is not a succession where one role retires and hands over the chair. The same pattern is repeating one layer up, and on a mature team the two need each other.

Which is also why the mistake is identical. Hire the marketing engineer who dazzles you with tools and you have made the error teams made hiring ops in 2012. You will fill the seat and miss the gap.

The one rule: audition, do not interview

Every good filter in this loop comes back to one move. You put the candidate in a position where they have to build something, decide something, or kill something, in front of you, in real time, with a wrong answer available.

Description is where the strong and the not-yet-ready sound alike. The moment the work goes live, they stop sounding alike. The gap between someone who has shipped fifty tools and someone who has mostly read about shipping them is invisible in conversation and impossible to hide in a build.

So the loop is built around three things that are genuinely hard to fake:

• A live build under constraint. Not “tell me about something you built.” Build something now, or on a short take-home, and let me watch how you work.

• A judgment call with a wrong answer in the room. Build versus buy, with a real tradeoff, where the impressive-sounding answer is the wrong one.

• A walkthrough of something they already shipped. Have them demo a real thing they built and drill into the thinking behind it. What did they choose, why, where did they get stuck, how did they get unstuck. The story falls apart fast if they did not do the work, because it only holds up in specifics the builder would know.

Everything below is in service of those three. Skip them and you are back to hiring on vibes.

Profound ran a very interesting version of this. They held a marketing engineer hackathon and made the prize an interview. You build under a clock, in front of people who know the work, and if you win you get a shot at the seat you were auditioning for the whole time. That is the loop, compressed, and it runs before anyone has spent a dollar on a screen. The build already happened. Anything the candidate says in the room afterward is confirmation of something you watched, not a substitute for it. The part worth stealing has nothing to do with hackathons. A job post pulls people who are good at applying for jobs. A build competition pulls people who want to be judged on the build, and that is a smaller and far better pool, sorted for you before you read a single resume. You can get some of this without the event. Put the work sample first, ahead of the screen, and say so in the posting. One way or another, make people show you what they can build in a short window. The ones who opt out were going to cost you four rounds to find out about anyway.

The loop

Four stages that matter, plus the posting and the debrief that bracket them.

Stage 1. The screen. Thirty minutes, run by the VP or a strong IC. Not a resume walk. You are listening for a short list of tells, below, that decide whether to keep going. Most of the field ends here and it should.

Stage 2. The work sample. The audition, and the most important stage in the loop. A small scoped problem, either a 90-minute live build or a three-hour take-home with a hard cap, paid. What matters is not the artifact. It is how they work.

Stage 3. The judgment loop. Two 45-minute rounds. One on what to build and build versus buy. One on measurement and taste. This is where you find out whether the person who built well in Stage 2 knows what is worth building at all.

Stage 4. References. Two calls, with questions that are not the usual ones. Almost nobody does this well.

What I left out: no take-home that eats twenty hours, because that filters for the desperate rather than the good. No brain-teasers. No panel of six people asking overlapping behavioral questions. Every stage earns its place by testing something a resume cannot.

The work sample: how to run the audition

This is the most important forty-five minutes to three hours in the loop, so run it with intent.

Give them a real problem, small and scoped. Not a take-home platform build. A single workflow with a clear edge. Examples that work:

• Here is a folder of ten customer call transcripts. Build something that turns them into a usable content brief. You have three hours.

• Here is our category and the five prompts a buyer would actually type into an AI tool to research it. Show me how we show up in the answers today, find the gap that is costing us the most, and build something that starts to close it. Tell us what you would measure to know it worked.

• Here is a competitor’s pricing page and ours. Build an agent or tool that flags a meaningful difference and drafts a one-paragraph response narrative.

• Here is a messy CRM export. Build the enrichment and scoring pass you would actually run, and tell us what you would not trust in the output.

Watch the front of the work, not the end of it. The finished artifact matters least. What tells you everything:

• What they clarify before they start. A strong one asks two or three sharp scoping questions and then goes quiet and builds. A weak one either starts flailing immediately or asks for a spec. The instinct to define the problem themselves is the whole role. If they need you to define it, you found a marketing ops manager with newer tools.

• What they cut. In three hours, nobody ships the whole thing. The good ones cut deliberately and tell you what they cut and why. The weak ones try to build everything and finish nothing, or polish one corner and leave the core broken.

• How they narrate tradeoffs. “I used the cheaper model here because this step does not need the reasoning and it runs a hundred times a day” is the sound of someone who has paid a production bill. “I used the newest model because it is the best” is the sound of someone who has not.

• Whether it actually runs. Not “here is what it would do.” Runs. On real input. With a failure mode they can name.

Then have them break their own work. The last fifteen minutes: “Where does this fall over? What breaks it at 2am? What would you not trust?” Someone who has run real systems answers instantly and specifically, because they have watched their own break. Someone who has mostly built demos says it is pretty robust. That answer is the whole interview.

The questions that carry the weight

Group your judgment rounds around five things. For each question below: what it is really testing, what a strong answer sounds like, and the red flag.

Use these live. The follow-ups matter more than the openers, so keep pulling the thread.

On taste (what to build)

This is the load-bearing skill, so it gets the most airtime.

“Walk me through the last three things you built. For each one, tell me what you decided not to build to make room for it.”

Testing: whether they think in tradeoffs, and whether they kill things. Building is a sequence of choices about what not to build.

Strong answer: they name the thing they skipped, and the reason is about leverage, not time. “I did not build the dashboard because nobody was going to open it daily, and a Slack alert did the same job in a tenth of the effort.”

Red flag: they cannot name anything they skipped. Everything they touched, they built. That is not range, it is no filter.

“Show me something you built that nobody used. What did you learn?”

Testing: honesty and volume, together. Everyone who ships a lot has built dead tools. It is unavoidable.

Strong answer: an immediate, specific, slightly embarrassed story about a tool that looked useful and rotted. Bonus if the lesson changed how they scope now.

Red flag: they cannot think of one. That means either they have not shipped enough to have a graveyard, or they are not being honest about the one they have. Both are disqualifying, for different reasons.

On build versus buy (the judgment)

“We are about to renew [real tool] at [real number] a year. Would you build the replacement? Walk me through how you decide.”

Testing: whether they reflexively say “build it” to impress you, or actually reason about maintenance cost, reliability, and opportunity cost. This is the trap question. The impressive-sounding answer is the wrong one.

Strong answer: they do not answer the question. They ask about how load-bearing the tool is, who depends on it, what breaks if the internal version has a bad day, and what else they would not be building if they built this. Then they might say buy. A marketing engineer who is comfortable saying “keep paying for it” is more senior than one who wants to build everything.

Red flag: “Oh, I could build that in a weekend.” Maybe they could. The point is they did not ask a single question about what it would cost to own it for two years. Building is cheap now. Owning is not.

“Tell me about a time you decided to buy instead of build.”

Testing: whether they can kill their own instinct to build. A builder who always builds is a liability, not an asset.

Strong answer: a real story where buying was correct and they knew it, usually because the thing was not a differentiator and the maintenance was not worth the ego.

Red flag: they cannot find one, or the story is really a build story wearing a buy costume.

“What is something on your team that could be automated, that you would leave manual anyway?”

Testing: whether they price automation against what it costs to own. Building is cheap now, so the discipline is not in what they can automate, it is in what they choose not to. This is the same judgment as build versus buy, one level down, at the task instead of the tool.

Strong answer: they name a specific task and the reason is ownership cost, not difficulty. The volume is too low to earn the maintenance, the inputs change every quarter so it would break constantly, or a human catches something in the doing that the output would lose. If they do not have a ready example, a candidate who reasons one out in front of you is answering the question just as well. The thinking is the thing being tested.

Red flag: they cannot find one, in memory or in the moment. Everything is a candidate for automation and nothing costs anything to keep alive. That is the disposition that fills a team’s week with agents nobody maintains.

On killing work and reliability

“An agent you built has been running for four months. How do you know if it is still worth running?”

Testing: whether they think past the ship date. Most of the failure in this role is not bad builds, it is good builds nobody maintained.

Strong answer: they talk about monitoring, drift, unit cost creep, and a specific check they run to decide whether to keep it alive. They treat their own tools as things that can rot.

Red flag: the question surprises them. They ship and move on. In this role, that is how you accumulate a graveyard of half-broken agents nobody trusts.

“What is the most expensive mistake you have made shipping something with a model in production?”

Testing: whether they have actually run things in production, which is a completely different world from building demos.

Strong answer: a specific, slightly painful story. A cost blowout, a hallucinated output that reached a customer, a silent failure that ran for a week. The specificity is the signal.

Red flag: they have never had one. Then they have never run anything real. Everyone who has shipped models to production has a scar.

On measurement (they are marketers, not engineers)

“The last thing you built. What number did it move, and how did you know it was that thing and not something else?”

Testing: whether they think like a marketer measured on pipeline, or like an engineer measured on shipping.

Strong answer: a real metric, and an honest account of attribution. Bonus points if they volunteer the confounders instead of pretending the attribution was clean.

Red flag: the number is “the team felt faster” or “it saved time,” with no attempt to quantify. This role is measured like marketing. If they cannot connect a build to a number, they will build things that feel good and move nothing.

“How would you measure whether a research agent you built is actually good, beyond it running?”

Testing: eval thinking. Do they have a way to know their output is right, or do they ship and hope?

Strong answer: they describe a real eval. A gold set, a spot-check ritual, a human-in-the-loop sample, something. They know “it runs” and “it is good” are different claims.

Red flag: “it works” is the whole answer.

On taste, again (the artist half nobody tests)

Test this directly, with real material. Do not ask about it. Show them something and watch.

“Here is a landing page. What is wrong with it?” (Bring a real, mediocre one.)

Testing: whether they can read work and know when it is slop. This is the exact skill that separates a marketing engineer from a junior engineer with a prompt library.

Strong answer: they see it fast. The headline is doing nothing, the proof is generic, the CTA is buried. They react like someone with an editor’s eye.

Red flag: they critique the tech stack, or they cannot find anything wrong, or their fixes are cosmetic.

“Here is an AI-written outbound email. Would you send it? What would you change?”

Testing: the single most practical version of the taste test. Can they tell good output from bad output? Because their whole job is producing output at scale, and if their taste is off, the scale just multiplies the slop.

Strong answer: they can tell it is machine-written in one read, name exactly what gives it away, and rewrite the tell out of it.

Red flag: they would send it. If they cannot catch AI slop, every system they build will produce it by the thousand.

The tells you can catch in the first call

You do not need the full loop to spot most of the field. These show up in the first thirty minutes.

Red flags:

• They lead with tools, not outcomes. Every answer starts with the stack. You asked what they built and they told you what they built it with. Push past the tools and see if an outcome is there.

• They cannot name something they killed. No graveyard means no taste or no volume.

• They need you to define the problem before they will engage. Spec-waiter. This is the disposition that does not survive contact with the role.

• They bring up the newest model unprompted, twice. Hobbyist. The best ones are boring about the technology and precise about the output.

• You cannot tell what number anything moved. They measured themselves like an engineer.

Green flags:

• Boring about the tech, precise about the output. They do not care which model, they care whether it landed.

• They volunteer what did not work. Unprompted honesty about dead tools is the strongest single signal in the loop.

• They ask sharp questions about your business before pitching a single build. They are trying to find the leverage, not show you their toolbox.

• They name real numbers, with the caveats attached. Real operators hedge their own attribution, because they have been burned by clean-looking numbers before. Overconfident attribution usually means they have not had to defend it.

The one kind of credential worth a second look

I told you resumes do not tell you much…and I meant it. Most marketing certifications are noise, because most of them are a quiz you can pass by watching videos at double speed. They prove someone sat through a course. They do not prove someone can do anything.

There is one exception worth knowing, and it is worth knowing precisely because it is not a quiz. Profound University runs certifications built specifically for this role, and they are structured as auditions, not tests. The Agent Engineering track makes a candidate architect and build a production-grade, multi-node agent from a real workflow in their own job (knowledge bases, structured outputs, conditional logic, real data), then present it and defend it on camera. The Analytics track makes them run a full diagnostic loop on live data, find a real opportunity, and defend the diagnosis with a root cause and a specific recommendation. Every submission is graded by hand by Profound’s team, not by an autograder.

Read that back against everything above. It is the same bar as the work sample you were about to run anyway: build something real, make it run, defend where it breaks. Which means if a candidate shows up already holding the Agent Engineering diploma, someone already made them audition for the hardest part of this job, and they passed it in front of experts.

That is the rare credential that earns a second look at the top of the funnel. Treat it as a strong green flag, not a substitute. You still run your own work sample. But it is the one line on a resume for this role that actually predicts the thing you are hiring for. If you want to point candidates at it, or size up your own bench, it lives here.

Reference checks worth the call time

References are where most loops go to confirm what they already decided. The standard questions (”what are their strengths)”, “would you hire them again”) are useless, because everyone says yes. Ask these instead.

• “What did they ship that other people depended on to do their work?” If the reference cannot name one, the candidate was building for themselves.

• “What did they build that nobody used, and how did they handle it?” You are checking their graveyard story against someone who watched it happen.

• “Did people ask for more of what they did, or did you have to assign them work?” Pull versus push. A real marketing engineer creates demand for their own output. This is the single best reference question for this role.

• “If you were starting a new team tomorrow, would this person be one of your first five hires? Why or why not?” Much harder to answer politely than “would you hire them again,” which forces a real answer.

Listen for hesitation on the pull-versus-push question specifically. A reference who has to think about whether people asked for more is telling you the work was invisible, which is the failure mode from the hire piece arriving early, for free, before you spent a dollar.

The debrief: three questions, honestly

By the end of the loop you should be able to answer three things. If you cannot, the loop was too soft and you should run the work sample again rather than voting.

Did they build something that ran, on live input, in front of you? Not described. Ran. If the answer is no, you did not test the one thing that matters and the rest of your notes are about a conversation.

Can they tell you what not to build, and why? If every answer was about what they would build and none about what they would skip, you found a builder without a filter. Call that the expensive-intern failure mode. It takes two quarters to surface and costs you both of them.

Would a marketer on your team be faster because of them, and can you point to who and at what? If you cannot picture the specific person and the specific workflow that gets better, they are not going to make anyone faster. You found a smart person who builds things.

Three yeses, make the offer and defend the comp band the way the first piece told you to. Any no, and the loop already told you where the gap is. Do not talk yourself past it. A wrong hire here gets worse the longer you leave it, in the same direction and at the same speed a right one compounds.

Steal this: the whole interview kit

That was the reasoning. Here are the scripts. Copy them into a doc, drop them in your Greenhouse, run them Monday. Swap the bracketed parts for your business. No attribution needed.

If you only run one thing, run the work sample. It catches more of the field than the rest of the loop combined.

How to sequence it

Total candidate time: about six hours, three of them paid. Total loop time: under two weeks if you schedule it up front.

1. The screener (30 minutes)

Five questions. You are listening for the tells, not grading answers yet. Two weak answers here and you stop.

1. Walk me through the last thing you shipped that someone else depended on to do their job. What did they use it for?

2. What have you built that nobody ended up using?

3. Tell me about something you decided not to build, and why.

4. When was the last time you paid for a tool instead of building it yourself? What made buying the right call?

5. What has your name on it and is running in production right now?

Advance if: they name outcomes before tools, they have a graveyard story ready, and at least one answer includes a number with a caveat attached.

Stop if: two or more of the red flags from the tells section show up. Most commonly it is the stack-first answer and the empty graveyard, together.

2. The work-sample brief (send this word for word)

We do not do whiteboard exercises. We want to see how you work, so we give every candidate one small problem and three hours. This is paid, and there is a hard time cap, so please do not go over. We are not looking for a finished product. We are looking for how you scope, what you cut, and whether it runs on live input.

The problem: [pick one and attach the files]

Here are ten customer call transcripts. Build something that turns them into a usable content brief.

Here is our category and the five prompts a buyer would type into an AI tool to research it. Show us how we show up in the answers today, find the gap costing us the most, and build something that starts to close it. Tell us what you would measure to know it worked.

Here is our pricing page and a competitor’s. Build a tool or agent that flags a meaningful difference and drafts a one-paragraph response.

Here is a messy CRM export. Build the enrichment and scoring pass you would run, and tell us what you would not trust in the output.

When you present, be ready to tell us: what you clarified before you started, what you cut and why, what number this would move if it were live, and where it breaks. Especially where it breaks.

Bring whatever tools you want. We do not care what you use.

Presentation structure (45 minutes): 5 min setup, 15 min they demo it running, 10 min your questions, 15 min they break their own work.

What you are grading, in order of weight:

1. Did it run on live input? (Pass/fail. No partial credit.)

2. What scoping questions did they ask before starting?

3. What did they cut, and could they defend the cut?

4. Did they narrate tradeoffs unprompted while building?

5. Could they name where it breaks, specifically, in the last fifteen minutes?

3. Judgment round 1: what to build, build vs buy (45 minutes)

Pick five. The first three are not optional.

1. Walk me through the last three things you built. For each one, tell me what you decided not to build to make room for it.

2. We are about to renew [real tool] at [real number] a year. Would you build the replacement? Walk me through how you decide. (Do not accept a fast answer. Push twice. The trap is that “I could build that in a weekend” sounds impressive and is wrong.)

3. What is something on your team that could be automated, that you would leave manual anyway? (A hypothetical counts. You are grading reasoning about ownership cost, not recall.)

4. Show me something you built that nobody used. What did you learn?

5. Tell me about a time you decided to buy instead of build. What made buying right?

6. Here is a snapshot of my marketing team’s week [describe a real one]. Where is the highest-return thing to build, and why not the other four?

7. It is your first thirty days here. What do you build first, and what do you deliberately ignore?

8. A stakeholder asks you to build something you think is a waste of time. What do you do?

4. Judgment round 2: reliability, measurement, taste (45 minutes)

Pick five. Number five and six are not optional and you need real material for both.

1. An agent you built has been running for four months. How do you know if it is still worth running?

2. What is the most expensive mistake you have made shipping something with a model in production?

3. The last thing you built: what number did it move, and how did you know it was that and not something else?

4. How would you measure whether a research agent you built is any good, beyond the fact that it runs?

5. [Show a mediocre landing page.] What is wrong with this?

6. [Show an AI-written outbound email.] Would you send this? What would you change?

7. Show me the ugliest thing you ever shipped that worked. Why was ugly the right call?

5. Reference questions (two calls)

1. What did they ship that other people depended on to do their work?

2. What did they build that nobody used, and how did they handle it?

3. Did people ask for more of what they did, or did you have to assign them work? (The one that matters. Listen for hesitation, not the answer.)

4. If you were starting a new team tomorrow, would this person be one of your first five hires? Why or why not?

6. The scorecard (paste into Greenhouse)

Score 1 to 4 on each. A single 1 on Judgment, Build, or Taste is a no regardless of the rest.

Judgment, knows what to build and what to skip 1: builds whatever is asked · 2: prioritizes when told the goal · 3: finds the return themselves · 4: tells you what not to build and is right

Build, it runs 1: demos only, nothing survives live input · 2: ships, but fragile · 3: ships things that run in production · 4: ships things others build on top of

Taste, tells good output from slop 1: cannot spot slop · 2: spots it when pointed at it · 3: catches it fast, fixes the tell · 4: their default output is already clean

Ownership, thinks past the ship date 1: ships and forgets · 2: monitors when reminded · 3: watches cost, drift, and failure by habit · 4: kills their own dead tools before you notice

Marketer instinct, connects builds to numbers 1: measures in “felt faster” · 2: names a metric, weak attribution · 3: ties builds to numbers with honest caveats · 4: chooses what to build by the number it will move

Decision rule: three or more 4s, and no 1s on Judgment, Build, or Taste. Anything else, walk.

Calibration notes for the debrief. Two things go wrong in the room and both are predictable.

The first is that someone will argue a 2 on Build up to a 3 because the candidate was impressive to talk to. Build is the one score that is not a matter of opinion. It ran on live input or it did not, and if nobody can point to the moment it ran, it is a 2.

The second is that a candidate with 4s everywhere and a 1 on Taste will get an offer anyway, because four out of five feels like a strong loop. It should not. Taste is the score that predicts the failure mode you cannot see for two quarters, which is a person who ships constantly and whose output nobody wants. Walk on the 1. You will not regret it and you will never find out you were right, which is what makes it hard.

The line

Anyone can learn the tools this year. Which is exactly why the tools are the wrong thing to test for.

The candidate who impresses you by naming every platform is easy to be dazzled by and usually the wrong hire. The one worth hiring is quieter about the stack, faster to ask what your business needs, honest about the things they built that died, and unable to stop themselves from telling you what they would not build. You will not find that person with better questions about their resume. You will find them by making them work in front of you and watching what they cut.

Your first marketing engineer sets the bar for the next five. Get the vetting right and the bar is judgment. Get it wrong and the bar is whoever interviewed best, which in this role is the wrong person almost every time.

You have the req. Now you have the loop.

Go find a real one.

You will get close to a hundred different lists. Order shuffles, length changes, the name that led one answer goes missing from the next. It looks random but really mostly isn’t and the part that holds still is the entire game.

I have spent the last 2 years auditing AEO strategies for companies paying real money to watch a number on a dashboard, and I open every engagement the same way: I read the prompts the tool is tracking and hold them against the questions I am watching buyers actually ask. Nine times out of ten the tool is measuring a market that stopped existing months ago, and nobody in the building knows, because the report keeps coming back clean. This is not a vendor bug you can file a ticket on. It is what these systems are, by design.

Which raises the question almost every team skips on the way to buying a dashboard: if the answer is different every time you ask, what exactly is the number on your AI visibility report measuring?

It is measuring a sample. Most teams read it like a census, and the gap between those two things is where AEO budgets go to die. The one input that decides whether the sample is any good is your prompt index, the curated universe of questions your tools ask the engines on your behalf, organized by topic, persona, and intent. Get it right and your visibility data describes the market you actually compete in. Get it wrong and you will spend two years optimizing against a mirage with a clean decimal point stamped on it.

When I audit an index, the questions I test it against don’t come from a dashboard. I run agents across the places buyers actually talk, the Reddit threads, the G2 reviews, the community forums, and pull the raw language people use while they are still deciding. That is buyer intent captured upstream, before anyone opens a chatbot. It almost never lines up with what the tool is tracking. Sometimes the set was wrong on day one. More often it was fine at setup and then nobody touched it again, frozen in whatever the market looked like that week while the buyers moved on without it. Either way, the dashboard on top kept reporting confident numbers the whole time.

The index isn’t a setup step you knock out in week one and never open again. It’s the denominator under every metric you will ever report, and a CMO who signs off on an AEO strategy is signing off on a prompt index whether they know it or not.

Here is what took me too long to see, even while I was running a global growth org full time: a good prompt index is barely a measurement asset. It is the clearest read you will ever get on what your market is trying to buy. When my team built our AEO program, the prompts that mattered were never the ones with our name in them. They were the unbranded ones, the “what is the best headless CMS for a marketing team that can’t lean on engineers” questions, because that is the exact moment a buyer is deciding and hasn’t picked yet. Get that set of questions right and you stop merely measuring AI visibility. You start holding a live transcript of your buyers’ intent, in their own words, refreshed continuously. That transcript should be the input every other GTM decision keys off. Most teams have it sitting in a settings panel they opened once and never reopened.

TL;DR

• Every number on your AI visibility dashboard is a sample, not a census. A set of questions called your prompt index generates it, and that set is almost never audited after setup.

• AI answers are wildly inconsistent run to run, but a brand’s appearance rate across many runs is stable and measurable. The catch is that it is only as honest as the questions you chose to ask.

• The errors split in two. Depth, meaning how many times you run a prompt, is recoverable with more runs. Breadth, meaning which intents, personas, and engines you cover, is not. A gap in coverage is a blind spot no volume of data fixes, and your dashboard looks just as confident with the hole as without it.

• Wording barely matters. The models read intent, not phrasing. Coverage of real buyer intent is what moves the number.

• A good prompt index is not a reporting asset. It is the sharpest read you get on what your market wants to buy, which makes it a CMO decision and a GTM input, not an SEO setting. The revenue leaders who treat it that way are the ones who will win. This is my personal most important belief in this whole piece.

• A five-minute gut check, and how to build an index that survives scrutiny, are at the bottom.

The randomness is the easy part

Last winter Rand Fishkin ran the experiment everyone in this space should have run before spending a dollar. Working with Patrick O’Donnell at Gumshoe, he had 600 volunteers run twelve prompts across ChatGPT, Claude, and Google’s AI nearly three thousand times, and logged every brand list that came back. The result was brutal for anyone selling “rankings in AI.” Less than a one in a hundred chance of getting the same list of brands twice. Less than one in a thousand of getting the same order. These tools produce a fresh answer on every run, so any product promising you a stable rank position inside an AI answer is selling something that does not exist.

If that were the whole story, the category would be snake oil and you'd be right to bail (don’t get me wrong, there is an unfair amount of BS in this category). It isn't, and the reason is where most teams stop reading.

When Rand’s team stopped staring at individual answers and started counting how often each brand appeared across all the runs, the noise resolved into signal. Ask Google’s AI to recommend digital marketing agencies with e-commerce expertise ninety-five times, and one firm, Smartsites, shows up in eighty-five of the answers. An 89% appearance rate. The rank inside any single answer bounced around almost at random, but whether a brand appeared at all held remarkably steady. One answer is a slot-machine pull. The pattern across hundreds of them is the thing you can actually measure.

So a visibility score isn’t a reading off a meter, it’s a statistic. Ask a population of questions enough times, count how often you turn up, divide. That makes it a poll, and every leader already knows the first law of polling even if they have never run one. The result is only as good as the sample. You can poll a thousand people and land further from the truth than someone who polled a hundred, if the thousand were the wrong people. Precision tells you nothing about whether the number is true. Your prompt index is that sample, and almost nobody audits it.

The math that makes the number real

Almost no dashboard will tell you this, but the randomness is a solved problem statistically. You just have to respect the unit of measurement.

Run a single prompt once and you have close to nothing. Run it enough times and the share of answers you show up in becomes a number you can actually estimate, and at low run counts the error bars are embarrassingly wide. This is just binomial math, the same math behind every political poll. A brand tracked on a single prompt, sampled the handful of times most tools manage, carries a margin of error wide enough that a "40% visibility" reading could honestly be sitting in the twenties or the fifties, and the tidy number on the screen would never tell you. Roll dozens of prompts into a topic and track them across weeks, not days, and that error tightens to low single digits. Nothing changed except the unit you measured and how often you looked. One prompt is jitter you can't act on. Roll enough of them into a topic and you have a number that survives a board meeting.

This is exactly why the serious platforms organize the world as topics with prompts rolled up underneath, instead of a flat list of questions. A topic is a broad buyer need, something like “cloud security solutions.” A prompt is the specific question underneath it, something like “compare the top three cloud security tools for a small business.” The topic is what you can attach a confidence interval to. The prompts are the samples feeding it. Leave prompts floating in a flat list with no topic layer and you have nothing you can measure with any rigor, which is where most tools leave them.

The depth side has hard rules of thumb now, and they are cheap to apply. A 2026 arXiv analysis of source stability found you need at least seven or eight runs per prompt before per-brand detection rates settle, and that daily source churn runs around 65%, so a single day or even a single week is too short a window to separate signal from noise. To be clear…that is seven or eight runs accumulated over weeks, not a prompt you hammer twenty times every morning. That would be expensive, and it misses the point. Depth only has to clear the noise floor. Once it does, every extra dollar buys you more by widening the net than by deepening the same hole.

None of this is fringe anymore. In May 2026 AMEC, the standards body that governs how communications gets measured, put out its GEO Principles, and the fourth one draws the line I've been drawing on whiteboards for two years: what one tool returns to one prompt, at one moment, in one market, is directional at best. Real measurement means a documented set of prompts, run repeatedly, with the variation shown rather than buried. That last part is where the whole category fails. Almost no AEO dashboard shows you the variation. They hand you a percentage and let you assume it's a fact.

There is a tradeoff buried in that math worth internalizing. Ten prompts run thirty-nine times each and fifty prompts run eight times each land at roughly the same confidence, around five points of error, for about the same total number of queries. The work is similar. But spreading the budget across more distinct prompts buys you something repetition never can, an estimate of the whole topic instead of one phrasing. That is the rule to carry out of the math. Once each prompt clears the minimum runs to stabilize, more prompts beats more repeats every time. You clear depth once. Breadth is where the game is actually played.

Run your dashboard against those numbers and most setups fail at least two of them. That is not a small thing. It means a meaningful share of the week-over-week “wins” teams report are smaller than their own margin of error, which is the analytical equivalent of re-polling forty people every morning and calling the jitter a trend.

Why none of that math saves you

Everything above fixes depth, and none of it touches breadth. Breadth is where the errors you can’t take back live.

Go back to Rand. After the brand-list experiment he asked his volunteers to write their own prompts, in their own words, for one shared intent: the best headphones for a family member who travels. A hundred and forty-two people produced a hundred and forty-two wildly different prompts. When his team scored how similar those prompts were to each other, the number came back at 0.081 on a scale where 1.0 is identical. Fishkin likened the average pair to Kung Pao chicken and peanut butter, two foods with peanuts in them and otherwise nothing in common. People do not search the way they Googled. They do not compress intent into three keywords. They ramble. They pile on context nobody asked for, then get oddly specific about the one thing that actually matters to them.

And the brands still converged. Across nearly a thousand responses to those scattershot prompts, Bose, Sony, Sennheiser, and Apple each turned up in 55 to 77% of answers. The phrasing was all over the place. The intent underneath was identical, and the models read the intent under the words and returned a stable set of brands.

Conductor’s research team ran the harder version of this experiment and landed in the same place. They fired 14,000 API calls across ten industries, seven intent types, and four engines, fifty runs per prompt, and found AI recommendations are not random at all. They are predictably inconsistent, and the predictor is not the industry, it is the intent behind the question. Purchase prompts, where a buyer is ready to spend, were the least stable in the study. Out of every ten brands that surfaced across any two runs, only four showed up in both. Comparison prompts, where the brands are already named in the question, were the most stable by a wide margin, the same brand leading 91% of the time. Same market, same buyer, wildly different reliability depending only on the shape of the question. That is the breadth problem stated as a law. Over-index on the easy, stable intents and skip the volatile ones, and your dashboard looks calmest exactly where your exposure is highest.

That should change how you build an index. The specific words barely matter, because the engine responds to intent, not syntax. Obsessing over whether you phrased a prompt the way a “real buyer” would is wasted effort. What matters is whether your index spans the full range of intents your buyers carry. Miss a phrasing and you lose nothing. Miss an intent, a persona, a journey stage, or an engine, and you have a hole no volume of additional runs will ever fill, because the observations that would fill it were never collected. Depth you can always go back and buy. Breadth, once you have decided what to ask, is locked, and the dashboard looks exactly as confident with the hole as without it.

That single distinction reframes the whole job. Building a prompt index is a coverage problem, not a phrasing exercise, and coverage means deciding which people, which journey stages, and which engines you are willing to be measured on. Those are strategy calls, and they belong to whoever owns the strategy.

The three indexes that lie to you

Coverage fails in three common ways, and each one feels responsible while you are doing it.

The first is the borrowed panel. Many tools do not build your index from your business at all. They infer prompts from third-party data, often scraped from browser extensions, and sell the size of that pool as a strength. The problem is who is in the pool. ChatGPT alone fields something like 2.5 billion prompts a day, and a panel assembled from the narrow, self-selected slice of people who install tracking extensions is a fraction of a percent of that, and skewed on top of it, because the extension-installer is no stand-in for your CFO buyer or your clinical director or your procurement lead. You are sampling the people nearest the exit and calling it the electorate. There is a quieter risk too. Data sourced this way can vanish overnight when an app store rule or a privacy law shifts, pulling the floor out from under your measurement without your consent. Panels are not worthless. You just have to know when one is the foundation of your number rather than a supplement to it.

The second is the wish list, the all-human index where a smart team writes down the questions they believe customers ought to be asking. It feels rigorous because it took effort, and two biases undercut it anyway. The first is reach. No group of people can enumerate the real variety of how a market asks, so the long tail goes uncovered, and the long tail is often where the engines cite most generously. The second is vanity. Teams write the prompts they want to win, the ones that flatter the product, and quietly drop the ones where they come off badly. What comes back is an instrument calibrated to make you feel good.

The third is the generic model dump, where you ask a general chatbot to list prompts for a topic. Fast, free, and hollow, because the model does not know who you are. It does not know your content, your architecture, your personas, or where your demand sits, so it hands you the average of the internet, a description of a generic competitor in your category rather than of you. The president of Optimizely said it well when his team went looking for an AI search data partner: most visibility tools build their prompts in a vacuum, with no grounding in what people are actually searching for. Same disease as the borrowed panel, newer clothes.

All three share one root cause. The index has been cut off from the only two anchors that keep it honest, your own business and real demand. Reconnect those two and most of the failure modes sort themselves out.

What a good index looks like, concretely

You can actually inspect a good index, point at each part and say why it’s there. That is the whole line between infrastructure and a vibe.

It is built from your own business as the source of truth, not from a generic outside list. The most rigorous version I have seen starts from your actual website content and site architecture. This is the core of how Conductor approaches it. Your site is stored in a vector database, and the system reads your specific pages and the semantic relationships between them to generate prompts grounded in what you have real authority on, then validates those against real organic search demand drawn from more than a decade of search data, so the questions are not just on-topic but genuinely in demand, in the geographies you care about. Your content is where the relevance comes from. Real demand is what keeps the set representative instead of merely plausible. A brainstorm gives you neither. A chatbot hands you a confident average of everyone who isn’t your buyer.

It is generated, not hand-typed, because the scale demands it. The method that matters here is synthetic prompt generation, using AI to produce a large, realistic set of conversational queries that mirror how real people ask, built around intent and context rather than keyword volume. Done badly, that is just a chatbot listing a hundred questions, which drops you right back at the generic dump. Done well, it is grounded in your site, weighted by real demand, and segmented before a single prompt is written. That gap, between a list of plausible questions and an index grounded in your business, is the thing to push on when you evaluate any tool.

It covers personas and intent on purpose. A hands-on practitioner and a C-suite buyer ask different questions in different language about the identical topic, and a good index holds both rather than averaging them into a person who does not exist. Better still when personas are built into generation from the start and can be mapped to specific products, and when the index deliberately walks the buying journey instead of camping in the easy middle. The lifecycle is the frame I find most useful: education and “what is” questions, recommendation and “best of” discovery, head-to-head comparison, pricing and value, branded navigation, high-intent purchase, and post-purchase support. Each stage is a different visibility fight, frequently against a different set of competitors. Conductor lets you choose which stages to generate against, and which personas and engines to track, before you create a single prompt, then surfaces the result as a persona-and-intent grid where the empty cells are the uncaptured demand. The grid view that matters is the one that shows you the holes instead of hiding them

Filled cells are demand you are capturing. Empty cells are demand you are not. Most teams have never seen their own index laid out like this.

It spans engines, not just one. Independent analyses of large prompt sets have found that different engines cite startlingly different sources for the same questions, with overlap between some platforms down in the single digits. Being the answer in ChatGPT and being invisible in Perplexity or Google’s AI are separate outcomes, and a single-engine index will happily hide one behind the other. Track one engine and you are measuring a slice of your visibility and guessing the rest.

It is mostly unbranded. Roughly three quarters of prompts should carry no brand name. Branded prompts confirm you own your own name, which you should, and which teaches you almost nothing. Unbranded prompts tell you whether you are reaching people who have never heard of you, which is the entire reason AI search matters as a discovery channel. An index stuffed with branded queries produces high scores and confirms what you already knew.

And it is deep enough to clear the noise floor, on the depth rules above, so the movements you report are real and not the dice resetting.

Why this lands on the CMO’s desk

Everything I just walked through sounds like configuration work, somebody in a settings panel checking boxes after they bought the tool. The prompt index is where an organization encodes the definition of its own market, which makes it a leadership decision wearing a technical costume.

I couldn’t be more passionate about this belief.

Trace what the index silently sets. It fixes your competitive set, because the only rivals who can appear beside you are the ones your chosen questions summon, which means a sloppy index can have you benchmarking against companies you don’t compete with and feeling good about beating them. It sets the baseline for the sentiment and share-of-voice figures headed into board decks and budget fights. It decides which content gaps look urgent, since a gap can only surface if a prompt existed to reveal it. A topic you forgot to index is a market you are blind to, and blindness does not feel like blindness from the inside. It feels like a clean dashboard and a good night’s sleep.

This is why “the SEO person handles the prompts” is an expensive sentence. It is the equivalent of saying the analyst handles the definition of revenue. The mechanics can live with a specialist, gladly. The strategic question underneath, are we measuring the market we actually fight in, belongs to the CMO, and deserves the scrutiny you would give any change to how you count pipeline. It also imposes a discipline most teams skip. If your reported quarter-over-quarter movement is smaller than your margin of error, you have learned nothing, and calling that wobble a trend is how good teams talk themselves into bad bets. When a tool flags a competitor newly claiming a high-value topic, or sentiment sliding on a specific source, those are not tickets for the SEO queue. They are early signals about your position in a market, worth exactly as much as the index that produced them and not a cent more. Conductor frames its AI Search Performance report as a system of record for this reason. The dashboard was never the point. The point is a defensible, audience-aligned account of where you stand that leadership can trust, because the sample under it was built to hold weight.

The view you can actually take to a board: benchmarked share of the conversation, where the gaps are, and what the competitive exposure looks like.

The stakes here are not abstract. AI platforms drove over a billion referral visits in a single month last year, up around 357% year over year, and those visitors tend to arrive already pre-qualified by the model, which is why teams keep finding they convert at multiples of ordinary organic traffic. The channel is still small as a share of total traffic and growing faster than anything else on the web. The brands building an honest measurement foundation now are the ones who will be able to act on it while it still counts as an advantage rather than table stakes.

The index pays off well past marketing

Once you hold an index that truly maps how your market asks questions, you have something more valuable than a visibility tool. You have a structured, continuously refreshed picture of buyer intent, and that picture is useful to functions that have never heard the term AEO.

I saw this firsthand at Webflow. The work that earned us a 64% answer share in the CMS category we didn’t even own 2% market share in was not a clever schema trick. It started with the index telling us, plainly, which comparison and objection questions buyers were bringing to the models, and a lot of those were questions our product marketing had never framed an answer to. The index was doing demand research before it was doing measurement. That is the part most teams leave on the table.

Product can read the comparison and pricing prompts that keep recurring as a live feed of the objections and feature expectations in the market, in customers’ own words rather than filtered through a roadmap survey. Sales and enablement can mine the same prompts for the exact language buyers use, which beats most win-loss programs for discovery and objection-handling. Communications can watch sentiment move at the level of individual prompts and trace a shift back to its source fast enough to act while it still matters, turning reputation work from quarterly archaeology into something close to real time. Competitive intelligence falls out almost for free, since the index constantly shows which rivals own which ground in front of which audience.

None of that holds if the index is a borrowed panel or a wish list, because then it is a detailed map of the wrong country and everyone navigating by it is lost in the same confident way. Build it right and it stops being a marketing utility and becomes shared infrastructure, one honest definition of what your market is asking that every customer-facing team can build on.

The agentic turn is already here

This matters more every quarter, not less, and there is a reason. Search is going agentic. The same engines that answer questions are starting to take actions, and the platforms are racing to wire AI insight straight through to execution. When Optimizely built out its AEO platform this year, it chose Conductor to power the prompt and visibility layer underneath its autonomous agents, the ones that find content gaps, benchmark share of voice, and route work without a human in the loop. Strip away the announcement language and the logic is plain. An agent that acts on your behalf is only as good as its read of the market, and its read of the market is the prompt index. The foundation does not get less important when machines start acting on it. It gets more important, because now the errors propagate at machine speed.

A five-minute audit you can run this week

You do not need a project to find out whether your foundation is sound. Take your current AEO setup into your next review and put six questions to it. Each one has a failing answer, and the failing answers are common.

• How was our index built? If the answer is a third-party panel, a brainstorm from last spring, or a chatbot session, you do not have a measurement program. You have a number that moves.

• How many prompts per topic, and how many topics? Under fifteen prompts a topic and you are below the floor where the curve even starts to flatten. A flat list with no topic layer means you have no unit you can attach a confidence interval to.

• What share of prompts is unbranded? Below roughly three quarters and you are mostly measuring whether you own your own name.

• Which personas, intent stages, and engines are covered, and which are not? If nobody can name the gaps, the gaps are running your strategy. Ask to see the empty cells, not the full ones.

• How many times is each prompt run, and over what window? Fewer than seven runs, or a window shorter than a couple of weeks, and your numbers are inside the noise.

• Does our reported movement exceed our margin of error? If you can’t answer this, you can’t tell a win from a wobble, and you are probably reporting both as wins.

Fail two or more, and most teams do, and the fix is not a better dashboard. It is a better sample.

Where to start

I’ve built this index the hard way, and I’ve reviewed enough of other people’s to know the pattern never changes. The dashboard looks fine. The foundation underneath it is a guess.

So rebuild it from the two anchors that carry real weight: your own content and authority, and real search demand. Cover the personas, journey stages, and engines you actually compete in on purpose, not by luck. It’s real work, and there’s no shortcut around it. The tools that do this well, Conductor among them, use your site as the source of truth and ground thousands of prompts in real demand instead of a generic list that flatters your product. Whatever you run, run it with your guard up and hold it to the six questions above.

Get the index right and everything on top of it gets honest. Get it wrong and you keep walking into rooms with confident slides about a market that was never there. I’ve been in those rooms. I’d rather you weren’t.

I have sat in that exact seat (and have PTSD typing this). So take this as one operator handing another the play I reach for first when search is the lever with the most room to move. It does not look like the others, and it has potential to pay off on a timeline nobody expects.

This is the sequence I run when a company brings me in below plan, ordered by which plays are most likely to land early wins. Same order, same filters, same plays I get paid to execute. The order is mine, learned in the field. The proof under it is AirOps’ research on how these engines actually cite, one of the best data sets I’ve seen to back this question.

Worth calling out…AEO is not predictable like paid search. The engines rewrite answers constantly, and no one can promise you a specific citation on a specific query in thirty days. I did not pick these four plays because they always work. I picked them because they are the highest-probability moves for a short window, run on pages already in the pool. Some hit in week two, some take the full month, a few will not move and you kill those. That is why you instrument it from day one and let the data pick the winners. The only difference between reading this and hiring my team is that we show up Monday and run it. The plays are the plays. Here they are.

First, why thirty days is even possible

If this were an SEO gap, I would tell you to lower your expectations and come back next year.

SEO does not really run on a 30-day clock. Teams wait three to six months to see anything, and closer to a year for a number they can bring to a forecast review. The reason is structural. The pages sitting in the top ten are old. Ahrefs found more than seven in ten of them are over three years old, and the average page in the number one spot is closer to five. That is not an optimization problem. You are stuck behind pages that have had a five-year head start.

AEO does not work like that and that’s why a gap like this is really just opportunity.

Citations don’t require rankings. In AirOps’ 2026 State of AI Search, Kevin Indig’s analysis found roughly 60 percent of AI Overview citations go to pages that do not rank in the top twenty organic results at all. The model is not reading a fixed leaderboard. It rebuilds the answer from scratch on every query, pulls a fresh set of pages each time, and rotates brands in and out as it goes. More than half the brands that drop out of an answer are back within two runs.

That churn feels like instability when you are winning. When you are not in the answer yet, it is the way in.

So the recovery moves fast for a reason most never clock. In SEO you have to outlast the incumbents. In AEO you have to out-structure them, and structure is something you can change this week.

Your problem is conversion, not discovery

One number makes the whole sprint work, and almost nobody has looked at it.

AirOps studied this head-on. They took 7,500 commercial queries and the 217,508 pages ChatGPT pulled in to answer them. Of every page the model retrieved, only about 15 percent earned a citation. The rest got read and set aside.

The models are not failing to find you. They are finding you and choosing not to cite you. You are already in the retrieval pool. You are losing at the citation layer.

That is the best news you will get all quarter. Closing a conversion gap on pages you already own is fast. Building authority from nothing is slow. This sprint works because it spends every hour on the first problem and refuses to touch the second.

One rule before we start. When you are behind, the reflex is to greenlight a big new content program. Kill that reflex. New pages have to claw their way into the retrieval pool before they earn a single citation, and clawing in is the one thing thirty days cannot buy. Every play below runs on inventory that is already in the pool.

Four plays, in the order I run them.

Play 1: Reclaim the pages you already get retrieved for

The fastest lever in AEO, and almost nobody pulls it first.

Most people hear “refresh your content” and tune out. This is not housekeeping. Because the answer gets rebuilt on every query, a page you have genuinely updated gets reconsidered on the very next run, while the stale one keeps getting passed over. So you do not refresh everything. You refresh the exact pages that are retrieved or mentioned but not cited, plus any page that was cited last month and has since dropped. Those pages already cleared the hard part. They are in the pool and are one signal away from the citation.

The data backs it. AirOps found pages untouched for more than three months are over three times more likely to lose their citations, and that 83 percent of citations on commercial queries land on pages updated within the last year. In SaaS and fintech the window is tighter. Past three months, citation likelihood drops hard.

Here is the filter I build first. One view: cited equals no, retrieved or mentioned equals yes. A second view: cited last month equals yes, cited now equals no. Stack them. Sort by how commercial the query is, so pricing, “vs,” and “best” pages rise to the top. That sorted list is your work order for the week. Nothing off the list gets touched.

Per page, this is a twenty-minute job, not a rewrite. The edits are real. The new date only reflects them:

• Fix any pricing, claims, or numbers that changed since the page went live.

• Swap the oldest statistic for a current one. Fresh data is the cheapest credibility you can buy. This one is huge and I can’t sayserously it enough (closest to finding a “hack” here that I know of).

• Break your densest paragraph into a short list the model can lift.

• Now that the page actually changed, update the publish date to match.

• Resubmit the URL for indexing, then move to the next one.

You are not creating. You are re-entering rotation.

Then watch that exact set as a cohort against your untouched pages, and re-pull it every Monday to mark which ones came back. Do not blend it into your whole-site numbers. Do that and the result vanishes into the noise, and you will not be able to defend it to anyone who matters.

The teams that win this are not more creative. They are faster, and the speed is cycle time, not headcount.

• Chime took content production from days to minutes per brief, cut total production time 89 percent, and tripled their AI citations.

• Venn ran the same play with a two-person team, took a two-hour process down to ten minutes, and grew citations 600 percent.

The constraint is never ideas. It is how many cycles you can run before the quarter closes.

Play 2: Rebuild your comparison pages so the model can lift them

Play 1 is the fastest. This one sits closest to a signed deal.

Comparison queries live at the bottom of the funnel. “Tool A vs Tool B.” “Best X for teams like mine.” The buyer already decided to buy something. They are deciding what. In the AirOps data, comparison pages produced the single strongest citation lift of any commercial page type.

Here is the part that stings. Your versus pages are almost certainly written as prose, and prose is nearly impossible for a model to quote cleanly in a side-by-side answer. So the model skips your page and pulls the comparison from a competitor or a review site that laid it out in a table. You did the analysis…and someone else gets cited for it.

Quick test before you rebuild anything. Read your versus page and try to say the verdict in one sentence. “Pick us if X, pick them if Y.” If you cannot land it in one clean line, the model cannot either, and it will quote a page that can. I seriously read pages like this before I dive into updating them.

The rebuild, with the thresholds I actually target:

• List every “vs” and “best” page you own.

• Turn the prose into tables. AirOps found three tables on a page earned about 26 percent more citations. Build one for features, one for pricing, one for who each option is right for.

• Put real numbers in the pricing table. Pages with seven distinct price points earned about 16 percent more citations. Ranges do not count. Numbers do.

• Cut the surrounding sentences short. On shortlist pages, sentences of ten words or fewer earned almost 19 percent more citations.

Write like you expect to be quoted, because that is the whole transaction.

One thing on those percentages. They are patterns in the AirOps data, not laws of physics. Three tables did not cause the lift, the clarity did, and tables are how you get there. So I do not treat them as dials that pay out on their own. I treat them as the starting hypothesis, ship the change, and let the citations on your own pages tell me whether I was right. Every number in this piece is a place to start, not a promise to collect.

When I ran growth at Webflow, this is the pattern we proved out with AirOps. We restructured CMS and comparison content for extraction and pushed answer share on core CMS queries past 60 percent, with a few hundred incremental citations on top. Same pages. Same claims. Different structure.

I see it again in advisory work. A fintech I work with was getting retrieved on a dozen comparison queries and cited on almost none. We rebuilt three versus pages with live pricing tables and cut the copy down. Inside a month they were cited on most of those queries. No new pages, no new backlinks, no new authority. The model finally had something it could use.

Play 3: Make your pricing and feature pages extractable

The most neglected page on most sites, and the closest one to revenue. That is a bad one to leave alone.

A buyer asking an assistant to confirm a price or a feature is not browsing. They are validating before they commit. That is the exact moment AI referral earns its comparison to paid search. Not because the volume matches yet, but because the intent does. The answer already did the qualifying. The person who clicks through has been pre-sold and shows up ready to talk.

Run the test yourself right now. Ask ChatGPT what your product costs. If it answers from a third-party page instead of yours, or gets the number wrong, you have lost your own pricing page. A stranger’s stale roundup is now the source of truth on what you charge. That is the diagnosis, and most teams have never bothered to check.

The reason it happens is design. A slick interactive slider with no extractable numbers gives a model nothing to cite, so it goes somewhere it can actually read.

The fix:

• Put your real pricing in plain, scannable lists underneath whatever design you want to keep. Tier name, price, seat count, the two or three limits that actually differ between plans. AirOps found validation pages with eight list sections earned up to 27 percent more citations, and pages with fifteen or more price points earned about 11 percent more.

• Keep it current. This page gets punished hardest for going stale, because it is the page buyers verify last before they decide.

I caught this one live with a dev tools founder I am close with. We asked ChatGPT what their Pro plan cost. It quoted a competitor’s roundup, off by twenty dollars a seat, because their own pricing page was a slider with no readable numbers. We dropped a flat tier list under the existing design. By the next pull the model was quoting them, at the right price. Nothing about the page looked different to a human.

This shows up in conversion too, not just citations. Go! Retail Group rebuilt product pages for clarity at scale and saw a 13 percent lift in PDP conversion on the first test set. The same clarity that earns the citation closes the buyer.

Play 4: Get into the third-party lists that are already being cited

The first three plays live on your site. This one does not, and it outweighs all three more than most teams want to admit.

AirOps found roughly 85 percent of brand mentions in commercial AI search come from third-party pages, not your own domain, and that a brand is more than six times more likely to be cited through someone else’s page than its own. About 90 percent of those third-party mentions come from listicles, comparison roundups, and review pages. Position is not random either. Roughly 80 percent of mentioned brands sit in the top three slots.

So this is not “do more PR.” It is precise, and it starts with finding the exact pages that matter:

• Ask ChatGPT and Perplexity your category question. “Best [category] tools for [your buyer].” Note every source it cites. That list, not your wishlist of publications, is your target set. The engines just told you which pages they trust.

• Get added to those specific pages, and push to land in the top three, not buried at number nine.

• Give the author a reason to say yes. Here is the outreach I actually send: “Hey [name], you have [Brand] in your best-[category]-tools piece. Two of the data points are from 2024. Here is the current pricing plus a 2026 benchmark stat you can cite, raw numbers attached if useful.” You are doing their update for them. People say yes to that.

Then run a community layer beside it, and run it like a real surface, not a drive-by comment (these are likely to get flagged as spam regardless). Around 48 percent of AI citations come from user-generated sources, and Reddit alone shows up in about one in five answers, almost always on category-level questions. That is not one thread, it’s really more like a channel.

Start by finding where the engines already look. Ask ChatGPT and Perplexity your category question and watch which specific subreddits, Quora threads, and YouTube videos get cited. That is your target list, same as the listicles. Then work it in three layers.

First, the threads that already rank. Find the Reddit and Quora posts the engines are already pulling from and get a real, useful answer into them. Not “we do that too.” Name three tools including yours, say what each is good and bad at, link to something genuinely useful instead of your pricing page. The engines reward the answer that reads like a person who has used all of them, because that is what a buyer trusts too.

Second, the threads that should exist and do not. If nobody has asked “best [category] tool for [specific use case]” and that is a query your buyers actually type, that gap is yours to fill. Post the honest question, answer it in the comments with real comparison, and you have created the exact citable source the model was missing. This is the move almost nobody makes, and it is the highest-leverage one.

Third, feed the format the model likes. A short YouTube walkthrough of the category, a comparison thread with a clean table in the top comment, a Quora answer structured as a real breakdown. UGC gets cited when it is extractable, same as your own pages. The platform changes. The rules do not.

One warning, because it is the way this play dies. Do not astroturf it. Fake accounts, planted praise, and “we’re the best” comments get pattern-matched and burned, and once a community flags you the engines stop trusting the source entirely. The reason honesty wins here is not principle, it is mechanics. The model is trained to detect exactly the thing you would be tempted to do.

This is the slowest of the four, so start it on day one and let it compound while the on-site plays produce the early wins.

Run it like a channel, not a science project

Here is what turns all of this from activity into a number you can defend in a forecast review.

Paid search has a target, a budget, attribution, and a weekly loop. AI search has earned the same treatment, because it now produces the same kind of outcome. Run it like a channel, or it keeps getting cut like a science project.

Stand up one dashboard with four numbers and review it every week.

Answer share. What percent of the answers to the questions that drive deals include you. This is the target. Pick it and commit.

Dual-signal rate. Not citations alone. AirOps found brands earning both a mention and a citation in the same answer are 40 percent more likely to keep reappearing across runs, and that only about 28 percent of answers contain a brand with both. Dual presence is rare and it sticks. It is the difference between showing up once and owning the answer.

AI referral conversion. Tag the traffic from AI sources and watch how it converts against your other channels. I am not going to hand you a benchmark here, because the honest truth is almost nobody has clean AI-referral-to-pipeline numbers yet, and the figures floating around are mostly guesses. The point of this sprint is to make you one of the few teams that can actually answer it. The mechanism is in your favor and the intent arrives pre-qualified, but you prove the number on your own funnel. You do not borrow mine.

Pipeline influenced. The number leadership actually wants. This is the hop most teams skip and the one that keeps the channel funded. Connect the AI referral traffic to opportunities in your CRM the same way you do for paid. Once a deal can trace back to an answer, AI search stops being a curiosity and becomes a line you defend like any other.

I am in the middle of this with a client right now. Before the sprint, their AI referral traffic was dumping into the “direct” bucket in GA4, credited to nothing and invisible to the pipeline conversation. Step one was not a content play. It was tagging that traffic and piping it into HubSpot as its own source, the same way a paid campaign gets its own line. Three weeks in, four opportunities now carry a first touch that traces back to an answer engine, about 180K in influenced pipeline, and one is already in final-stage negotiation. That number is small next to paid, and that is fine. The point is it exists and it has a name. Their VP of Marketing can sit in a forecast review and point at a deal that started when a buyer asked ChatGPT a question and the answer came back with them in it. That is the moment the channel stops getting cut.

Budget the work the way you would a paid test. Refresh cycles, page rebuilds, placements, all map to hours and dollars. Put a number on it and bring it to finance as a channel, not a hobby.

What the thirty days looks like

Week one. Build the retrieved-but-not-cited filter and start the refresh sweep, hardest-commercial queries first. Stand up the four-number dashboard the same week, so you are measuring from day one instead of reconstructing it later. Fire the off-site outreach now too, so it has runway.

Week two. Rebuild your top five comparison pages and your pricing page for extraction. Run the “ask ChatGPT what we cost” test before and after so you can prove the swing.

Weeks three and four. The refreshed pages re-enter rotation. The rebuilt comparison and pricing pages start getting cited on the queries closest to a sale, and that is where you watch the referral land and trace it into pipeline. Your Monday cohort report shows the slope.

By the time the board deck gets built, you are not explaining why the channel is behind. You are pointing at the climb, and at the first deals that came in through an answer.

Here is the whole thing in one breath. You do not have a discovery problem, you have a conversion problem, and that is the good news, because conversion is fast. You reclaim the pages already in the pool, rebuild the ones closest to the deal, make your pricing readable to a machine, and go earn the third-party lists the engines already trust. Then you instrument it like the channel it is, and you let the data pick the winners instead of guessing. Not every play lands in thirty days. Enough of them do that the number moves and you can defend it.

You did not need a new content engine. You needed to get cited for the pages you already own, run it like a channel, and let the number prove itself.

This is the same sprint I run for the companies that pay me to run it. Take it.

I’ll be tearing this all down live on Wednesday, August 5th at 2:00 PM ET with the AirOps crew. Come hang! Join the webinar →

Part one: what this is

Right now, on a review site or in a thread somewhere, a person who almost bought your product is explaining exactly why they didn’t. In detail. In their own words. To a stranger who can’t do anything about it.

It’s been sitting there for months. Nobody on your team has read it, because nobody has time to read ten thousand threads.

Provv reads them, and builds you a panel of buyers out of what it finds.

Not personas. Not a model doing an impression of your ICP. Buyers reconstructed from the record: the threads, the reviews, the forum replies, the community posts where your market explained itself to itself while you weren’t in the room.

Then you run your work past them. Copy. Creative. Video. Briefs. Whatever’s about to ship.

Every buyer comes back with a verdict and the exact words behind it. Who bought it, who didn’t, and the specific sentence that lost them. Click any objection and it traces back to something a human being actually wrote. Not the software’s opinion. A position that exists in the record, held by a person.

It drops into Claude through our MCP. Paste the draft, watch the panel react in the thread, rewrite, run it again. Sixty seconds instead of six weeks.

Plays runs it the other way. Instead of judging what you made, our agents read what your market said this week and hand you back a campaign. Channels, copy, a schedule you can steal. Market signal to campaign in minutes.

That’s the product.

It runs on one idea. Your buyers already told you. They just didn’t tell you to your face.

What it costs

I’m opening ten invites. Free. Not a trial, not a discount. Free.

Not for the money. Because I want ten people building it with me while it’s still being decided, and ten is the number of people I can actually be useful to.

Want in? Reply, or DM me on LinkedIn, with the one thing you’re trying to figure out about your buyer right now. The more specific the sentence, the better the panel. I’ll close it when it’s full.

Not one of the ten? Provv Beta is open to everyone.

Provv.ai

What it isn’t

It won’t predict your conversion rate. It doesn’t replace talking to customers. It’s useless in markets that don’t talk online, and it’ll tell you when yours is one of them.

Full list of reasons not to buy it is in part three. I’d rather you read that than the feature list.

Part two: why I built it, and how AEO research led me here

The meeting

You know the meeting.

Six weeks of work on the screen. Positioning doc, message house, the headline three people fought about, the proof points, the calendar. Twelve people on the call. It’s good work.

And somebody, usually whoever’s paying, asks the only question in the room.

How do we know this is going to land?

Everyone does the thing. The research supports it. We socialized it with sales. The advisory board loved it. Let’s ship and see.

Nobody says the true sentence: we don’t know, we think so, we’ll find out in three weeks, and even then we won’t really know.

Fifteen years. B2B and B2C growth at Affirm, through the IPO. Self-serve and enterprise growth at Webflow. A global digital marketing team at FIS across a dozen markets. Hundreds of millions in spend.

Different companies. Different motions. Different decades.

Same hole in the middle of every one of them.

Every other function tests. Marketing guesses.

Not an insult. Structural…and the vagueness about it is why nobody fixes it.

Engineering ships to staging first. Tests, canary deploys, feature flags, rollback in ninety seconds. An engineer can be wrong at 2:00 and correct by 2:04.

Finance models the downside before the money moves. Base case, bear case, sensitivity. The whole discipline exists to know what happens when you’re wrong.

Sales walks in with a strategy. Discovery, qualification, a champion, a mutual action plan, a known objection list with a rehearsed answer for each one.

Product puts a prototype in front of five humans before anyone writes production code.

Marketing writes a doc, argues about it, and picks a date.

Then we ship, and three weeks later a number shows up.

The problem isn’t “no data.” It’s the shape of the signal.

Marketing isn’t short on data. Marketing is drowning in it. We have more dashboards than headcount.

The problem is that the signal coming back is slow and flat, and the decision it’s meant to inform is fast and enormous.

Look at what you actually decide when you approve a campaign:

• Which problem to lead with

• Which of six ways to say it

• Which objection to preempt, which to ignore

• Which proof goes first

• Which channel

• Which sequence

• What to leave out

Dozens of live variables. And the feedback is one number. Pipeline. MQLs. CTR. Whatever the board looks at.

You can’t take one number apart. When it’s bad, you have no way to know whether the message was wrong, the channel was wrong, the timing was wrong, the offer was wrong, or a competitor announced something on Tuesday. One bit of information about a forty-variable decision, three weeks late, by which point you’ve already committed the next six weeks.

So you do the only rational thing left. You guess, confidently, and then you defend the guess.

That’s not a character flaw. That’s what any sane person does when the loop is that broken. The confidence is a coping mechanism for the delay.

Every marketing org I’ve run or advised has this hole. Most have decorated it with a persona doc.

The detour

I did not set out to build this. I set out to do something else and walked into it sideways.

I spent the last two years deep in AEO. Reverse engineering what the models cite, why they cite it, what you can do about it. That work turned into the Answer Ownership System, Answer Capture Rate, most of what I write and most of what I do for clients.

The core mechanic is easy to say and miserable to execute: if you want to know what the model cites, go where the model learned it.

So that’s what I did. Two years tracing citations backward, into the substrate. Which forums. Which review sites. Which threads. Which communities. Which specific posts shape how a model talks about a category. It’s unglamorous work. It’s mostly reading.

And that’s the thing. It’s mostly reading.

I read for two years. Not summaries. Not dashboards. The actual threads, the actual reviews, the actual replies, because that’s the only way to see the shape of what a model learned.

Somewhere in there, I stopped caring about the citations.

Because I finally looked at what I’d been staring at all day. It wasn’t a citation graph.

It was the most honest record of buyer opinion that has ever existed, sitting in public, unread.

Everything I’d been treating as a means to an end was the end. I’d built a two year habit of reading the places where buyers tell the truth, in order to win a ranking. The ranking was the small prize.

Why that record is honest

Every method we call “customer research” is distorted in proportion to how present the vendor is in the room.

Rank them by that one variable and the whole discipline falls apart.

Sales calls. The vendor is physically there, wanting something. Your buyer is performing and negotiating. They will not say “your pricing page confused me and I felt stupid,” because that’s a status admission to a stranger trying to sell them something.

Win/loss interviews. The vendor is on the phone asking someone to explain a decision that affects the vendor. You get the polite reason, which is never the real reason. “It wasn’t the right time” is what people say instead of “your product annoyed me and your CSM made me feel like a ticket.”

Surveys. The vendor is implied. People pick whichever answer ends the survey fastest.

Customer advisory boards. Selection bias so severe it’s funny. You’ve assembled the people least able to tell you what’s wrong and asked them what’s wrong.

Personas. The vendor wrote them.

Now: your buyer, on Reddit, at 11pm.

No vendor in the room. No relationship to protect. No procurement leverage to preserve. No status to manage, because they’re anonymous. They’re talking to a peer, and the peer is the only audience that gets the truth, because the peer is the only person who can’t do anything with it.

The audience changes the answer.

That’s the insight. Not “there’s data on Reddit,” everyone knows there’s data on Reddit. The insight is that anonymous peer conversation removes exactly the distortions every formal method introduces. It’s the only place your buyer has no reason to manage you.

Your buyers already told you. They just didn’t tell you to your face.

Personas are a compression of what you wish were true

Where does a persona actually come from?

A workshop. Notes from sales calls, which are performances. Four customer interviews, which are your happiest customers, because those are the ones who take your calls. The PMM’s intuition, which is often good and is still one person. A stock photo of a woman in glasses looking thoughtful. A name that alliterates.

A persona isn’t a model of your buyer. It’s a compression of what your company wishes your buyer were. Aspirational by construction, because the people who built it needed it to be true. Written once. Updated never.

That’s not the fatal part. This is:

A persona cannot disagree with you.

It’s a document. Documents don’t push back. You can’t run a headline past a PDF. You can hold your copy up next to Marketing Mary and nod, and Mary nods back, forever, because she’s a slide.

The persona is a mirror you built and then asked for advice.

“Just ask ChatGPT to be my ICP”

I’ll get this objection in every conversation for the next two years.

You can open a chat window right now and say “you are a VP of Engineering at a Series B fintech, react to this landing page.” It’ll produce something articulate and plausible.

It’s close to worthless. Four reasons, none of which require you to touch my product.

1. You get the average, and the average never bought anything.

Ask for a VP of Engineering and you get a blend of everything ever written about VPs of Engineering. Markets aren’t decided at the average. They’re decided at the edge: the specific objection held by the specific fifteen percent who almost bought. The average VP of Engineering does not exist and has never signed a contract.

2. You get your own stereotype handed back to you.

Think about what’s actually been written, at volume, about VPs of Engineering. Job descriptions. LinkedIn thought leadership. And an enormous pile of vendor content about VPs of Engineering, written by marketers, describing the VP of Engineering that marketers wish existed.

So when you ask a model to play your ICP, you’re asking it to blend a pile substantially made of persona docs. You’ve automated the mirror. Same thing as before, faster, and now it talks back in complete sentences.

3. It wants you to like it.

These tools are built to be helpful and agreeable. Correct for an assistant. Catastrophic for research. Ask “would you buy this?” and the rate of enthusiastic yes is absurd. You’ll get “this is compelling, and here are three thoughtful suggestions” for copy that would die on contact with a real market.

The tool is optimized to make you feel good about your work. That is the precise opposite of the job.

4. It has no idea who your buyer is.

“VP of Engineering at a Series B fintech” isn’t a market. It’s a demographic. Your market is the four hundred people with this specific problem, this quarter, at this price, against this competitive set.

No prompt fixes any of this. You can’t write your way out of an average. The only fix is anchoring the thing to a real, current, traceable record of what your actual market actually said.

Which is the thing I’d been building for two years without noticing.

Don’t take my word for it. Take ten minutes.

Stop reading. Do this. It costs nothing and it doesn’t involve me.

1. Open your persona doc. The real one, not the one on the wiki. Find the section called pain points, or challenges, or “what keeps them up at night.”

2. Open a browser. Search site:reddit.com plus your category plus the problem you think you solve. Sort by new, not by relevance. Relevance sorts for what’s popular. New sorts for what’s true this week.

Fair warning: your first page of results will mostly be people selling services in your category. Scroll past it. That’s the disease, and I’ll come back to it in part three.

3. Read ten threads. Not skim. Read. Including the replies, because the replies are where people correct each other, and correction is where the honesty is.

4. Write down the objection that keeps coming up. Not the one you think matters. The one that won’t go away.

5. Go back to your persona doc and find it.

That gap is the company. That gap is the six weeks. That gap is every number that came back bad and couldn’t tell you why.

You just ran the manual version of what I built. Sample size of ten. Ten minutes. Free.

Now try it at ten thousand.

Part three: what it actually looks like

Here’s the thing itself, including the parts I’m not proud of.

Building a panel

Every buyer on that roster is a cluster of real positions. Not a job title with a personality bolted on. The problems they described, the objections they raised, the tradeoffs they made, the words they used to describe the category to each other.

A verdict, and the trace behind it

This is the part I’d judge any tool in this space on, mine included.

When a buyer objects to your headline, you can click the objection and land on the thing a human being actually wrote. It isn’t the software’s opinion. It’s a position that exists in the record, held by a person, in their words.

If you can’t click through to a human, you don’t have research. You have a vibe with a nice interface.

The loop, inside Claude

This is where I actually use it. Not the web app. Claude.

Paste the draft. Watch the panel react in the thread. Rewrite. Run it again. The whole loop lives in one conversation, which means the cost of testing an idea drops to roughly zero, which means you test ideas you’d never have bothered to test.

That’s the part I didn’t predict. When the loop is sixty seconds, you stop defending your first draft, because defending it costs more than checking it.

Plays

Same record, other direction. Instead of judging what you made, the agents read what your market said this week and hand you back a campaign. Channels, copy, a schedule you can steal.

Market signal to campaign in minutes.

What’s underneath: the part nobody wants to talk about

Everything above is worthless if this part gets hand-waved. Take this one and use it against me.

The data is not clean. Anyone who tells you their market data is clean has either not looked at it or is hoping you won’t.

Astroturf. Review sites are gamed. Vendors run campaigns. Leave a review, get a twenty-five dollar gift card. Not fraud exactly, but a brutal selection effect: incentivized reviews skew positive and, worse, skew generic.

What we do: weight by specificity. “Easy to use” carries almost no signal. “The SAML setup took our IT team three weeks and we almost bailed” carries an enormous amount. Specificity is the best available proxy for honesty, because it’s expensive to fake and nobody bothers for twenty-five dollars.

Power-user skew. Reddit is not your market. Reddit is the three percent of your market that posts on Reddit, and that three percent is more technical, more price-sensitive, more contrarian and considerably more negative than your actual buyers.

What we do: say so, loudly. A panel is not a representative sample and I’ll never claim it is. It’s an honest sample. Different instruments, different jobs, and confusing the two is how this whole category discredits itself.

Staleness. A thread from 2021 describes a market that no longer exists. Your category moved. The competitive set moved. Price expectations moved. Old data produces confident buyers with obsolete objections, which is worse than no buyers at all, because it’s wrong with a straight face.

What we do: recency weighting on panel construction. Plays windows to the last week, on purpose.

Contamination. This is the one that keeps me up, and the thing you should interrogate hardest in any tool like this, mine included.

If your competitor’s content marketing gets into the pile, your buyers start sounding like their blog. Vendor content is written to be found, which means it’s overrepresented in exactly the places you’re looking. Scrape naively and you don’t build a panel of your market. You build a panel of your category’s marketing departments, agreeing with each other, in a nice voice.

Here’s how bad it is. While writing this post I picked a well known B2B product and went looking for real buyer conversation about it. Plain searches, the kind anyone would run.

Here’s what came back on page one: a Medium post about the category, four Fiverr gigs, three Dribbble service listings, a Capterra comparison page, two job ads, and a Substack selling a consulting course.

Almost no buyers. Almost entirely people selling services in that category, ranking for the exact words buyers use.

Try it on your own category right now and you’ll get the same thing. That’s not an edge case. That’s the normal condition, and it’s what every naive scrape eats first.

What we do: classify sources, aggressively. Vendor-authored content can inform the category map. It is not allowed to inform a buyer. A buyer can only be built from someone talking to a peer, not to a funnel. This has eaten more engineering time than anything else in the product and it is not finished.

Attribution. Is this person actually your buyer, or a student writing a paper, or a competitor’s SDR, or a bot?

What we do: cluster on problem language, not identity claims. Someone who describes your problem in lived detail is far more likely to be your buyer than someone who opens with “as a CMO.” Stated identity is cheap. Demonstrated fluency in the problem is expensive.

Volume versus honesty. Ten thousand threads sounds impressive. It’s a vanity number. What matters is whether the threads contain a decision: someone weighing something, choosing something, regretting something. A million threads of “interesting, thanks for sharing” is a million threads of nothing.

What we do: grade every panel. Source density, recency, specificity, decision content.

Steal this: five questions to ask any synthetic research tool

Including mine. Especially mine.

1. Where did the data come from, specifically? Not “public sources.” Which ones. If they won’t name them, they’re embarrassed by the answer or there isn’t one.

2. Can you trace one verdict back to one source? Click the objection. Does it lead to a human who said something, or does it lead nowhere? This is the whole ballgame. Everything else is downstream.

3. How old is the median source? Ask for the number. If they don’t have it, they aren’t measuring it, which means it’s whatever the scrape happened to catch.

4. What’s excluded, and why? If nothing’s excluded, everything’s included, and everything contains your competitor’s blog, three SEO farms, and a Medium post a machine wrote in 2023. An honest tool has a deny list and will show it to you.

5. What does it do when it doesn’t know? A tool that always has a confident answer is a tool that’s always guessing. Ask to see it fail. If it can’t fail, it can’t be right either.

That rubric is yours whether you ever touch Provv or not. Use it on me first.

What it can’t do

Plainly, because the limits are what make the rest of it credible.

It won’t predict your conversion rate. Directional, not quantitative. Anyone selling you a synthetic panel that outputs a forecast is selling you a number they made up.

It doesn’t replace talking to customers. It replaces the six weeks where you were talking to nobody. Go talk to your customers. Then use this to work out what to ask them.

It’s useless in markets that don’t talk online. Some enterprise categories are dark. Some regulated industries are silent. Some buyers only speak in rooms with no internet. The grade will tell you, and if the grade is bad, don’t buy it.

It skews negative. People post when they’re annoyed. Complaints are overrepresented in the record and therefore in the panel. Read every verdict with that in your head.

It tells you what people say, not what people will do. Nobody can do the second one. Anyone who claims they can is selling you something, and I’d like to be the person in this category who says that out loud on launch day.

I ran this launch through it

I’m not going to write four thousand words about evidence and then ask you to take my word for it.

I ran my own launch post through a Provv panel before I published anything.

Then I rewrote it. Six times.

The product told me I was wrong. I was.

Is the product perfect yet?

No. That’s the point of building in the wild.

I could have spent another six months polishing behind a curtain until it felt safe, and shipped something extremely good at satisfying me and possibly nobody else. I’ve watched a hundred companies walk into that. You build in private, you launch to applause, and then you find out.

So I’m doing it the other way. It’s in beta, in public, while it’s still being decided, and the people using it get to decide it with me.

I don’t want you to try my product

I want ten people to build it with me.

Ten invites. Free. Not a trial, not a discount. Free.

A cohort. Ten operators chasing something real about their market. You’ll use it. You’ll break it. You’ll tell me the thing I got wrong, and I’ll fix it that week. You get it free, you get the room, you get me directly, the whole way through.

Because the goal here isn’t to blow this up. The goal is to learn.

The unfair advantage isn’t the tool. It’s that your market already told you, and nobody else read it.

Want in? Reply, or DM me on LinkedIn, with the one thing you’re trying to figure out about your buyer right now. The more specific the sentence, the better the panel. I’ll close it when it’s full.

Not one of the ten? Provv Beta is open to everyone.

I love doing this. Sitting down with an operator I trust, hitting record, and getting the real version of how they actually work. I did one with George from Ramp not long ago. Nick Lafferty was the one I wanted next.

Marketing engineering is exploding right now, and Nick Lafferty is one of the first people to hold the title. I’ve worked alongside him for about a year, and his work is some of the sharpest I know. So when I wanted the real answer on this role, he was the only call.

Here is the thesis I brought in. Marketing is not becoming engineering. The execution layer is. Building the pipeline. Cleaning the data. Running the agents. Winning the citation. That floor is getting engineered fast, and it is not going back. The judgment layer is a different story. What to build, what it should say, which bet is worth the chips. As the floor gets cheap, judgment absorbs the value.

I wrote the playbook for this role in April. Then Profound made it real. Idea on stage in April. A person in the seat by June. All in the open. The category caught up after: Figma, Plaid, Cloudflare, Ramp…all these companies are investing in the role. There is now a job board just for it.

Before Profound, Nick led growth at Loom and Mailgun through two acquisitions and spent six million dollars on Google Ads in a single year. Then he turned down Head of Marketing to build the seat he actually wanted.

So I hit record and asked him to show me the real thing. Operator to operator.

The spine

You ended up in a seat that did not exist yet. Nick, you turned down Head of Marketing to build it. Take me back to the moment the normal next step started to look like the wrong one. What did you see that the people around you did not?

I wanted to optimize my career, and my life, towards maximizing joy and fulfillment. It sounds simple but I knew having a head of marketing role wouldn’t make me happy. Sure the title feels good and it looks good on LinkedIn, but the actual work would make me miserable. I’m at the point in my career where I can make choices like that. So even though I knew what I didn’t want, the next step wasn’t immediately clear. I went to James, our CEO, and gave him a heads up, but I didn’t know what came next.

A few months later, this idea of the marketing engineer started to emerge internally and I was literally the perfect person for it. By turning down a job I knew wouldn’t make me happy, I ended up in exactly the right place.

I wrote that this role owns output, not pipes. That it closes the loop alone. Identifies the problem, builds the thing, ships it, measures it, kills it or scales it. So show me one. Walk me through a single system you shipped in the last month that a traditional team would have turned into a brief, a meeting, and three people. What did it replace, how long did the old version take, and what is the number that proves it worked? I want it specific enough that I could rebuild it from your answer.

I’ll give you a very meta example. We launched a podcast recently called The Marketing Engineer. I’ve interviewed Heads of Growth at Wispr Flow, Figma, Samsara and about 5 more companies that haven’t come out yet. We have this incredibly rich and content dense source material, in the video recording and the transcript. The smart play is to repurpose that content into 10-12 different formats across LinkedIn, Twitter, and Youtube.

So I built a system I’m calling the Profound Podcast OS that does exactly that. It takes the transcript and a bunch of shared context Claude skills for Profound’s tone of voice, our writing style, our social media guidelines, and a file for how I like to write in my own personal voice on social.

It helps me draft all the content we want to repurpose across all channels and then stages them in our social accounts for review by my team. It also sends a short Slack message to our internal Sales and Customer teams, giving them a very short summary of the episode and why it matters, so they can share with prospects and customers.

But I didn’t want to stop there. For each episode I write out questions ahead of time, and I wanted a way to analyze how good I was at asking those questions and getting the right response. So within this whole Profound Podcast OS there is a coaching section that analyzes my questions, compares them to the transcript, and then grades me on dimensions like Talk Share (how often I speak vs the guest), Improv ability (how well am I following conversational threads), and Specificity (am I getting exact details).

It runs that across every episode I’ve recorded so far and I can see improvements in some areas, and still some areas I need to work on.

Here is a thought of mine I want you to confirm or break. The tooling is the paintbrush and the marketing engineer is the artist. That a beginner with a great brush still paints a beginner’s painting. That someone who can only build becomes a backlog and someone who can only imagine becomes a PM. In your seat, which half is driving the work, and was I right that the taste has to lead, not the system?

Mostly right, with one correction. The artist framing works well. A beginner with Claude and every tool in the world still ships beginner work, because the tool will happily produce a confident, average thing. But average isn’t good enough.

The reason I can look at an output and know it’s wrong in four seconds is that I’ve shipped the wrong version of that thing before and watched it underperform. So much of that is driven by my experience of frankly screwing a lot of things up over the years. Someone who only imagines never earns that calibration, so their taste stays theoretical and soft.

So in my seat the judgment leads, yes. But judgment is a muscle you only grow by having your hands on the work. The pure builder becomes a backlog and the pure imaginer becomes a PM. The person who’s actually dangerous built enough to develop real taste and then started using it to kill things.

You ran the old playbook at a high level. Paid, funnel math, attribution, the whole thing. What part of that training actively misleads you now? Where does the instinct you spent a decade building still pull you in the wrong direction inside this seat?

The instinct I need to fight, and I think most marketers right now do too, is trying to do all of the tasks you used to do manually. Building campaigns, executing on individual work. That’s the kind of thing that does not scale and is the opposite of thinking in systems, which is how a marketer in 2026 should operate.

Instead of rewriting your ad copy manually for Google Ads, build a system that follows all your best practices, downloads your ad copy from the Google Ads API, makes changes, and then pings you for review. This can be a skill in Claude or something you code into a system with Claude Code or Codex.

It’s the systems thinking that every marketer needs to adapt right now.

I have a strong opinion about where this role belongs. It owns what it builds and influences everything else. It reports to whoever owns the GTM number, and it dies three levels down. You are the test case. Where do you actually sit, did the org try to hand you a single lane to own, and where does the rest of the team still not know what to do with you?

I spoke about this in my talk at Zero Click NYC this year, but we strongly believe marketing engineers need to sit within the marketing org and reporting to your Head of Marketing, VP, or CMO. That’s exactly how it sits at Profound, we have two associate marketing engineers that sit underneath me as well. I run twice-weekly standups with them, acting almost as an engineering manager, to help triage new tasks and direct them towards what are the highest priority areas to build in right now.

For the reader who wants to become this, or hire it. Output is cheap now, and the machine scales the average, so the demo everyone shows up with is no longer the signal. I wrote that the best ones are boring about the technology and precise about the output, and the worst are performing AI instead of using it. What separates a person who can do this job from someone who has read every thread and built one impressive thing? What is the part you cannot teach?

You really need someone with deep marketing experience first. They need to know what not to do and have a refined sense of taste and judgement from a career in marketing. Then you can layer on the AI builder skills to give them superpowers.

Not the market forecast. You have your hands in this every single day, which means you see things before the tooling and the rest of the industry catch up. What can you see coming from inside the seat that almost nobody outside it has clocked yet?

I think AI systems will quickly move from running locally on your computer into the cloud. We’re seeing this movement with Anthropic’s new Slack bot called Claude Tag. Everything will move into the cloud which in many ways makes a marketing engineer’s job easier – the hardest transition right now is going from a single player build (building something that works for you on your local machine) into multi player build (scaling that system so your whole team can use it).

We’re still in the very early stages of watching all of this play out.

You have a phrase for the build-versus-buy call: tolerance for jank. I want the unglamorous version. Where is the line between a janky system that works and a janky system that quietly poisons your GTM six months later? Give me a time the jank actually cost you something.

I think a lot of internally vibe-coded tools will need to be rebuilt in 6 months! And I think that’s ok, it needs to solve a short-term need now and then you can reevaluate down the road, because needs change and priorities shift and there is a real cost to maintaining these things that most marketers aren’t used to. This goes back to my singleplayer vs multiplayer analogy earlier, once you start building something that other people use internally you have a real responsibility to maintain it for all your users. And maintenance kind of sucks.

I think it’s ok if some internal systems are short-lived. I built a tool that syncs Email Unsubscribes across a few different marketing automation platforms we have. It works great now but we will eventually consolidate those systems into one, which is the right long term call. So my system is a band-aid by design. It didn’t take long to build (2-3 hours max), solves a real time suck for my team now, and gives us a bridge to figure out the right email system long term. Everyone wins.

Profound is building the marketing workbench for superintelligence, which means you cannot hide behind theory. You have to win the thing you sell. The Reddit agent that hunts for threads likely to become future citations. The engine that turns every old webinar into something built for how AI reads. Without me feeding you the numbers, what has building produced that headcount never could, and what is the one metric you watch that your peers are not?

Building these systems enables our entire marketing team to keep up with the massive growth Profound is seeing. Every marketer here needs to be a force-multiplier for the business, and AI systems help 10x every marketer who works here. Most people don’t know this, but through the end of 2025 there were only 4 marketers on the team here, and we kept hearing “Profound is everywhere”. That kind of scale was only possible because of the systems we built and because we hire world-class people. We’re still hiring, by the way.

The takeaway

The floor is getting engineered whether you like it or not. Your edge is not the tool. It is the calibration to look at an output and know in four seconds that it is wrong, because you shipped the wrong version once and watched it die. You only earn that by keeping your hands on the work.

So start there. Pick one manual task you repeat every week. Build the system that kills it. Ship it janky. Then go build the taste to know what to kill next.

Be on the lookout for more marketing engineer content. I will be hosting an upcoming event in a few weeks. Details to come.

It starts with where AI citations actually come from…and almost none of it is your own website. The exact on-page versus off-site split is hard to quantify, and every study measures it differently, but the direction isn’t in question. AirOps found brands get cited about 6.5x more often through third-party sources than their own domains, and the newer industry reads keep pushing the on-page share down, some now putting it under 10%. The rest happens off your site: mentions, review platforms, community, third-party coverage. Almost everyone is still automating the small slice they control and hand-touching the majority that actually decides who gets cited. I flipped it.

So here are the five automations I’m running right now, the data behind each, and what I’m actually seeing. If you’re a CMO or growth lead and your team is doing any of this by hand, that’s the tell you’re behind, not that you’re careful. One caveat I’ll hold the whole way down, from Cyrus Shepard’s May meta-analysis of 54 studies: these are mostly correlations, not proven levers. It’s the map I’m betting on, not a guarantee.

Amongst others…if you hired me or my team right now, this is what we'd be running this month:

1. The weekly citation-gap sweep

This is the control tower. Every Monday, my tracked set of buyer prompts runs across ChatGPT, Perplexity, and Google AI Mode. It logs which domains get cited, where I’m absent, who’s winning the answer, and the week-over-week movement. Everything else in the stack runs off what this surfaces.

The reason it has to be automated and per-engine: the engines barely agree with each other and the gap is increasing. ChatGPT and Perplexity share only around 11% of their cited sources, and the same brand can swing wildly between them. If you’re eyeballing one platform once a month, you’re blind on most of the map and you can’t see a gap opening until it’s a canyon. I’m running this as a standing job, not a quarterly audit. That’s the difference between reacting and steering.

Obvious, but everything else is built on it.

2. The piggyback mention engine

This one feeds off the sweep. When automation one flags the third-party pages and community threads that AI keeps citing for my category, this play turns them into a weekly shortlist of places to go earn a spot: the roundup author to email, the review profile to complete, the thread where I can add something genuinely useful.

The logic is the 6.5x third-party finding made operational. Instead of trying to out-rank the whole internet with my own domain, which is a multi-quarter grind, I go get included in the sources the models already trust, which moves in weeks. Brand mentions correlate with AI visibility about three times more strongly than backlinks (roughly 0.66 versus 0.22 in Ahrefs’ 75,000-brand study), and the mention doesn’t even need a link to count.

This is the play I'd have killed for a shortcut to back at Webflow. When my team ran our AEO program there, we pushed CMS answer share past 60% (despite 1.x% market share) and earned north of 300 citations, and the honest lesson was that the step-change came from the off-site work, not the on-page polish we over-invested in early. It's now standard in every client program I support. One guardrail baked into the automation: it only queues genuine contributions, because promotional posts get filtered out of communities within hours.

3. The ship-it-twice LinkedIn system

Every point of view I create gets automatically forked into two formats: a named, personal post that opens with a clean 40-to-60-word answer, and a longer article in the 500-to-2,000-word range. Both scheduled, cadence held at two to three times a week, originals only.

LinkedIn is the single biggest mover in the data. It’s the most-cited domain for professional and B2B queries across the major engines, and its authority on ChatGPT roughly doubled in a three-month stretch, the largest shift in that dataset all year. The format detail is what makes the automation worth building: named individuals account for around 92% of cited LinkedIn content, not company pages, and reshares almost never get pulled. So the system publishes from a person, publishes original, and keeps the cadence high enough that consistency does the compounding.

This is the highest-ROI automation I run, and it’s the one most teams are still treating as a manual “post when we have time” afterthought.

4. The freshness watchdog

Any of my priority buyer-intent URLs that crosses 90 days without a meaningful update gets auto-flagged, and the flag comes with a pre-drafted refresh brief: a new stat to add, an example to update, the modified date to correct. It turns freshness from a thing I forget into a queue I clear.

Recency is a gate, not a nicety. For commercial and evaluation-stage queries, 83% of AI citations came from pages updated within the past year, and more than 60% within six months. The bias sharpens fast after about three months. The pages this matters most for are the exact ones tied to revenue: comparisons, pricing, evaluation. So those are the ones the watchdog guards first.

5. The first-party data flywheel

Once a month, this pulls one proprietary number out of my own systems (usage patterns, funnel benchmarks, or an audit like the category sweep behind this very post) and packages it as a quotable stat with a simple chart. That one data point then seeds the LinkedIn system and the mention engine.

This is the compounding one, and if I could keep only a single play it would be this. Original data earns brand-specific citations that borrowed third-party stats can't, because engines cite the origin of a number. The highest-return thing you can publish isn't a landing page or a comparison grid, it's one number pulled from your own systems, the kind that gets quoted across dozens of independent posts. Every one of those quotes feeds straight back into automation two, because a proprietary number travels on its own. I'm running exactly this right now with a Series D AI company you'd recognize instantly, and it's the single highest-leverage move on the board. A competitor can out-publish me on generic advice any day of the week. They cannot republish my data as theirs.

What I’m deliberately not automating

A good stack is defined as much by what it leaves out.

I’m not automating llms.txt babysitting. I’m not automating more schema as a growth play. Schema is packaging that helps a machine read a strong page. It won’t rescue a vague one, and treating it as a strategy is where a lot of budget quietly dies.

And I’m not chasing raw mention volume when the real gap is trust. If engines name me but cite someone else, the fix is stronger source content on my own domain, not more noise pumped into the system.

The through-line

AEO in July 2026 is a consensus game played mostly off your website, and the winners are treating it like infrastructure, not a content chore. The on-page craft is table stakes now. The leverage is in the systems: a control tower that watches every engine, an engine that earns you into the sources AI already trusts, a publishing rhythm from real human voices, a freshness clock on the pages that matter, and a data flywheel only you can spin.

These five are running for me this month. Steal the stack.

Hmu with questions.

I understand the instinct, because I had it too. When I first started taking AI search seriously, my first move was to point the content machine at the problem and pull the lever harder. More pages. More answers. More coverage. It felt like progress, mostly because I could measure the output, and output is the one part of this whole thing you completely control.

It mostly did not work. The wins, when they showed up, came from somewhere else entirely.

Here is the number that I can’t stop referencing. Ahrefs ran a study this year on what correlates with getting cited by AI engines. The relationship between how many pages a site has and how often it gets cited landed at 0.194. For anyone who has not stared at a correlation coefficient lately, that is a polite way of saying there is almost no relationship at all. You can triple your page count and the models will shrug.

So why does everyone keep reaching for it anyway?

Because publishing is the one move you can make alone, today, with nobody’s sign-off. You can ship a post by Friday. You cannot ship a Reddit community deciding you are worth trusting by Friday. One of those feels like work. The other one gets you cited. We tend to pick the one that feels like work and then call it a strategy.

Most of what gets sold as AEO right now is a content marketing program with an AI label quietly stapled to it. Same calendar, same briefs, new dashboard. The teams pulling ahead are doing something less glamorous and a lot more useful. They have stopped trying to out-publish the problem and started building systems that watch where answers are forming and put themselves in front of them.

Three of those systems are doing most of the work for my clients right now. Steal all three.

(Source: Ahrefs)

1. Hunt for the gaps, not the wins

The first one is almost embarrassingly simple. I take a fixed list of the questions our buyers ask, run them across ChatGPT, Perplexity, Google’s AI Mode, and AI Overviews on a schedule, and log who gets cited. Brand, URL, type of source.

Everyone wants to look at who showed up. The part that matters is who did not. Specifically, us, on questions where we already rank perfectly fine in regular Google.

When that happens, and it happens constantly, it is almost never because we did not publish enough. Something else is off. The model trusts a different source. A competitor answered the question more cleanly. The citation is coming from a review site, or a Reddit thread, or a comparison page we have no presence on whatsoever. That gap is the to-do list. Not “go write more,” but “go figure out why a G2 page is beating our own product page inside Perplexity.” That second question is where the work gets interesting.

2. Catch the decay before the traffic does

The second system exists because of a timing problem.

Most teams find out they have lost AI visibility the way you find out about a roof leak: once the ceiling is already stained. By then you are not preventing anything, you are explaining it in a QBR.

So we stop waiting. We watch the pages and topics that matter for early signs of slippage. Fewer citations than last month. Showing up lower in the answer. The evidence on the page going stale. A competitor quietly taking a spot we used to own. The second one of those trips, we do not open a blank doc. We go back to the winning page and make it better. Answer the question in the first two sentences. Add a data point nobody else has. Tighten the parts of the page the model uses to figure out what it is even about.

The question moves from “what should we publish next” to “which of our winners is starting to slide.” That is a much shorter list, and it pays back far more than anything net-new.

3. Get to the question before anyone answers it well

If you made me delete the other two and keep one, this is the one I keep.

We are constantly pulling fresh buyer questions out of the places people actually ask them. Reddit. Quora. YouTube comments. Review sites. The private communities. And the most underrated source on the list by a mile, sales calls, where your prospects describe exactly what confuses them, for free, in real time.

Then we rank them. Not by search volume, which is the old reflex. By how likely the question is to get cited. Is it coming up over and over? Are the AI engines answering it badly today? Are the sources they lean on weak enough that we could realistically take the spot? When the answer is yes, we do not default to a blog post and a prayer. We go put a real answer in the places the models are already reading: the community threads, the videos, the third-party write-ups, the comparison pages, and our own site too.

Reddit does not care about your content calendar. It cares whether you said something true and useful when someone asked.

The actual job

None of this is more work than running a content calendar. It is different work. Less writing, more listening. Less guessing about what to make, more watching where the answers are forming and showing up there before your competitor does.

That is the whole shift. The brands winning AI search this year did not get there by making the most content. A few of them are making noticeably less of it than they used to. They got there by being the answer in the rooms where the question gets asked.

So publish less. Pay attention more.

Then go steal all three.

Brand audits, competitive narrative studies, customer sentiment refreshes, the “how is the market actually framing us” project, the messaging validation work that used to precede every launch. Every one of those was historically either a $100-300k engagement with an outside research firm, or a six-to-eight-week internal sprint that produced a static deck most of the org never opened twice. Both took months, and both were already stale the day they shipped.

Now you build a private market intelligence app yourself, in a weekend, for around thirty dollars in API spend. The app scrapes every public mention of your company and your competitors across Reddit, G2, Capterra, TrustRadius, podcast transcripts, LinkedIn, Hacker News, comparison content, and operator newsletters. It clusters what it pulls into a structured narrative: positioning gaps, messaging opportunities, competitive scoring across the dimensions you actually care about, sentiment heatmaps by theme, and the verbatim quotes that hold up when someone pushes back on a finding. You ship it as a private, password-gated URL the team can scroll through and re-run on demand.

The tool is Lovable, the artifact is yours, and the work that used to take months and burn cash now ships in a weekend for about the price of dinner.

I built one for Clay (the GTM data orchestration platform) as the worked example for this piece. I don’t work with Clay. I picked them because they had the right conditions for a useful teardown: loud sentiment in both directions, a crowded and rapidly evolving competitive set, and an unusually deep public data trail across Reddit, G2, operator podcasts, and LinkedIn ambient commentary. If Vibe Market Research works for Clay, it works for anything.

You can play with the live Clay Narrative OS at before reading the rest. What follows is a walkthrough of the artifact, the full prompt I used to build it, and instructions for adapting that prompt to your own company by the end of this weekend.

How the practice actually works

The signal was never the bottleneck. It’s been sitting in plain sight for years on Reddit, on G2 and TrustRadius, in every podcast transcript your buyers actually listen to. The bottleneck has always been synthesis, the analyst hours required to pull thousands of public mentions into a coherent narrative a CMO can act on. That layer is what a build in Lovable now eats.

If anything, the work matters more than it used to because category lines are being redrawn faster than any annual planning cycle can track. The difference is that one person with the right prompt can now produce something better than what a research firm would have shipped, in a weekend for thirty dollars instead of two months for sixty thousand.

Brand and PMM teams get something specific out of this practice that other functions don’t.

First, the deliverable is alive. It’s a URL, not a PDF, and you re-run the synthesis whenever you want. The narrative refreshes with the market because there is no static deck to update.

Second, the output is grounded in verbatim evidence. Every claim sits next to a sentence someone actually wrote on a public forum, with attribution and a source link. That changes how the conversation goes. You stop arguing positioning from gut, and senior stakeholders engage differently when the evidence is on the screen instead of in your head.

Third, you own the artifact. You’re not renting it from a firm, waiting on a contract, or hoping the renewal gets approved next year. It belongs to your team forever, and it gets sharper every time you run it.

The Clay Narrative OS

I picked Clay as the worked example because of the public data depth I mentioned above, and because it’s a company most PMM and Brand operators reading this will already have an opinion on. Easier to test the artifact against your own gut when the subject is familiar.

The Clay Narrative OS scraped 1,247 public sources, pulled 564 verbatim quotes, scored eight competitors across five dimensions, mapped sentiment to nine themes, and surfaced three positioning gaps that were not on my radar going into the build. Total build time was about 3 hours of actual work spread across a weekend, with around thirty dollars in API spend.

The OS has five screens, and each one does a specific job in the analysis. The structure matters as much as the data, because the screens compound. The Briefing forces the synthesis to commit to a thesis. The Market Moment grounds that thesis in category context. The Competitive Landscape shows where the subject sits relative to alternatives. The Customer Voice provides the evidence layer. The Strategic Implications convert all of it into the moves you can actually run on Monday morning.

The Briefing

What this section is for: to force the synthesis to commit to three claims the rest of the OS has to support. If your research doesn’t end in a clear point of view, you don’t have research, you have a literature review.

The Briefing opens with three headline findings the synthesis surfaced. These were not findings I went looking for. I wrote the prompt, ran the synthesis, and read what came out. The OS pulled these directly from the clustered evidence:

1. Clay is bought as a system, not a database. Buyers frame the purchase as enrichment-plus-orchestration. Apollo is bought as a database. That distinction is the entire competitive wedge.

2. The onboarding tax is real and underdiscussed. It’s the single most consistent negative theme in the dataset. Median time-to-full-ROI mentioned in the reviews is around six weeks.

3. Community advocacy is the moat competitors cannot copy. The Clay Slack, the template library, and the certification program get referenced in customer language more often than the product itself.

The 4-up stat row at the top (sources analyzed, quotes surfaced, net sentiment score, top competitor by share of voice) gives the reader a one-glance sense of the dataset’s depth before they read a single finding. The “Run new synthesis” button at the bottom is the architectural point of the whole OS, because the artifact is alive instead of frozen.

The Market Moment

What this section is for: to set the category context so the subject’s positioning gets judged against the right backdrop. Most positioning work fails because it analyzes the company in isolation. The Market Moment fixes that by forcing the analysis to name the forces reshaping the category before it touches the company itself.

Four sourced stats on the GTM data category establish that the ground is moving. Three forces reshaping the category (deliverability collapse, signal-based selling becoming table stakes, RevOps shifting from cost center to revenue lever) name the actual movement underneath the noise. The synthesized pull-quote that closes the screen is “Clay is not winning the data war. It is changing what the war is about.” That sentence did not come from me. The clustering surfaced it, and it does more work in one line than most positioning decks do in twenty pages.

The Competitive Landscape

What this section is for: to remove the option of arguing positioning in a vacuum. Buyers compare, boards compare, investors compare. The Competitive Landscape forces the analysis to do the same in three different formats so no one in the room can claim the comparison wasn’t structured.

The 2D positioning map plots Clay against Apollo, Smartlead, Instantly, Clearbit, ZoomInfo, Common Room, Outreach, and Salesloft. Two axes (point tool to platform, self-serve to enterprise) define the category space, and the dots are sized by mention volume. The scoring table runs across five dimensions (data breadth, workflow power, ease of use, price perception, community advocacy) and uses synthesized signal weights from the dataset. The three head-to-head comparison cards (Clay vs Apollo, Clay vs Smartlead, Clay vs ZoomInfo) hold the verbatim quotes that justify each claim, so a skeptical reader can drop into the evidence from the map and the table on demand.

The Customer Voice

What this section is for: to put the evidence layer on the screen. This is what turns the OS from “interesting deck” into “irrefutable artifact.” Every claim earlier in the OS is anchored to quotes that live here, with attribution and source links. If a stakeholder challenges a finding, you scroll here. Most board rooms have never seen a positioning argument that holds up under that pressure.

The sentiment heatmap maps nine themes (pricing, onboarding, data quality, integrations, workflow power, support, ROI, ease of use, scale) against five sentiment buckets, with every cell sourced. Twelve verbatim quotes carousel across the top with full attribution. Below them sit nine theme deep-dives, each with three example quotes and a sentiment ratio bar showing the positive-to-negative split. The reason the heatmap is the most screenshot-worthy artifact of the entire OS is that it compresses thousands of public mentions into a single visual a CMO can scan in ten seconds.

The Strategic Implications

What this section is for: to convert analysis into moves. Without this screen the OS is interesting but inert. With it, you walk out of the read with a brief for what your team should do next quarter.

Three positioning gaps name where the subject is under-credited in the market relative to what the data shows. Three messaging opportunities pull verbatim phrases buyers already use that the subject isn’t echoing back. Three competitive plays prescribe specific moves against the three main competitors, each with a two-sentence rationale and a supporting quote. The closing pull-quote is “ZoomInfo is the incumbent we are paid to replace. Clay is the tool we use to replace it. The narrative is already written.” Someone in the dataset said something close to that, and the clustering pulled it through as the closing argument. That single line is the entire competitive thesis, surfaced not written.

The architecture under the design

Perplexity scrapes the sources in parallel, one call per source type, returning verbatim quotes with attribution. Claude synthesizes everything into a JSON payload that follows a schema defined in the prompt. Supabase persists the results and runs the API calls through edge functions so the keys never touch the client. End-to-end run takes under two minutes once it’s wired up.

I’m not a developer and haven’t written code in over a decade. Lovable scaffolded the entire app from one initial prompt and a few rounds of iteration after that.

Where this earns its keep

This isn’t a one-time party trick. It’s a new tool slot in how Brand and PMM teams operate, and here’s where it pays for itself in the first week.

Board decks. When the board asks where the moat is, you don’t say “community advocacy.” You show them the heatmap, quote the buyer who said it, and hand them the live URL. The conversation changes because the evidence is no longer abstract. Same dynamic for any board question about competitive position, pricing perception, category framing, or churn drivers. The room engages differently when the evidence is on the screen.

Internal presentations. Sales kickoffs, all-hands meetings, QBRs, leadership offsites. The artifact lands anywhere the company gathers and needs a shared view of the market. Project the top twelve verbatim quotes on the wall and the whole company starts speaking buyer language instead of internal language. I’ve watched CROs change how they open the next QBR after sitting with one of these for ten minutes. It reframes what the team thinks it’s actually selling.

Roadmaps. The onboarding tax finding the OS surfaced for Clay didn’t come from anyone’s gut. It came from forty reviews saying the same thing. Take a finding like that to product and the prioritization conversation changes shape in real time, because product can see the volume of the complaint instead of hearing it once secondhand from sales.

Interview prep. This is the one I’d quietly tell anyone going after a senior PMM or CMO role. I built a version of this OS for a CMO interview process I went through earlier this year at a B2B SaaS company. I walked into the final round with a synthesized narrative on the company, its competitors, and the exact language its buyers were using in the wild. The interviewers asked me how I built it, I told them, and I sent them the URL after the conversation. It was the most prepared I have ever been for a senior interview. I got the offer but ended up walking from it for unrelated reasons, but the artifact changed the conversation while I was in it.

Messaging tests. Before you ship new positioning, run the OS. Does the market already use the language you’re about to claim? If yes, you’re echoing back, which is what you want. If no, you have an uphill battle ahead of you, and you should know that before the campaign budget is committed.

Important: The displacement window

Most competitive displacement runs on stale inputs. The win/loss program reports once a quarter. The battlecard gets updated when someone remembers to update it. By the time the deck reflects a competitor’s weakness, the weakness is three months old and the competitor has had a full quarter to patch the narrative around it.

The OS collapses that delay to days. You re-run the synthesis on a schedule. When a competitor stumbles, the signal shows up in the heatmap before it shows up anywhere else you’d look. A price increase, a botched release, a support backlog, a security incident, a founder departure the forums are picking apart. Each of those opens a window where the competitor’s own customers are saying out loud, in public, why they’re unhappy and what they’d switch for. The window stays open for a few weeks and then it closes, because the competitor fixes the issue or the thread cools and the buyers move on.

Catching the window the week it opens is the entire game.

Here is the sequence I’d run. Re-run the synthesis weekly. Watch the negative cells in the heatmap for a competitor you’re targeting. When a cell spikes, drop into the quotes and confirm it holds across two runs, so you’re acting on a real shift and not one loud thread. Pull the verbatim phrases buyers are using. Then move on three fronts while the window is open. Hand sales the exact language so the talk track lands the day a prospect is already feeling the pain. Stand up a comparison page that mirrors the complaint back in the buyer’s words instead of your marketing’s words. Point outbound at the segment complaining loudest, timed near the competitor’s renewal window when switching cost is lowest.

Take ZoomInfo, since it’s the incumbent in the Clay set. The recurring complaint in the data is contract lock-in and price at renewal. That part is not a secret. What the OS gives you is the moment the complaint volume spikes, the phrases buyers use when it does, and the segments where it’s loudest right now. You’re not running a generic “switch from ZoomInfo” campaign that’s been live for a year. You’re running the play that matches what their customers are upset about this week, in the words they’re using this week.

The competitor cannot respond at your speed. Their win/loss program still reports quarterly. You’re on a weekly clock against an opponent on a quarterly one. That gap is the displacement.

The honest limits

A few things to know before you put one of these in front of an executive.

It’s directional, not statistically valid. Public data has selection bias. Loud users post more than satisfied users, and churned customers leave louder reviews than active ones. You’re seeing a slice of the truth, not the whole truth, and you should say so when you present it.

You still need real customer conversations. The OS surfaces patterns. Conversations tell you what the patterns mean and which patterns matter most for the decision in front of you. The OS is upstream of the customer interview, not a replacement for it.

The competitive scoring is synthesized signal weights, not benchmarks. Treat the numbers as sentiment proxies. Don’t put a synthesized 84/100 in front of a board and call it a Gartner score.

Real-time means real noise. If you re-run the synthesis on a day when one negative thread is going viral, the sentiment heatmap will tilt with it. Run it weekly, not daily, and look at trend rather than a single snapshot.

Public data is not all data. Your CRM, your call recordings, your closed-lost notes, your renewal survey responses are all richer than anything on Reddit. Wiring those in is the next move and the architecture above supports it. That’s a follow-up post.

The point

The research firm just got Napsterized, and the eight-week internal sprint went with it. What replaced both is a one-person operating practice that costs almost nothing, ships in days, and refreshes whenever you want.

The work Brand and PMM exists to do hasn’t gotten easier. The cost and time required to do it well has collapsed. The teams that internalize this first will buy themselves two or three quarters of compounding advantage before their competitors notice the playbook changed. The rest will keep paying firms forty to a hundred thousand dollars for a PDF that goes stale before it ships.

Play with the Clay Narrative OS at if you haven’t already. The full prompt is below, versioning it for your own company is about an hour of editing, and the math is in your favor on every dimension that matters.

Build one this week. Send me what your OS surfaced about your category that you didn’t already know. I read every reply.

The full prompt I used

This is the part most posts about AI tools never give you. Here is the full prompt I pasted into Lovable as the first message to scaffold the Clay Narrative OS. It defines the design system, the data model, the five screens, the API integrations, the synthesis logic, the voice rules, and the lockdown. Adapt the four sections I’ll call out below the prompt and it becomes yours.

Build me a private, single-operator market intelligence app called Clay Narrative OS. It is a synthesized competitive and customer-sentiment narrative for Clay (clay.com, the GTM data orchestration and workflow automation platform), generated by analyzing thousands of public mentions across Reddit, G2, Capterra, podcast transcripts, LinkedIn, Hacker News, newsletters, and comparison content. This is an internal research artifact, not a marketing site. It must never be indexed by search engines and should sit behind a lightweight passcode gate.

THE FEELING I WANT

Premium operator dashboard. Dark, editorial, calm, expensive. The reference points are a Bloomberg terminal crossed with a modern research firm’s interior dashboard: near-black canvas, one confident electric chartreuse accent, sharp typographic hierarchy, lots of negative space, subtle depth, smooth motion. It should feel like a tool a CMO is proud to project on a boardroom screen. Avoid the default AI-generated look: no purple gradients, no generic rounded-everything cards, no emoji in the UI, no clip-art icons, no bubbly drop shadows. Specifically no Clay-style purple anywhere. This is our analysis of Clay, not Clay’s brand.

DESIGN SYSTEM (use these exact tokens)

Colors

- Canvas: #0A0B0D

- Surface 1: #131418

- Surface 2: #1B1D22

- Border: #2A2D34

- Text primary: #F4F5F7

- Text secondary: #9CA0AB

- Text tertiary: #5E626D

- Accent: #C8FF3D (electric chartreuse, use sparingly)

- Accent dim: #8DB52A

- Positive sentiment: #6BE3A0 (mint)

- Negative sentiment: #FF6F5E (coral)

- Neutral / mixed: #FFD45E (warm amber)

Typography

- Display: Space Grotesk, weights 500 / 600 / 700

- Body: Inter, weights 400 / 500 / 600

- Mono (numbers, citations, timestamps): JetBrains Mono, weights 400 / 500

- Type scale: 12, 14, 16, 18, 24, 32, 48, 64

Layout

- Max content width 1280px, centered

- 96px between top-level sections

- Cards have 1px solid border, no drop shadow, optional 2px accent rail on left for emphasis

- Radius: 6px on cards, 4px on inputs, 999px on pills

Motion

- All transitions 200ms cubic-bezier(0.2, 0.8, 0.2, 1)

- Fade-in-up on scroll via Framer Motion, 24px translate

- No bouncy spring animations

- Page transitions: 400ms cross-fade with slight slide

- Number counters animate up on scroll for stat blocks

No-go list

- No emoji in the UI

- No clip-art icons (Lucide outline icons only, 1.5 stroke)

- No drop shadows (use borders for depth)

- No purple or violet anywhere

- No gradients except one optional subtle radial behind the hero

- No fully rounded cards, no playful microinteractions

TECH STACK

- Vite + React + TypeScript

- Tailwind for utility styling

- Framer Motion for transitions

- Supabase for persistence and edge functions

- Lucide React for icons

- Recharts for data viz

API INTEGRATIONS

Prompt me for three API keys before any live scraping:

1. Perplexity API (primary intelligence workhorse)

2. Anthropic API (Claude for synthesis, model claude-sonnet-4-6)

3. Optional Firecrawl or Apify (only if direct scraping of G2 pages is required)

Every API call must be proxied through Supabase edge functions. Keys never appear in client code. If a call fails, surface a clean inline error inside the relevant card and do not break the page.

DATA SOURCES TO SCRAPE, IN PRIORITY ORDER

1. Reddit via Perplexity queries: r/sales, r/SaaSMarketing, r/marketing, r/SaaS, r/B2BSaaS, r/RevOps, r/coldemail, r/Entrepreneur, r/Startups

2. G2 reviews and comparison pages

3. Capterra and TrustRadius reviews

4. Podcast transcripts: 20VC, Lenny’s Podcast, GTMnow, Topline, SaaStr Podcast, RevOps Co-op, The Marketing Millennials

5. LinkedIn ambient public discussion (public posts and threads only, no private data)

6. Hacker News threads

7. Operator newsletters: Lenny’s Newsletter, Stacked Marketer, Demand Curve, Pavilion, GTM Partners

8. Comparison content (vs pages, alternative listicles, head-to-head reviews)

THE SUBJECT AND COMPETITIVE SET

Subject: Clay (clay.com). GTM data orchestration and workflow automation. Lets revenue teams enrich, dedupe, and orchestrate outbound across hundreds of data sources.

Competitors:

- Apollo.io (volume and coverage)

- Smartlead (outbound execution and deliverability)

- Instantly (deliverability and volume)

- Clearbit, now part of HubSpot (enrichment heritage)

- ZoomInfo (enterprise data incumbent)

- Common Room (community and signals)

- Outreach and Salesloft (sequencing platforms with enrichment bolted on)

- LeadMagic, Findymail, Datagma (point tools at the data layer)

DATA MODEL

type Source = {

id: string

type: ‘reddit’ | ‘g2’ | ‘capterra’ | ‘trustradius’ | ‘podcast’ | ‘linkedin’ | ‘hn’ | ‘newsletter’ | ‘comparison’

url: string

author: string | null

date: string

raw_text: string

retrieved_at: string

}

type Quote = {

id: string

source_id: string

text: string

sentiment: ‘very_negative’ | ‘negative’ | ‘mixed’ | ‘positive’ | ‘very_positive’

themes: string[]

competitor_mentioned: string | null

attribution: ‘verified’ | ‘synthesized’

}

type CompetitorScore = {

competitor: string

data_breadth: number

workflow_power: number

ease_of_use: number

price_perception: number

community_advocacy: number

overall: number

source_quote_ids: string[]

}

type Synthesis = {

id: string

run_at: string

source_count: number

quote_count: number

payload: object

}

BUILD THESE FIVE SCREENS, IN THIS ORDER

1. The Briefing (landing and command center)

Eyebrow at top: CLAY NARRATIVE OS in accent chartreuse, mono, 12px, uppercase. Below it, headline in Space Grotesk 600 at 64px: “What the market is actually saying about Clay.” Subhead at 18px in text-secondary: “Synthesized from [N] public sources across Reddit, G2, podcasts, and community forums. Last run [timestamp].”

Below the hero, a 4-up stat row in JetBrains Mono:

- Sources analyzed

- Quotes surfaced

- Net sentiment score (positive minus negative, out of 100)

- Top competitor by share of voice

Below the stat row, a “What we found” section with three large stacked cards. Each card carries a step number (01, 02, 03), a one-line claim in display 32px, a two-sentence elaboration, and one verbatim supporting quote with attribution chip. The first card carries an accent rail.

At the bottom, a primary button “Run new synthesis” and a secondary “View raw sources.” Show last run timestamp.

2. The Market Moment

Three modules:

- Why this category matters now: a 2x2 stat grid with sourced numbers about the category. Each stat carries a source citation in mono 12px.

- The three forces reshaping the category: three vertical cards in a row, each with a force name, two-sentence explanation, and one-line implication in accent color.

- Where the subject sits: a single paragraph synthesis with one pull-quote in accent color, large.

3. The Competitive Landscape

Three modules:

- Module A: A 2D scatter plot in Recharts. X axis: “Point tool” to “Platform.” Y axis: “Self-serve” to “Enterprise.” Each competitor is a dot sized by mention volume. The subject is highlighted in accent chartreuse with a label. On hover, show competitor name, total mentions, net sentiment.

- Module B: A scoring table with rows per competitor and columns for the five dimensions plus an overall. Use thin horizontal bars in chartreuse with the score number in mono. Subject row has an accent rail. Footnote: “Scores synthesized from clustered signal weights across ingested sources. Directional, not benchmarked.”

- Module C: Three side-by-side comparison cards: subject vs main competitor 1, subject vs main competitor 2, subject vs main competitor 3. Each card has two text logos, three rows (”Where subject wins,” “Where the competitor wins,” “Where they tie”), and one representative verbatim quote underneath.

4. The Customer Voice

Three modules:

- Sentiment heatmap: rows are themes (pricing, onboarding, data quality, integrations, workflow power, support, ROI, ease of use, scale), columns are sentiment buckets. Each cell shows a count in mono, shaded by intensity using a coral-to-chartreuse gradient. Cells are clickable, opening a modal with every quote in that cell.

- Top quotes carousel: a horizontally scrolling row of quote cards with quote in display 24px, attribution beneath (source type, author handle, date, link icon), sentiment pill, theme tags. 12 quotes default with “Show all” expand.

- Theme breakdown: vertical stack of theme sections. Each has theme name, one-line summary, three example verbatim quotes, and a horizontal sentiment ratio bar.

Every quote shows its source as a chip beneath it. Verified verbatim quotes get a small chartreuse checkmark. Synthesized quotes get a SYNTHESIZED FROM CLUSTERED LANGUAGE eyebrow tag and no checkmark. Never invent attributions.

5. Strategic Implications

- Three positioning gaps: each with a name, description, and one-line “what to do about it.”

- Three messaging opportunities: phrases buyers actually use, pulled verbatim from the dataset.

- Three competitive plays: specific moves against the three main competitors. Each with a two-sentence rationale and one supporting quote.

- One closing pull-quote: the single most clarifying verbatim quote from the dataset, displayed large in accent color, full-bleed.

At the very bottom: “Last synthesized [date]. Re-run from the Briefing screen.” in mono 12px.

THE SYNTHESIS ENGINE

When the user clicks “Run new synthesis,” a Supabase edge function runs the following pipeline.

Step 1: Parallel scraping via Perplexity. Fire eight Perplexity API calls in parallel using these query templates.

Reddit query: “Search Reddit for the 50 most recent and substantive discussions about [SUBJECT] (the [CATEGORY] tool). For each discussion, extract direct user quotes about pricing, complexity, data quality, integrations, ROI, and direct comparisons to [COMPETITOR LIST]. Return verbatim quotes with username, subreddit, and link. Do not paraphrase. Do not summarize. Return raw text only.”

G2 query: “Find the most recent and informative G2 reviews of [SUBJECT]. Extract 20 quotes covering both positive and negative experiences. Include reviewer role, company size, and review date. Return verbatim text only, no summary.”

Capterra / TrustRadius query: “Find recent Capterra and TrustRadius reviews of [SUBJECT]. Extract 15 most informative verbatim quotes with reviewer context.”

Podcast query: “Search podcast transcripts for substantive discussion of [SUBJECT] in the last 18 months. Prioritize [PODCAST LIST]. Extract direct operator commentary about how the product is used, who uses it, what works, what fails. Include episode title and approximate timestamp.”

LinkedIn query: “Search LinkedIn for public posts and public comment threads discussing [SUBJECT] in the last 6 months. Focus on posts by [TARGET AUDIENCE]. Extract verbatim quotes about deployment, ROI, and comparison to alternatives. Do not access private data.”

Hacker News query: “Search Hacker News for threads mentioning [SUBJECT]. Extract substantive technical and commercial commentary verbatim.”

Comparison content query: “Find comparison content (vs pages, alternative listicles, head-to-head reviews) covering [SUBJECT] against [COMPETITOR LIST]. Extract specific claims and data points verbatim, with source URL.”

Newsletter query: “Find operator newsletter mentions of [SUBJECT] in the last 12 months. Focus on [NEWSLETTER LIST]. Extract substantive analysis verbatim with publication and date.”

Store every returned source row in the sources table with its raw text.

Step 2: Synthesis via Claude. Pass all raw extracted text to Claude (claude-sonnet-4-6) in a single call with this synthesis prompt:

“You are a senior PMM analyst synthesizing public market intelligence about [SUBJECT]. You have been given raw extracted quotes and discussion threads from Reddit, G2, Capterra, podcasts, LinkedIn, Hacker News, comparison content, and operator newsletters. Synthesize this into a structured narrative analysis.

Return only JSON. No prose, no markdown fences. The JSON must follow this shape exactly:

{

briefing: { headline_findings: [3 cards with claim, elaboration, supporting_quote_id] },

market_moment: { category_stats: [4 stats with source], forces: [3 forces], position_paragraph: string, position_pullquote: string },

competitive: { scores: [CompetitorScore array], positioning_map: [...], head_to_head: { subject_vs_competitor1, subject_vs_competitor2, subject_vs_competitor3 } },

customer_voice: { heatmap: { themes, sentiments, cells }, top_quotes: [12 verbatim], themes: [9 themes] },

strategic: { positioning_gaps: [3], messaging_opportunities: [3], competitive_plays: [3], closing_quote_id: string }

}

Voice rules: No em dashes anywhere. Short declarative sentences. Operator-to-operator tone. No consulting jargon (no leverage, unlock, paradigm, best-in-class, thought leader, synergy, robust). No marketing words (no revolutionary, game-changing, transformative). Proof before explanation. Every numeric claim needs a source. Every quote shown must be verbatim with attribution. If you must synthesize a representative quote, mark its attribution as ‘synthesized’ rather than ‘verified.’

Be honest. If the subject has clear weaknesses, name them. If competitors have clear advantages, name them. This is for an operator making positioning decisions.”

Step 3: Persist the synthesis JSON to the synthesis table with a timestamp. The UI always reads from the most recent synthesis row.

VOICE AND COPY RULES

These apply to every string in the UI:

- No em dashes anywhere

- Short declarative sentences

- Banned words: leverage, unlock, paradigm, holistic, best-in-class, world-class, thought leader, ecosystem, synergy, robust, revolutionary, game-changing, transformative, cutting-edge, next-generation

- No emoji

- Proof before explanation

- Use numbers when available

- “Synthesized from clustered language” label on non-verbatim content

LOCKDOWN

- robots.txt with Disallow: / for every user agent

- noindex, nofollow meta tags on every page

- No social meta tags

- Passcode gate on entry, default passcode “narrative-os-2026,” validation through a Supabase edge function so the passcode is never in client code

- All API calls proxied through Supabase edge functions, keys never client-side

- No public share functionality

BUILD ORDER

1. Scaffold the five screens with placeholder synthesis JSON so design is locked first

2. Build the passcode gate and lockdown

3. Build the Supabase edge function for Perplexity scraping

4. Build the Supabase edge function for Claude synthesis

5. Wire the “Run new synthesis” button to the pipeline

6. Persist synthesis runs in Supabase with timestamps

7. Polish motion, micro-typography, and the heatmap last

Ask me for the three API keys when you need them. Confirm the Supabase project setup before any live scraping. Build the design and empty state first, then the data layer. Make it beautiful before it works.

How to version this for your own company

The prompt is structured so adapting it is a find-and-replace job, not a rewrite. Four sections need to change.

The subject is the obvious one. Replace every reference to “Clay” or “Clay (clay.com)” or “Clay Narrative OS” with your company name and URL. The naming convention I use is [Company] Narrative OS, but you can call it whatever you want.

The competitive set is the second swap. The prompt lists eight competitors specific to the GTM data category. Replace them with the seven or eight competitors that actually matter in your category. Keep the list to seven or eight, because fewer makes the positioning map look empty and more makes it unreadable. Order matters too. Put your two or three most important head-to-head competitors first, because those drive the comparison cards on the Competitive Landscape screen.

The data sources are the third swap. The Reddit subreddit list is GTM-tilted right now. If you’re in a different category, replace those with the subreddits where your buyers actually hang out. Same goes for podcasts and newsletters. If you sell to engineers, the relevant properties are The Changelog and Software Engineering Daily, not Lenny’s or 20VC. Pick the seven or eight your buyers actually consume.

The visual identity is the fourth swap. I used electric chartreuse for the Clay OS because I didn’t want it to look like Clay’s brand. If you’re building one of these for your own company instead of as outside analysis, you might want it to feel on-brand. Swap the accent color, the typography stack, or both. Keep the dark canvas though, because it reads as serious and analytical, and it makes the heatmap pop.

Once those four swaps are done, paste it into Lovable as the first message. The first version will be 80% of the way there. You’ll iterate from there with two or three follow-up prompts, mostly to fix things you only notice after the data actually scrapes.

The poster, who goes by westernarian, disarmed the easy rebuttal first. They are not a luddite. They build workflows, like systems, and have shipped their share of automations. The worry was sharper than “I don’t understand the tools.” It was this: “Marketing is becoming a ROI center and im not sure thats even marketing anymore.” Underneath it, quieter, the line that earned the upvotes: “Some of it should probably stay messy. Some of it should probably stay human.”

They’re right about what to be afraid of but wrong about what it means. The distance between those two is the whole debate, and the industry is about to get this exact question wrong in his exact way.

The room split in an hour

Scroll the thread and you can watch marketing argue with itself in real time.

One camp came to bury the old job. “The future of marketing is filled with engineers who are building and managing teams of agents,” wrote BigPersia, who figured the creatives “will get pushed out in the short term if they can’t adapt.” A commenter posting as three_s-works was honest about the incentive: “As a marketer that came up through engineering, I’m for it, selfishly.” The mood is simple. The train is leaving.

The other camp came to a funeral. The single most upvoted comment in the entire thread was not bullish. It came from a marketer posting as Prettylittlelioness, watching two former clients get taken over by operators who treat marketing as a machine. Her word for the campaigns that resulted: “bloodless.” Her diagnosis of the era: “we overcorrected from being vibes led to turning into a math class.” A twenty-year creative director, posting as Top-Establishment918, said it shorter. “Now all I do is prompt all day. Feels lazy.” And the line that got the OP to reply “Great line!” came from UnoMaconheiro: “we didnt make marketing smarter we just made it easier to make spreadsheets about it.”

Both camps are loud. Both have a point. Both are answering the wrong question.

The reframe a stranger got right

The question everyone is fighting about is binary. Is marketing becoming engineering, yes or no, pick a team.

Dozens of comments deep, a poster called Nexio_10 quietly fixed it and almost nobody noticed. “I don’t think marketing is becoming engineering. I think the execution layer is becoming engineering while the strategy layer becomes even more important.” Then the line that should have closed the thread: “The tools are getting commoditized. Good judgment isn’t.”

That is the answer. The execution layer of marketing is becoming engineering. That is a different sentence with a different conclusion bolted to it.

The work is splitting into two layers. The bottom layer is execution. Building the pipeline, cleaning the data, running the agent ecosystem, shipping the dashboard, wiring the attribution, winning the citation. That layer is being engineered, fast, and it is not going back. The top layer is judgment. Knowing what to build, what the work should say, which bet is worth taking, which output is sharp and which is filler. That layer is not being engineered. It is becoming the only scarce thing in the building.

When the bottom layer commoditizes, the top layer does not lose value. It absorbs all of it. The bulls think the engineer eats the creative. The mourners think the creative is already dead. They are watching the same event, the floor dropping out from under execution, and drawing opposite conclusions, when the real conclusion is that a point of view just became the most valuable asset a marketer can hold.

Why this happened now, and why it is not reversible

The reason execution turned into engineering is not that a vendor wished it into being. It is that the channels marketers leaned on for a decade compressed at the same moment, and the layer that replaced them is machine-readable in a way the old web never was.

Answer engines do not reward “good content.” ChatGPT, Perplexity, Claude, and Google’s AI Overviews extract entities, weigh source authority, and match structured knowledge against a query before a human ever sees a result. Winning a citation in that environment is an engineering problem and a judgment problem at the same time. You need the structured, retrievable substance and a point of view worth citing. One without the other loses.

This is the highest-leverage thing a marketing engineer builds, and it is brand new. It did not exist as a discipline three years ago. At Webflow my team built first-party systems to own this layer in the CMS category and watched answer share cross 60 percent with a swing of more than 300 incremental AI citations. That is not a content calendar. That is not “a marketer who learned SQL.” It is a new category of work, and you cannot optimize a citation you cannot see, which is the entire reason a measurement layer like Profound exists.

What the role actually is

A marketing engineer owns output, not pipes. They identify the problem, build the thing, ship it, measure it, kill it or scale it, alone, end to end, then decide what it should have said in the first place.

A few weeks back, in the playbook for hiring this role, I used one example to make the “alone, end to end” part concrete. At Webflow I wanted to go deeper in the freelancer community. Between days stacked with calls, I built a handful of agents that scraped the freelancer and web-dev subreddits, surfaced real-time pain, shaped a narrative and an offer around it, activated across channels, measured, and refined. One person running a cross-functional acquisition program in days, not quarters. No PM, no brief, no ticket sitting in someone else’s backlog. Fitting, given where this piece started, that the raw material was a stack of subreddits, the same place this whole debate is now playing out.

That last move, deciding what the work should say, is the job. A marketing ops manager keeps the systems clean so campaigns can run. Necessary, valuable, not this. A skeptic in the thread, b2b_framework_guy, made the cheap-version case well: “most of the people calling themselves marketing engineers are just marketers who learned sql. its not that deep.” For a lot of people wearing the badge, he is right. That describes the floor, not the ceiling. Learning SQL gets you into ops. Knowing which of a hundred possible builds will move the number, and which will rot in three months without a human watching it, is the part you cannot learn in a weekend. That part is the role.

Here is the ceiling, written by someone who has never read a word of this. This week the Director, Marketing Strategy & Ops at Figma posted a req for a Marketing Engineer. The mandate: make the marketing org disproportionately faster with AI. Find the most valuable problems hiding inside manual work. Build agents and workflows that ship. Delete work that no longer needs a human. The line that gives it away: the person he wants gets annoyed at a repetitive process because they can already picture the system that should replace it. That is not “learned SQL.” That is the role, written by a company that does not sell the tool, in language I never handed them.

Three questions I keep getting asked

Since I published that playbook, I have had somewhere north of fifty messages about it, from founders, from CMOs, and from people already doing this job inside companies. The questions were not the part that stayed with me. The notes that stayed with me came from the practitioners, each writing a version of the same sentence: that is what I do, and I finally have a name for it. That is how you know a role is real. Skeptics argue about a title. The people living it exhale, because someone finally described their actual week.

I have been calling this shift the Great Convergence. The technical marketers are being pulled toward creativity. The creative marketers are being pulled toward systems. The two halves the org chart kept in separate rooms are collapsing into one person, because the best operators stopped honoring that wall years ago. The marketing engineer is the name for the person standing in the middle of it. Profound put that name on the role, and they put it on correctly, which almost never happens in a category that ships a new title every quarter. They saw the loop running inside their sharpest customers before the rest of the market had a word for it, which is why they are the ones who ended up naming it.

The OP’s fears live inside the questions, though, so here are the three sharpest, anonymized, answered straight.

“Isn’t this just a vendor’s invented title? Profound named it because they want you to buy Profound.”

A founder asked me this, skeptical. He had the OP’s best argument, which is genuinely good. westernarian described the playbook exactly: “Invent the title, write the manifesto, get a few high signal people to wear it as a badge, sell the tool the title basically forces you to adopt.”

He is right about the mechanism. I will not pretend otherwise, and you should distrust anyone who does. He is wrong about the conclusion. A go-to-market move and a real shift in the work are not mutually exclusive. The vendors are selling the shift because the shift is happening. When Profound’s team presented the role at SEOweek, the framing, relayed by a commenter who was in the room, was not “fire your creatives.” It was that the marketing engineer takes direction from the marketing leader and the creative call stays human, while “teams are definitely going to be smaller, with a focus on high performing teams.” That is not hype. That is your org chart in eighteen months. The cynicism about the label is fair. Letting it talk you out of seeing the thing the label points at is how you lose two years.

One req is an anecdote. Here is the market. Look at the titles and you would think these are different jobs. Read the reqs and they are one job. Figma calls it Marketing Engineer. Ramp calls it a Vibe Growth Marketing Manager. Cloudflare files it under Marketing Engineering. The title is still settling. The role already showed up. And the demand shows up before the reqs even post. Pull Google Trends yourself. “What is a marketing engineer” and “marketing engineer jobs” are both breakout terms in the last month.

That is Google’s own data, not a vendor’s slide. A vendor can name a title. A vendor cannot make companies with no stake in the tool write the req, or make strangers start searching for the job by name.

Let me say plainly where I land because you should always know where the writer is standing. I am all the way in on this role. Not because backing it is the fashionable move this quarter, but because I have already built this way myself, and because the people operating this way now are not theorizing, they are shipping, and they are starting to find each other. Last week Profound hosted the first-ever Marketing Engineering hackathon. It ran five times oversubscribed, packed with people who read the role description and recognized their own week in it, then showed up to build in public because somebody had finally made a room for the work they had been doing alone, which is more than any other hackathon can say right now. That is not a title hunting for adherents. It is adherents who finally got a title. The label is a go-to-market move. The work underneath it is the realest thing happening in marketing right now. Call the role a fiction if you want. The waitlist disagrees. The burden is on you to explain the people already living it, and I have yet to hear a version of that argument that survives ten minutes with one of them.

“I’m a creative marketer. I can’t code, and I don’t want to. Is my career over?”

This is the question under the entire thread. It is what “feels lazy” meant. It is what “how many of us are even going to survive this transition” meant. It deserves a real answer.

No, and it is closer to the reverse. The scarce input is now the judgment that decides what to build, and that judgment is creative. Knowing which agent moves the number is taste. Knowing what the work should say so a stranger who has never heard of your company stops scrolling is narrative instinct. None of it comes from the stack. The career that is ending is not the creative’s. It is the creative’s who refuses to touch the tools at all, and the builder’s who can ship anything and decide nothing. The first becomes slow. The second becomes an expensive intern who ships a hundred things no one uses. The job is both halves in one head, and the creative half has to lead. If you have taste and you learn enough of the tools to be dangerous, you are not getting washed. You are the person the role was invented for.

“If everything has to prove a return, doesn’t the bold work die in the planning meeting?”

This is the OP’s strongest point and I will not wave it off. He named the failure mode precisely: “the weird bold stuff dies in the planning phase because nobody can prove in advance that it’ll work.”

That death is real, and it is a management failure, not a property of the role. A commenter named buttonMashr99 wrote the fix without dressing it up: keep early exploration messy and close to the customer, then systematize only what shows repeatable traction. You do not run the bold bet through an attribution model before it exists. You protect a lane where work is allowed to be unmeasured, you let the idea live long enough to throw a signal, and then you bring the engineering in to scale what worked. Teams that demand proof before the experiment had that problem in 2015 with a spreadsheet. The tools just let them do it faster. The role does not kill the bold work. A leader with no nerve does, and now has better software to do it with.

The tell hiding in the thread

Here is the part that should end the argument, sitting in the comments where no one assembled it.

The thread is about whether marketing is becoming a machine that scales the average. The thread is also full of comments that were obviously written by AI, getting downvoted on sight, by a crowd that smelled them instantly.

One commenter posted a long, fluent, structurally flawless reply. The top response was not a rebuttal. It was “I LOVE HOW obviously written by AI that comment is.” Another reader nailed the fingerprint: “the ‘it’s not just x, it’s y’ sentences.” The poster, caught, laughed it off: “Nah nah my mate Claude wrote it.” Further down, no mercy: “Use your own words and formulations.. hate this ai shit.”

A commenter named Valuable-Cap-3357 had already explained why, a hundred comments before it happened. The default output of these tools, he wrote, “isn’t bad, it’s average,” and the engineering layer “doesn’t fix that; it just scales the average.” The thread then proved his thesis on itself, in public, in real time. The average got posted. The humans flagged it and moved on.

Now the part nobody in the thread wanted to say.

A share of the grief in that thread is not about losing creativity. It is about losing the place that the absence of creativity used to hide. For twenty years, execution volume was cover. You could fill a week with campaign ops, channel management, reporting, and QA, and never once be required to have a point of view, because the busywork read as value. Automate the busywork and the cover is gone. What is left on the table is the single question the machine cannot answer for you: do you have something to say. The crowd in that thread answered it instantly for every AI comment that didn’t. The machine did not kill the point of view. It revealed who never had one.

That is also the good news, if you have one. Taste was always the moat. It just used to be optional. Now it is the whole job.

The ruling

Give the OP his warning, because it is real. Build systems for the sake of systems, optimize for the dashboard instead of the customer, run every bold idea through attribution before it can breathe, and you get exactly the bloodless marketing he fears. That outcome is avoidable. It is a choice, not a destiny, and treating it as destiny is how good people talk themselves into sitting this one out.

The bulls had the timeline right. This is coming, it is not optional, and the teams that move first run smaller, ship more, and open a lead that does not close cheaply. By 2027 this is a standard role. By 2028 it is a team. The companies hiring the first one now will have the second and third in seat before everyone else writes the req.

But the binary both camps fought over was the wrong fight, and the stranger dozens of comments deep had it right. Marketing is not becoming engineering. The execution layer is becoming engineering, and that is the best thing to happen to creative marketers in years, including the ones currently mourning. When the machine absorbs the average, judgment becomes the scarcest and most defensible asset in the function. The marketing engineer is the person who runs that trade on purpose. Hired for the tools, you get the bloodless machine. Hired for the taste, you get the opposite: the operator who hands every repeatable, measurable task to the machines so every human hour left goes to the work no model can do, which is the exact work the OP is fighting to protect. The engineering does not cost you the creativity. It buys the creativity its time back, then scales whatever you brought to begin with.

The part the mourners need straight: the marketers getting washed out are not the ones who refused to become engineers. They are the ones who spent a career mistaking motion for a point of view, and just lost the place that hid it.

So if you run a marketing team, stop debating the title and make the hire. Pay on the senior IC band, because the people worth hiring are choosing between you and a senior engineering offer, not a marketing one. Point them at the layer where your buyers now ask their questions, the answer engines, and hand them the one input the job runs on: a current read on what those engines say about you and your competitors. You cannot win a citation you cannot see. The teams already ahead are watching that share in Profound and shipping against the gaps every week, which is the whole loop the role exists to run.

Have something to say. Then build the machine that says it everywhere your buyers are looking, all at once. In that order, never the reverse. That is the whole job now. Done right, human marketing does not end here. It gets the most room it has had in a decade, finally carried by a machine big enough to keep up with it.

On May 7, ChatGPT started hyperlinking brand names straight to company homepages. Plain bold text became a clickable link. Referral traffic to monitored brands jumped roughly 60% overnight and held.

Most people read that as a traffic story. Based on the calendar, I’m reading it as an ad-tech story.

What actually happened

Start with the size of it, because the size is what makes this real instead of anecdotal.

In Profound’s monitored set, daily OpenAI referrals roughly doubled starting May 7 and stayed there. Homepage share of those referrals went from around 3.5% to around 24% in a week. Brand front doors went from a rounding error to one in four referral clicks.

This is not one dataset talking to itself. Similarweb’s clickstream panel measured total ChatGPT referrals up about 158% week over week, with homepage traffic up 355%. Qwairy studied more than 140,000 responses and found inline brand links jumped from 0.4% of answers to 6.2% in a single day. Three independent vantage points. One event.

Josh Blyskal and the Profound team caught it in the wild first (props). One of the most important observations in AI search this year.

Real, attributable brand clicks where there used to be almost none. If that were the whole story, it would be a good one.

It is not the whole story.

Now look at the calendar

May 5: OpenAI opened its self-serve Ads Manager. CPC bidding. A conversions API. No minimum spend.

May 7: brand hyperlinks rolled out.

Two days. You do not need a leaked roadmap to read that sequence. A relevance-weighted CPC auction needs one thing above all else: a clean record of what people click. The branded link update is how OpenAI started collecting it, at scale, across every brand and every query at once.

Every clickable mention now throws off a signal. Which brand got clicked. On which question. In which context. At what rate against the other brands in the same answer.

That is the raw material of an ad ranking system.

Why the clicks matter more than the traffic

Google’s ad machine was never built on impressions. It was built on learning which results earn clicks.

CTR is not a reporting metric there. It is the input that decides what gets shown, where, and at what price. Impressions were the warm up. Clicks were the unlock.

OpenAI is running the same play in compressed time, and it is not hiding it. The Ads Manager that went live on May 5 runs a relevance-weighted second-price auction. That is the published mechanism. It means a tighter, more relevant ad on a smaller bid beats a lazy ad with a bigger budget. Relevance gets weighted. To weight relevance, you have to know what people actually click. The branded link update is the meter that reads it.

So the two events are one event. The traffic win is real. It is also the byproduct. The product is the click data itself.

Who won, who sat out, and why

The category split makes sense the moment you stop counting traffic and start reading intent. In Profound’s data, B2B SaaS referrals jumped more than 200%. Fintech rose around 60%. E-commerce stayed roughly flat.

ChatGPT is minting clicks in the categories where people ask for a company by name. Best tool for. Alternatives to. Who should I use for. Purchases with a transactional path route through the shopping surface instead, so retail sits the link auction out for now.

The brand auction is getting built first. If yours is a category where buyers ask ChatGPT for a name, you are already in the training set whether you planned to be or not.

Three moves while the window is open

The free-traffic window is open right now. Treat it like inventory you can earn before you have to rent it.

Make ChatGPT a named channel in your reporting. Add chatgpt.com and openai.com as referral sources today. Google made this easier on May 13 by adding a native AI Assistant channel to GA4 that classifies ChatGPT, Gemini, and Claude traffic with no custom filters. One caveat. A large share of AI visits arrive from mobile apps that strip the referrer and land in your direct bucket, so the reported number undercounts the real one. Tag what you can. Assume there is more.

Treat your homepage like a cold landing page. One in four AI clicks now lands on your front door from someone who knows nothing but your name. They arrive with the one sentence ChatGPT wrote about you and about five seconds of patience. If they cannot tell what you do and who it is for in that window, you earned the click and handed it right back.

Win share of answer while the clicks are still earned. This is the Answer Ownership move. Every click you earn organically today is a data point in the auction that prices your ads tomorrow. The brands that own the answer now set the floor everyone else has to outbid later. The ones that wait will pay to buy back visibility they could have had for free.

The part that should make you uncomfortable

OpenAI pulled in over $100 million from a six-week pilot before any of this went self-serve. The stated target is $2.5 billion this year and $100 billion by 2030.

Those numbers do not come from showing ads. They come from pricing them well. And pricing them well requires exactly the click data the May 7 update just switched on.

You are not the customer of that system yet. Right now you are the free traffic. Every click you earn today becomes the case study OpenAI uses to price your ads tomorrow.

The window where AI mentions are earned, attributable, and free does not stay open. Own the answer while it is still organic.

Great work to Josh Blyskal and the Profound team. This is the most important shift in the category today.

This is a real step, and I want to say that plainly before I get into the parts everyone is going to skim past. We have wanted first-party AI visibility data since AI Overviews started eating the top of the page. Now a slice of it exists, inside the tool we already trust for organic, and attributed to Google itself.

Then you read the help documentation, and the report quietly tells you what Google is willing to measure and what it is not. The second list is longer, and a lot more interesting.

Start with one sentence

Skip the launch bullets. The line that matters is in the help doc, in the definition of an impression. Google counts an impression as how often “links to your site were shown” inside a generative AI feature on Search.

Read that twice. The unit is a link shown, not your content used. That distinction is the entire AEO game, and Google just drew a hard line straight through the middle of it.

AI Overviews and AI Mode do two different things with your pages. Sometimes they surface a link, a citation chip a user can click. Often they pull a sentence, a stat, a definition off your page to ground the answer and never surface a link at all. The first is citation. The second is influence. AEO has always been about both, and you can argue the second matters more, because being the source the model trusts is more durable than being one of six links in a carousel.

This report sees the first and is blind to the second. When your content shapes an answer without earning a visible link, your impression count does not move. So the number in front of you is not “how much am I showing up in AI,” it is “how often did Google decide to link me.” Those are very different questions, and treating them as one is the first mistake people will make with this data.

One tell that Google knows the difference: its companion blocking control (more on that below) lets a site opt out of being used “as links or for grounding purposes.” Google separates the two when it suits them. The report only reports one of them.

The pros spotted both problems within a day

Watch who said what under Google’s announcement, because the split is the real signal. The “finally, thank you” replies landed fast. So did sharper reads from people who do this for a living, and they converged on the same two complaints.

The first is granularity. The report blends AI Overviews and AI Mode into one impressions bucket. Giorgio Taverniti, who wrote the book on liquid search, did not soften it: aggregating AI Overviews and AI Mode, and shipping nothing on grounding queries, is the kind of choice you make when you have drifted away from the people working in the field. Rebekah Logan made the operator’s version of the same point. Citation behavior in AI Overviews versus AI Mode can differ wildly, so one blended number hides the thing you most want to know, which surface is citing you and which is not. Simone De Palma called the aggregated sub-menu “data engineering 101” and noted that Bing at least ships query-level data from its engine.

The second is the grounding gap, the same one sitting inside the impression definition. Jayme Welch put it cleanly: this is a measure of visibility, not understanding, and it tells you nothing about how often AI features used your content without a citation. Radu Stoian and Yongjoon Yang both asked for query fan-out, what Bing calls grounding queries. That request is going to define the next year of this conversation, and Google shipped without it on purpose.

Notice the divide. The loudest praise was excitement that the report exists at all. The most specific reactions came from the people who pressure-test AI visibility for a living, and they found the same two holes I did, inside a day. Excitement that a thing exists is not evidence the thing is good.

The missing column is not an oversight

There are no clicks. No click-through rate, no average position, no query data. Just impressions, sliced by page, country, device (Search only), and date, down to hourly granularity, with data that appears to begin around May 18.

Barry Schwartz asked Google directly about clicks and got the line you would expect: they are working with site owners on what is helpful and will add metrics over time. Maybe. But I would not hold your breath for clicks, and not because instrumenting them is hard.

A clicks column would quantify the one thing Google has the least incentive to quantify: how much traffic the AI answer keeps for itself. The whole economic logic of AI Overviews is to resolve the query on the page so the user never needs to leave. A per-page, exportable click number sitting in everyone’s Search Console would put a hard figure on that cannibalization. “Additional metrics over time” is a real roadmap. Clicks are the last room in the house they unlock.

So treat the impressions number as a denominator, not an answer. It can show you the shape of your AI visibility and how it moves. It cannot tell you whether AI search sent you a single visitor. For that you still need your server logs and your analytics, and you still need to do the matching yourself.

Why the UK got it first (not for the reason you think)

The rollout is a small subset of UK site owners, reportedly limited to .co.uk properties at first, ahead of a global expansion with no date attached. The instinct is to read that as a routine phased launch. It is not.

The UK went first because the Competition and Markets Authority is forcing the issue. The CMA’s remedies require Google to give publishers genuine control over how their content feeds AI, including the ability to opt out of having it used to fine-tune models, and Google has roughly nine months to roll that out across the UK. The legally mandated piece is the controls. The report is not itself required by the CMA, but it shipped in the same market on the same day, which is not a coincidence. When a regulator makes you hand publishers an opt-out, you want to be able to point to a reporting product launched alongside it and call the whole thing a publisher win.

That reframes the timeline for the rest of us. The pace of access is being set by antitrust pressure in the UK and the EU, not by Google’s eagerness to hand operators better data. If you are in the US waiting for this, you are waiting on regulators in another jurisdiction, not on a product roadmap you can lobby.

Read the rollout cynically and you land somewhere uncomfortable. This looks timed to coincide with the CMA controls and scoped to do just enough to keep regulators satisfied. And notice the detail that should bother you most: the data starts in May 2026, with no history before it. So you cannot line up a decline in your own clicks against the rise of AI Overviews, even if you wanted to. Convenient.

The sleeper launch is the toggle, not the report

Shipped the same day, and arguably more consequential: a control that lets a site opt out of appearing in AI Overviews, AI Mode, and generative features in Discover, both as links and as grounding. Opt out and you get no impressions and no traffic from those surfaces. Google says it will not use the choice as a ranking signal in core Search, so in theory you can leave AI without leaving Search. For the test cohort it takes effect June 17.

In an earlier poll, about a third of SEOs said they would block Google’s AI features. I think almost none of them should, and I suspect almost none of them will once the toggle is real and the thing they would be zeroing out is their own impression count. The honest use of this control is narrow: publishers whose entire business is the click and who have decided AI exposure is pure leakage. For nearly anyone in B2B or considered purchases, AI surfaces are now part of how a buyer builds a shortlist before they ever search your brand. Opting out to protect a click you were probably going to lose anyway is a bad trade. It is still good that the lever exists, because consent should not require a robots.txt hack.

What to actually do with it on Monday

A few concrete moves, whether you have access now or are planning for when you do.

Build the page-level citation inventory. The Pages view is the most useful tab here. It tells you exactly which URLs Google is willing to link inside an AI answer, which is your current answer-worthy set in Google’s eyes. Put it next to the pages you assume are your strongest answer assets. The gap between those two lists is your roadmap, and closing it is a production problem more than an analysis one. This is where a content engine like AirOps earns its place, because turning a list of under-cited topics into answer-shaped pages at real volume is the work, and doing it by hand does not scale to the surface area AI search now covers.

Use impressions as answer-share input, not a traffic proxy. Track the trend and the page mix, watch the country and device splits (AI behavior diverges on both), and kill the reflex to convert impressions into sessions. They are not sessions, and a chart that implies they are will get you in trouble in a board meeting. Plenty of operators in that LinkedIn thread were already drafting the client line, “your content showed up 9,000 times in AI answers this week,” and that is a fair story to tell as long as you are honest that it is a visibility story and not a value one. The moment it gets dressed up as traffic, you have lied to a client with Google’s logo on the slide.

Triangulate, do not switch. This does not replace a cross-engine AI visibility platform like Profound, and anyone telling you GSC makes one redundant has not understood either tool. They are not measuring the same thing. Profound tracks your share of voice and citations across the engines that actually carry the conversations, ChatGPT, Perplexity, Gemini in its own app, Copilot, while GSC reports Google’s own count of links displayed on Google’s own surfaces. Those numbers will not line up, and they are not supposed to. One is a cross-engine market-share lens, the other is a single-vendor, single-metric ground truth. Read them together. GSC becomes the first-party Google reference point you anchor to, and the platform stays the only thing giving you the rest of the map, which is most of it, because Google is one engine among several and AI search is not happening only inside Google.

Keep an eye on what Bing already shows. Bing’s AI performance report has been global for a while and exposes more signal than Google’s, including citations and grounding queries. No clicks there either, but the contrast is the point: Bing surfaces the grounding-level data Google is choosing to withhold. If you operate at any scale, treat Bing’s view as a useful second instrument, not because its volume matters, but because it sees the half of the picture Google won’t show you.

The measurement gap is half-closed

Here is the state of play. We went from no first-party AI data to a real, Google-attributed view of where our links land in AI answers. That is progress and I am glad to have it.

But the half that is still missing, clicks and queries and grounding without a link, is exactly the half that would settle the argument everyone is actually having: is AI search a traffic apocalypse or a new front door. Impressions tell you that you are in the room. They do not tell you whether anyone walked through the door, what they asked to get there, or how often your words ended up in the answer with your name stripped off.

We got a better altimeter this week. We still have no airspeed indicator. Plan accordingly, and read the fine print before you put any of it in a deck.

One is from recent Google’s generative-AI guidance. It puts llms.txt on the list of things you can stop fussing over, next to a reminder that you do not need special files or markup to appear in AI search. The other is from Chrome’s Lighthouse documentation, published within days of the first. It calls llms.txt an emerging convention for LLMs and agents and notes that without it, agents may spend more time crawling a site to work out its structure.

So one team tells you the file does nothing, and another team ships an audit that checks whether you have it. Same logo, same month. Read only the headlines and it looks like a company arguing with itself.

But…the two statements describe two different jobs. The conversation online went sideways because we keep folding both jobs into one flat word, “AI,” and then act surprised when the advice points in two directions.

Here is the distinction that settles it.

Discovery and functionality are not the same job

When put the question to John Mueller (Google) directly on Bluesky, his answer was the most useful thing said on the topic all month, and I would read the whole thread before forming a take.

The short version: the llms.txt files sitting on Google’s own developer properties are not there for search. Mueller split the question into discovery and functionality. Discovery is getting found by a global search engine. Functionality is what happens after the find, when a person or an agent is already on your page and trying to finish something. He compared it to a call to action. You do not add a CTA to rank. You add it because once someone arrives, you want them to convert. Both matter to whoever owns the site’s outcomes. They are simply different problems that call for different tools.

llms.txt lives on the functionality side. It was never a discovery lever, which is exactly why a search team can tell you to ignore it for rankings while a browser team finds it mildly useful for agents mid-task. Both statements are true because they are answering different questions.

Mueller went further, and this is the part most of the hot takes skipped. The place llms.txt actually pulls weight today is developer documentation read by AI coding tools. Handing a coding assistant a simplified, low-token version of a reference page can make it faster and more accurate. He was careful to frame it as a stopgap, since those tools read HTML perfectly well on their own. For anything that is not developer docs, he was blunt: it does not make much sense yet, agentic traffic is still a trickle (his advice was to go check your own logs), and a Markdown copy of a shoe’s spec sheet is not going to sell more shoes. His closing line is the one I keep coming back to. Prioritize needs before dreams.

What the broader data says

This is where the marketing internet and the measurable reality stop agreeing.

SE Ranking analyzed roughly 300,000 domains and found no relationship between having an llms.txt file and how often a domain showed up in AI answers. When they removed the file from their model entirely, the model got more accurate, not less. On the consumption side, one log study tracking AI crawlers across ninety days found that out of more than half a billion bot visits, only a few hundred touched llms.txt at all. Adoption sits somewhere around ten percent of sites and climbing, which gives you the real shape of this thing. A lot of people are creating the file, and almost nothing is reading it.

That gap, high creation and near-zero consumption, is the whole story. We are writing a file for an audience that has not shown up.

What I found when I ran it

I do not like arguing about this from the sidelines and I’ve tested this nearly two dozen times in the last 6 months.

Across the sites and pages where I ran before-and-after measurement, I saw no movement I could honestly attribute to the file. No lift in AI citations, no change in how answer engines pulled from the pages, nothing that separated the test set from the control beyond ordinary noise.

I want to be precise about what that does and does not mean. It does not mean the file is harmful. It is cheap to publish and it will not hurt you. It means that if you are shipping llms.txt expecting to appear more often in AI Overviews, ChatGPT, or Perplexity, the expectation is the problem, not your implementation. I wish it were that easy…

Where it actually earns its place

There is a clean rule hiding in all of this.

If you publish developer documentation, ship llms.txt. Coding agents genuinely use it, it is inexpensive to maintain, and the token savings are real. This is the one case where the file does a job nobody else is doing.

If you do not publish developer docs, treat it as optional. Worth knowing that the Lighthouse audit marks the file Not Applicable when it is missing rather than failing you, because providing it is still optional. An audit checkbox is not the same as a result.

The argument worth having instead

The reason the llms.txt fight wears on me is that it is pulling attention away from the part of that same Lighthouse release that actually matters.

The llms.txt check is the least important item in Chrome’s new agentic browsing category. The heavier signals in that category decide whether an agent can read and operate your site at all: a clean accessibility tree, layout that does not shift under an agent’s feet, semantic HTML, and WebMCP, the emerging standard that lets a site expose its functions to an agent directly instead of forcing the agent to guess from a screenshot. WebMCP is heading into a public origin trial in Chrome 149. That is the work that will separate sites an agent can use from sites that merely exist near it.

So when the agentic web does arrive in volume, and the logs say it has not yet, the sites that win will not be the ones that wrote the best text file. They will be the ones an agent can actually navigate and act on.

Discovery is one job. Functionality is another. Spend your scarce hours on the one that is real today, and build the structural pieces that will still matter when the rest catches up.

Needs before dreams.

About StackedGTM

StackedGTM is a media and intelligence platform for go-to-market in the AI era, written by Josh Grant. It’s read by founders, CMOs, and the growth and marketing operators at companies like Anthropic, OpenAI, Stripe, Rippling, Cursor, Webflow, and hundreds more. No BS, no fluff, just deep insights and how-to frameworks on what’s happening in AI GTM.

The newsletter is supported by Profound, the platform serious brands use to measure and act on how they show up across AI search, from tracking citations to running agents against the AEO workflow. That support keeps it independent and free to read. The editorial calls are mine, including the skeptical ones above, and any piece paid for directly is labeled at the top. This one is not.

If you want an operator’s read on where GTM is heading, subscribe.

“How are we actually doing in AI search compared to our competitors?”

Most arguments about AEO measurement get framed as a data problem but they’re really not. They are a market structure problem, and until this week the channel was missing the one piece of infrastructure every other legitimate marketing channel has.

Consider what makes a performance channel legit inside an enterprise. Not volume. Not even attribution, which most channels have only partially solved. The thing that matters is price discovery, a mechanism by which the market tells you what your performance is worth relative to everyone else trying to do the same thing.

AEO will never have a literal price the way paid does. That is not the point. Price discovery in market structure terms is about comparative valuation, not dollar amounts.

SEO has it. The entire channel is built on it. You cannot rank in position three without two URLs sitting above you, and Google’s results page is a daily, public, brutally honest auction of relative quality. Position is comparative by definition. There is no such thing as a good ranking in a vacuum.

Paid search has it. Auction data, CPC benchmarks, impression share, quality scores. You know what a click is worth because the market sets the price every time you bid.

Brand has it (even if imperfectly). Share of voice, unaided recall, brand tracking studies. Signals that are noisy but real. A marketing leader knows roughly where the company sits.

Even email, the oldest channel in the stack, has open and click rate benchmarks granular enough to be useful.

AEO has had none of this. For nearly two years the channel has run on absolute counts. I have sat in rooms where one site’s monthly citation count was treated as a triumph, and rooms where another site’s count, many times larger, was treated as a disappointment. In neither case did anyone actually know which read was correct. This is not because the people working in the channel are unsophisticated. It is because the data to answer the question correctly did not exist outside any one platform’s walls.

What looked like a measurement problem was actually a structural one. You cannot have a legitimate channel without comparative valuation. You can have activity, you can have spend, you can have dashboards that look the part. But until participants can see where their performance sits in a real distribution, the channel is running on faith, and budgets allocated on faith get cut first when the cycle turns. I am already watching that happen, unfortunately.

What benchmarking infrastructure in AEO actually looks like

This week Profound shipped Benchmarking in Agent Analytics, and it is the first credible answer to this problem the category has produced. They built the missing layer, the one that lets the channel calibrate itself against itself.

The mechanics are straightforward. Profound compares your AI citation performance against more than 100,000 pages tracked across the Profound Network, filterable by industry and company size, refreshed weekly. I have been a Profound customer long enough to remember when a cross-network view like this was technically impossible. That it is now table stakes tells you how fast this category is moving. Every page lands in one of four tiers, Poor, Fair, Good, or Great, based on where it falls in the percentile distribution. You see your site’s overall tier at a glance, and you can click into any page to see exactly where it sits in the network.

That is the description. Here is what it actually does.

It turns “we got cited 32,000 times” into “we are in the 73rd percentile for our industry and company size, with seventeen pages in the bottom quartile dragging the site average down.” Those two sentences are not the same. Not in category, not in usefulness, not in what they let you do on Monday morning.

The page-level view is the part I did not expect to matter most but I think it does. Site-level benchmarks are useful for executive conversations. Page-level benchmarks are what let content teams act. When every cited page on your site is ranked against peers, the bottom-tier pages stop being a guess about where to focus. They become a worklist.

There is a quiet design choice worth flagging. Pages need at least five AI citations to be included, and any tier segment with fewer than twenty sites is suppressed. The team understood that benchmarks at small sample sizes are worse than no benchmarks, because they give false precision. The thresholds are conservative enough that what you see can be trusted.

The part most people will get wrong

The first-order effect of this shift is obvious. Marketing leaders can finally answer the board question with a percentile instead of a screenshot. That is real and it matters, but it is the least interesting consequence.

Here are three things I expect over the next twelve to eighteen months that most teams have not priced in yet.

Budgets will get defensible. A percentile is something a CFO can argue with. A citation count is not. When AEO investment can be defended against a distribution (”we are in the 40th percentile, here is what it costs to move to the 70th”), the channel graduates from experimental line item to forecastable spend. That is the transition every emerging channel has to make, and most stall there for years. AEO might compress that timeline considerably. This is timely given I’m already companies who invested early in AEO start to look back and see if the return was worth it with skepticism on the investment.

Enterprise content teams will shrink, not grow. This is the prediction I am most confident in and the one most teams will resist. Most content orgs have been hiring against a “more content” thesis for the AEO era. The data does not support that thesis once you can see the distribution.

A fintech I advise pulled their AI citation data by page. Their top 14 pages were driving 61% of their AI citations. The bottom 180 pages were driving 9%. I want to say that out loud one more time, because I do not think most people have internalized what a Pareto distribution looks like when it shows up in their own content org. Fourteen pages. Sixty one percent. One hundred and eighty pages. Nine percent.

They did not have benchmarking when they built that long tail, so they had been investing in it on instinct for nearly 18 months, assuming volume would eventually compound. The distribution said the opposite. Kill half the bottom tier, take the saved budget, and triple down on the fourteen pages already doing the work. Most content orgs are sitting on a version of this same distribution and do not know it yet.

Once page-level percentiles exist, the math is unavoidable. The shrinking is what everyone will fixate on. It is the least interesting part. The real shift is in what the job becomes. The skill that compounds in the AEO era is not the ability to produce more content. It is the ability to architect workflows that account for performance across every discovery channel at once. That is the Marketing Engineer, the role I wrote about recently. The companies that see this first will rebuild the content org around a few people who think in systems instead of output. The companies that don’t will keep hiring against a volume thesis the data has already killed. I have watched this exact pattern run twice in SEO over the last decade. The people who read it early built careers on it. The people who read it late are still writing the brief they wrote in 2019.

The information asymmetry between participants and non-participants will widen fast. Benchmarks compound. The more sites in the network, the sharper every comparison gets, and the harder it becomes for anyone outside the network to credibly claim they know their position. Six months from now, the gap between teams who can answer “where do we rank” with a percentile and teams who can only answer with a screenshot will be the difference between teams that get budget and teams that lose it.

What this means for the category

The category is too young for any one platform to have won anything yet. The next eighteen months will be a real fight, and right now Profound is out in front of it.

The company that ships the benchmarking layer first usually holds a structural advantage in the period that follows, because comparative valuation is itself a network effect. Every additional site in the Profound Network sharpens every benchmark in it. Every sharpened benchmark makes the platform more valuable to be measured by. Every increment of value makes the next customer easier to win. It is not a moat that closes quickly. It is one that compounds, and the compounding starts now. Props to Profound for shaping this and finding a way to make the data they’re collecting valuable to the entire industry.

We have been calling AEO a channel for 18 months. Some of us have been calling it that slightly longer, and feeling slightly stupid about it every time the data part of the conversation came up. A channel without price discovery is a behavior. A channel with price discovery is an operating discipline.

Eli is the author of the book Product-Led SEO and for the last few years has worked as an SEO strategic consultant for some of the largest brands on the internet like LinkedIn, Tinder, Coinbase, Gusto, G2, and many others. He writes a weekly newsletter on the future of SEO and AEO at ProductLedSEO.com, and he advocates for putting the user first in any organic strategy, whether it’s designed for traditional search or new LLMs.

Eli sent me a line last week I haven’t been able to stop thinking about:

“For years I didn’t believe small and local businesses should have websites at all. They didn’t get any traffic. Now they feed the LLMs.”

Read it twice. That’s the entire shift in two sentences.

For a decade, the pages most B2B content teams built were optimized for one thing: aggregate organic traffic. Pages that didn’t drive sessions got deleted. Pages too narrow to rank for a head term never got written. The unranked, the specific, the weird. They got pruned out of every content portfolio I’ve ever audited.

Some of these pages LLMs cite…and cite well.

This isn’t a vibe. AirOps spent the last several months running fan-out analysis across tens of thousands of real ChatGPT responses. They decompose prompts into the sub-queries the model actually retrieves against, then score which pages get cited and which get walked past. It’s the deepest empirical dataset we have on any answer engine. The findings appear to hold directionally across Perplexity, Claude search, and Google AI Overviews based on what teams in the space are seeing but the specifics vary by system and the rest of this piece is grounded in the ChatGPT data unless noted.

The picture is unkind to almost every editorial instinct the SEO industry has spent fifteen years building.

The thesis in one sentence:

Ranking still matters. Everything you did to rank doesn’t.

The mechanism: page becomes passage

Google ranks URLs. Answer engines cite passages. That two-word swap is the whole shift and most of the AEO advice you’ve read is downstream of getting it right.

When ChatGPT answers a question, the model doesn’t fetch your page. It fetches a passage. The pattern across retrieval-augmented systems is broadly the same: pages get chunked into passage windows of a few hundred tokens, each chunk gets scored against the query (typically through some combination of embedding similarity and lexical match), and the top chunks get pulled into the model’s context. The exact pipeline inside ChatGPT, Perplexity, and AI Overviews isn’t fully public and the systems differ in important ways. The directional principle holds across all of them: the unit of value is the passage, not the URL.

Your page’s domain authority gets you indexed. Your page’s ranking gets you retrieved. AirOps found that the first returned result for a fan-out query gets cited 58.4% of the time versus 14.2% for the tenth result, so SEO is still a precondition. Once a chunk enters the retrieval pool, it’s on its own.

Authority becomes local. A single page can be cited for one sub-topic and invisible for the other eleven on the same URL. Page-level signals like internal linking, topical depth, length, and comprehensive coverage don’t transfer cleanly down to the chunk. Every word on the page that isn’t the answer becomes a cost. Each paragraph of adjacent context dilutes the chunk’s relevance to the query that retrieved it.

It’s probably worth naming this.

The Retrieval Tax (working name. If you've got something cooler, I'm listening…reply and tell me). Every word on a page that isn’t the answer to the question being retrieved is a tax on the answer’s relevance. Ultimate guides pay the highest retrieval tax in content marketing. Focused answer pages pay almost none.

The AirOps fan-out work measures the tax in citations.

Before the three findings: 95% of the surface is invisible to your keyword tool

One precondition fact. Nothing in the rest of this piece makes sense without it.

The fan-out queries ChatGPT generates when it builds an answer are largely invisible to traditional keyword tools. In the AirOps dataset analyzed by ALM Corp, 95% of fan-out queries had zero monthly search volume in conventional keyword tools. Ahrefs, Semrush, Google Keyword Planner. None of them can see these queries.

The surface of your category is bigger than your keyword tool can see and the invisible part is where citation is decided. If your content strategy is downstream of a keyword tool, you are optimizing for 5% of the actual retrieval surface, and not the 5% that matters most for AEO.

Everything below follows from this.

Finding 1: Domain authority is inversely correlated with citation

Inversely correlated, not uncorrelated. The numbers aren’t subtle.

In the AirOps fan-out dataset, pages that get cited consistently have an average DA of 53. Pages that never get cited have an average DA of 56. The backlink gap is sharper: always-cited pages have around 1.1M backlinks on average; never-cited pages have around 3.2M. A 3x inverse gap. At every level of query match, the lowest DA quartile in the dataset performs as well or better than the highest.

The site-type breakdown is even more telling. The five highest-DA categories in the dataset are YouTube (DA 100), Wikipedia (95), major news (94), Reddit (92), and health publishers (90). Effectively identical authority profiles. Their citation rates range from 2.4% to 59.2%. Twenty-fold difference at essentially identical DA. The signal is somewhere else.

LLMs don’t penalize high-DA pages. The mechanism is more boring than that. The editorial behaviors that maximize DA (comprehensiveness, hedging, generalism, optimizing for head terms) are the same behaviors that maximize the retrieval tax. High DA selects for content that pays the most retrieval tax. The correlation isn’t punitive. It falls out of the mechanism.

This is the finding that breaks the link-building model of enterprise SEO. Links still matter for ranking. They still matter for getting crawled, indexed, and retrieved. The link-building budget that won you DA is no longer the budget that wins you citation, and the reallocation question is genuinely uncomfortable for any team whose marketing org chart has been organized around DA for the last decade.

Eli’s small-business framing is the same point on a different timescale. The local plumber has a DA of 12. He’s not in the link-building game. He’s also written the single most specific page on the internet about a P-trap, because it’s the only thing he was qualified to write about. The model picks his page over Wikipedia’s, not despite his DA but in part because of what high DA correlates with at the page level.

Finding 2: Ultimate guides bury the answer

A 4,000-word ultimate guide is an SEO masterpiece. For passage retrieval, it is a disaster.

Here’s why. The retrieval system chunks the guide into passage windows. The user’s question is narrow. The chunk containing your answer also contains hundreds of words about tangentially related topics, so its relevance score against the specific fan-out query that retrieved it gets diluted by the surrounding noise. A competing page that runs 800 words on one question chunks into a single window whose content sits much closer to the query. The model picks it.

AirOps shows this empirically. When primary query relevance is high, focused pages around 800 words outperform comprehensive guides at 5,000+. SparkToro covered the same finding under the right headline: the death of the ultimate guide. That framing is correct. Not “the difficulty of the ultimate guide.” Not “the new constraints on the ultimate guide.” The death of it.

Most enterprise content portfolios have spent the last five years consolidating narrow pages into hub guides because hubs ranked. Atomization, the opposite move, is what wins for citation. If you’ve spent the last two years consolidating, you’ve spent the last two years building the wrong asset for what’s coming next.

That’s not a comfortable sentence. It’s the sentence the data forces.

Finding 3: 26 to 50 percent subtopic coverage beats 100 percent

This one gives you a planning heuristic, which makes it the most operationally useful finding in the AirOps work.

For any seed query, the fan-out process expands into a set of sub-questions the model uses to assemble its answer. You can score any candidate page on how much of that sub-question set it addresses and how strong its primary query match is. When primary match is strong, citation rate peaks at 26 to 50 percent sub-topic coverage, then falls off above it. Pages that try to cover everything get cited less than pages that own a defined slice.

A page that owns three sub-questions definitively gets cited three times. A page that touches twelve sub-questions superficially gets cited zero.

This retires the keyword map as a content planning artifact. In its place: a sub-question map. Enumerate the questions in your category. Pick the 26 to 50 percent slice where you have practitioner authority, proprietary data, or a differentiated point of view. Build one page per sub-question. Abandon the rest.

The pages you don’t write are as strategic as the ones you do. Probably more.

What Eli sees in client work

1) An ecommerce page that never received a lot of search traffic because it was on a highly competitive search term, but is now seeing outsized clicks because it is featured in LLMs.

2) Commoditized product review content that no longer receives many clicks despite high rankings in search, but it leads to brand mentions in LLMs that likely, but can’t be proven, drives brand traffic.

3) On a personal level, when prospective clients reach out and say they found him on an LLM, he always asks for the prompt, and typically, the source citation that led to an LLM mention is on a page that might never have seemed like a good source. Examples include podcast show notes pages, bios from many years ago at conferences, or media mentions.

4) A client whose primary product was classified listings rearchitected their AEO strategy to have their local classified pages be mentioned as a source for users to find. These pages never really ranked in organic search, but now they are far more relevant because they are mid-funnel for LLM users.

5) Companies that have specifically doubled down on LLM-only content, with the expectation that they were going to get cited, didn’t see the results they wanted because they didn’t think through their whole user journey.

The playbook, asymmetric on purpose

The to-do list isn’t subtle. It’s also not symmetric. One of these moves matters more than the rest.

The big one: atomize. Audit your top 25 pages by historical SEO performance. Identify every page that contains more than one answerable question. Most will contain five to fifteen. For each answer that earns its own page, spin it out to a dedicated URL whose title is the question. Keep the original as a hub if it earns its keep on rankings, but the answers themselves need to live alone, with their own chunks and their own retrieval surface. Atomization isn’t free. It reshuffles internal linking, redistributes authority across more URLs, and creates cannibalization risk if the new pages aren’t differentiated enough. For priority topics where citation share is the actual goal, the gains outweigh the costs. For your weak-performing long tail, leave the guide alone. This is the move that takes a quarter and pays for the next two years. Nothing else on this list matters as much.

Specialize. Pick the 26 to 50 percent of your category’s sub-questions you can own deeply. Abandon the rest.

The Deletion Audit. This is the inversion of every content audit you’ve ever run. Pull the list of pages your team has flagged for sunset: low traffic, narrow topic, weird angle, no internal links, dead in Google. Filter for the ones that are still factually current and well-written. Then ask one question of each: is this the best answer to a specific question on the internet? The ones where the answer is yes are not sunsets. They are your AEO portfolio, hiding in plain sight because the SEO audit framework you inherited can’t see them. Most teams will find five to twenty pages they were about to delete that are quietly the best assets in their portfolio. Stop deleting them. Start linking to them.

Title-Query Convergence. The cleanest unilateral move on the list. The ALM Corp analysis of AirOps’ data found pages with 50%+ title-to-query word overlap get cited at 20.1%. Pages below 10% overlap get cited at 9.3%. A 2.2x lift driven entirely by what’s in your title tag. The actionable version: on narrow answer pages, your title should be the question, not a clever framing of the question. “How do I revoke an API key” beats “The Complete Guide to API Key Management” every time on a single-question page. Hub pages and category pages need a different titling approach because they still have to serve broader keyword rankings, so this is a tactic for the atoms, not for everything. Most teams haven’t made the move on their atoms because clever titling is what content marketers were trained to do. Untrain it where it costs you citations.

Measure citation, not rank. Pick a citation tracking tool. Pick a category. Run weekly. If you’re not measuring share of cited sources across ChatGPT, Perplexity, Claude, Gemini, and Google AI Overviews, you are flying blind on the part of the funnel that will be load-bearing by Q4.

One honest caveat before the prediction

Retrieval algorithms move fast. The AirOps findings reflect how ChatGPT was building answers when the data was collected. Six months from now the specifics will shift. The optimal coverage band may move. The DA correlation may compress or invert further. The exact title-overlap thresholds will drift. The numbers are not laws.

The direction is. The shift from page to passage, from comprehensive to specific, from generalist to specialist, from keyword map to sub-question map. Every retrieval system that matters is moving toward more of all of these, not less. Anyone betting against atomization, specificity, or measurable citation share is betting against where every answer engine is heading. The numbers will move. The direction won’t.

What I’d put money on

By the end of 2026, the ultimate guide stops being the default unit of B2B content (slightly sad here as a fan/reader/writer of long-form content). The hub-and-spoke model doesn’t disappear but it loses its position as the answer to every brief. The platforms that sold teams on hub-and-spoke are already pivoting. The risk isn’t with them. The risk is with the thousand B2B content shops that built their playbooks on top of those platforms and haven’t pivoted yet. They will either rebuild around answer atoms or they will lose meaningful share in the AEO surface of their categories.

The model rewards specificity. The model penalizes comprehensiveness. The model is the distribution system now.

Eli’s plumber didn’t know any of this. He just wrote the most specific page on the internet about a P-trap because that’s the only thing he was qualified to write about. The model found him.

The question isn’t whether the plumber was right. The question is whether your team can deliberately reproduce, at scale, across an enterprise content portfolio, in the next two quarters, what he did by accident.

Josh Grant and Eli Schwartz

Josh Grant is the founder of StackedGTM.AI and writes the Weekly Operator Brief. Eli Schwartz is the author of Product-Led SEO. Subscribe to his newsletter to stay on the cutting edge of SEO & AEO productledseo.com.

Data in this piece is drawn from AirOps’ fan-out research (The Fan-Out Effect, The Long Tail), ALM Corp’s analysis of the AirOps dataset, and SparkToro’s coverage. The retrieval mechanism descriptions reflect the standard pattern across retrieval-augmented systems and are not specific assertions about any one platform’s pipeline.

This is one of the longest piece I’ve ever written. I usually believe brevity is the soul of wit. This one I went deep on because the topic earned it.

The model in my head was Sean Ellis. Hacking Growth changed how I thought about my career. I had the chance to write something with a few months back and that experience reset my bar for what a flagship piece should be.

Mid-April to today. Steal the prompts. Steal the frameworks. Build on top of them.

Read time: ~30 minutes. Save it. Share it. Come back to it.

Now let’s go.

Two years ago, the game was clear.

Own the blue links. Own the category keywords. Displace your competitors in paid and organic. If you could do that, you got the traffic, you built trust on your website, you ran a funnel with predictable data at every stage. Hard. But legible. You could see it. You could manage it.

That game is over. Not evolving. Not shifting. Over.

Discovery, trust, comparison, the entire early buyer journey, has moved into a single conversational window. Your buyer opens ChatGPT. Types something like “what’s the best demand gen platform for a Series B SaaS company.” Gets three names back in eight seconds. Closes the tab.

Your website never loaded. Your funnel never started. Your sales team never got the ping.

There is no analytics event for this. Sometimes you catch a referral from ChatGPT or Perplexity when someone clicks through. But the shortlist moment, when your brand is named, evaluated, and either recommended or skipped, happens before the click. Before the session. Before you ever know a buyer was looking.

The thing recommending vendors is not a smarter search engine. It is something different. It browses. It evaluates. It cross-references multiple sources before it answers. It hands a shortlist to a human buyer who mostly trusts it.

It is, in every practical sense, the first salesperson in every B2B deal happening right now.

You did not hire it. You cannot train it. You cannot fire it.

And it is already talking to your buyers.

I have been writing about AEO since before most people agreed on what to call it. I published The Definitive 2026 Guide to AEO when people were still arguing whether zero-click was real. The best thing we can do right now is build in the wild, share what we find, and push the industry forward together.

This is The Shortlist. Let’s go.

The Buyer You Have Never Met

94% of B2B buying groups rank their preferred vendors before they ever talk to a sales rep. 77% of the time, they buy from that day one favorite. Not the best demo. Not the smoothest negotiation. The vendor they preferred before your sales team picked up the phone.

That is from 6sense’s 2025 Buyer Experience Report. Nearly 4,000 buyers. Not a small sample. Not an outlier.

The deal is decided before your team enters the room.

Now add this. 94% of those buyers used large language models during the buying process. Nearly two thirds used tools like ChatGPT and Perplexity as much as or more than traditional search. 45% said AI was their primary research method for identifying suppliers. Not secondary. Not a double-check. Primary.

Buyers form their day one shortlist using AI. That shortlist predicts the winner 77% of the time. Your team has zero visibility into any of it.

It gets sharper. AI platforms do not return ten options and let buyers browse. According to BrightEdge and Amsive research across millions of AI responses, the average AI platform cites 3 to 4 brands per response. The top 20 domains capture 66% of all AI citations.

That concentration is brutal. In traditional search, ranking fifteenth still gets traffic. You can improve incrementally. In AI-mediated research, the math is binary. You are cited or you are not. There is no page two of an AI answer.

George Bonaci, VP of Growth at Ramp, put it bluntly when I interviewed him for StackedGTM.AI:

“In traditional search, position two still gets clicks. In AI answers, there’s often only one recommendation. The citation layer is a power law, and we’re in the land-grab phase right now. The alpha is now.”

The downstream impact is not small. Early data from Averi.ai suggests AI-referred traffic converts 4.4 to 5.6x the rate of organic search. I saw this firsthand at Webflow. AI-referred visitors converted 6x higher than non-brand organic. Buyers who arrive from AI citation are not browsing. They are deciding. They show up already knowing your category, your positioning, and roughly where you stand against your competitors.

The AI did the work.

The direction of travel is not subtle. Gartner projects 90% of B2B buying will be intermediated by AI agents by 2028, with $15 trillion in B2B spend flowing through those exchanges. Timeline will be messier than a clean number. Direction is not in question.

The buyer has changed. The research process has changed. Your funnel is not where deals are won or lost anymore. It is downstream of a decision the AI already made.

The question is whether you are on the list.

How the Shortlist Gets Built

80% of the URLs being cited by AI agents right now do not rank in Google’s top 100.

Not page two. Not page five. Not indexed at all in traditional search. The content building your buyer’s shortlist is almost entirely invisible to your SEO dashboard. These are two separate games. Most GTM teams are only playing one.

Here is what actually happens when a buyer asks ChatGPT, Perplexity, or Gemini to recommend vendors.

The AI is not crawling your website in real time. It pulls from two sources. First, parametric memory: everything baked into the model during training. Second, retrieval augmented generation (RAG): real-time pulls from external sources the model trusts at the moment of the query.

RAG-driven citations carry significantly more weight than anything from training memory.

The practical implication: what other people say about you, in real time, in places AI trusts, matters more than what your own website says about you.

82% of links cited by AI come from earned media. Over 95% from non-paid coverage. Muck Rack’s Generative Pulse study, over one million AI responses.

Your homepage is not building your shortlist position. Other people’s content about you is.

Recency tightens the screw. More than half of all AI citations come from sources published in the last 12 months. The highest citation rate hits within seven days of publication. A brand that goes quiet for a quarter does not just lose momentum. It actively loses ground as newer content about competitors fills the retrieval layer.

Consistency is structural. Not optional.

Now the part most GTM teams miss completely.

ChatGPT, Perplexity, and Gemini are not the same system. They have fundamentally different citation models. Optimizing for one while ignoring the others means you are structurally invisible to a significant chunk of your buyers.

Yext analyzed over 6.8 million citations across all three. SEMAI analyzed 25,000 URLs over 60 days. The data is consistent. The differences are stark.

Here is the platform breakdown in one place.

A few notes on the table that matter operationally.

ChatGPT is the platform your buyers used this morning. It is also the only platform that meaningfully cites LinkedIn posts. A mid-size B2B company publishing “47% of our 200-respondent survey preferred X” beats a high-authority site saying “many businesses prefer X.” Specificity beats domain authority.

I want to be honest about something. Six months ago, more than 90% of our AI referral traffic at Webflow came from ChatGPT. My instinct was to optimize for ChatGPT and let the rest follow. The volume gap felt rational. That instinct made sense then. It does not make sense now. Enterprise adoption of Claude is accelerating fast. Gemini’s referral traffic grew 388% year over year. Enterprise buyers especially are living inside Claude and Copilot, not ChatGPT. The one your buyer uses at work and the one they use at home are increasingly different.

You need presence across all of them.

Gemini grows fastest and rewards owned-domain structure. If your website is not built for machine extractability, you are invisible on it.

Perplexity is the most underestimated. It is the only platform actively citing comparison pages and solution-specific content at scale. It is also the most citation-transparent: every claim links to a source. Your citations need to be airtight. The teams winning Perplexity have a real Reddit presence, comparison content, and niche publication coverage. The teams losing Perplexity have none of the three.

Before you read another word of this guide, do this.

I built a tool that runs your brand against ChatGPT, Perplexity, Gemini, and Reddit in real time. The Zero-Click Funnel Mapper shows every buyer question in your category, every AI answer, every competitor mention, and exactly where you are invisible. 90 seconds. Most brands score under 30.

That number is not a vanity metric. It is a structural diagnosis of your shortlist position right now.

Run it here: Zero-Click Funnel Mapper.

Only 11% of domains are cited by both ChatGPT and Gemini. The overlap is smaller than almost anyone expects. Winning on one platform guarantees nothing on another.

There is a deeper problem most teams have not yet named. The vast majority of AI conversations happening about your category never surface in any tool you currently use. They happen inside ChatGPT, Claude, Perplexity, and Gemini sessions that produce no clickthrough, no referral, no analytics event. Profound calls these dark queries and surfaces them through Conversation Explorer, a panel of 400M+ real anonymized user prompts refreshed weekly, with intent, sentiment, and demographic breakdowns. The volume of dark queries in B2B categories typically dwarfs the volume of measurable clicks by 10x or more.

One more signal that reframes everything. Brand search volume has a 0.334 correlation with AI visibility, the strongest single predictor identified in recent research. Backlinks show a weak or neutral relationship. Models favor brands people already search for directly. The more people look you up, the more the model treats you as a default authority.

Brand investment is now AI visibility investment. The two are the same thing.

Knowing where you appear, where you do not, and how you are described when you do, that is the starting point for everything that follows.

Most teams cannot see it at all.

That is the real problem.

Social Is Not a Channel. It Is Your Retrieval Infrastructure.

48% of AI search citations come from community platforms.

Not from your website. Not from your blog. From Reddit threads, YouTube tutorials, and LinkedIn articles written by people who have nothing to do with your marketing team.

85% of brand mentions in AI answers originate from third-party pages, not owned domains. Independent analysis of over 5.5 million LLM responses. Your homepage, your product pages, your carefully crafted brand narrative, they are the minority voice in how AI systems understand and represent your company.

The majority voice is your community. Or your competitor’s community, if yours does not exist.

The brands that built genuine communities years ago accidentally built citation infrastructure. Webflow, Notion, Figma, and Linear all benefit from years of community content they never planned as AI training data. Now that conversation determines their AI shortlist position.

The brands on the other side are traditional enterprise software companies. Sales-led. Content gated. No Reddit presence. No YouTube ecosystem. No community to speak of. Starting from zero on the layer that matters most. The gap is widening every month.

This is why the push for community marketing is not a trend. It is a structural response to how AI retrieval works.

Now the data your GTM team needs.

Reddit is the single most cited domain across LLM responses at a 40.1% citation frequency. It beats Wikipedia, YouTube, and every major news outlet. Analysis of 150,000 citations across 5,000 keywords. Google paid $60 million per year to license Reddit’s data for AI training. That number tells you exactly how much Reddit content is worth as a retrieval signal.

Reddit is not uniform. 88% of Reddit citations come from category-level queries. When a buyer asks “what is the best demand gen platform,” Reddit is where the AI turns first. When they ask about a specific brand, one third of citations check features, one third ask how to use something, one quarter seek factual detail. Reddit is shaping your category positioning and your product perception simultaneously, in conversations you are probably not part of.

YouTube has overtaken Reddit as the most cited social platform overall. 16% of LLM answers versus Reddit’s 10%. YouTube is no longer a traffic channel. It is a citation engine. The transcripts, chapter markers, and descriptions create exactly the kind of extractable text AI agents pull into answers about how products work and which option is best.

LinkedIn shows up consistently across every platform studied. Top social source for Copilot, DeepSeek, and Meta AI. The format that gets cited is articles, not posts. A LinkedIn post disappears in 48 hours. A LinkedIn article with a clear argument, specific data, and a named author becomes a retrievable asset that compounds.

Run a Reddit audit this week. Do not do it manually. Use AI to do the heavy lifting.

Open Gumloop. Drop in their Reddit Scraping node. Zero setup. Point it at your three most relevant subreddits, pull the top 50 posts and comments from each, and feed the output directly into Claude with this prompt:

“You are a forensic competitive intelligence analyst building a B2B citation strategy from raw Reddit data. I am giving you the top 50 posts and comments from [subreddit name], [subreddit name], and [subreddit name]. For every vendor mentioned in [your category], extract: (1) frequency of mention, (2) the exact phrases buyers use to describe them, (3) sentiment (positive, negative, mixed) with the specific objection or praise driving it, (4) which buyer pain points trigger each mention, (5) which vendors are recommended together and which are positioned as alternatives. Then identify the five highest-value content gaps: specific buyer questions where no vendor is clearly winning the answer. For each gap give me the exact question phrasing, the format that would win it (comparison table, how-to, listicle, deep guide), the first paragraph of a genuinely useful answer, and the subreddit and thread to seed the conversation. Mirror the actual buyer language. Do not sanitize it. Return as a structured report by vendor with a separate gap section.”

What comes back is a real-time map of the citation content AI agents are using to build your buyer’s shortlist right now. Which brands own the conversation. How they are described. What language buyers use that you are probably not using in your own content. Where the gaps are that nobody is filling.

Run it monthly. Lighter version: paste five Reddit threads directly into Claude and run the same prompt. Twenty minutes. More signal than most tool dashboards.

What to do with this section.

Identify the three subreddits your buyers spend time in. For B2B SaaS, typically r/sales, r/marketing, r/entrepreneur, plus category-specific communities. Build genuine, consistent presence. The 95/5 rule applies. 95% value, 5% brand. Authentic participation over time is the only thing that works. Fake community presence gets called out and actively damages your AI citation position when negative sentiment surfaces.

Search “best [your category] platform” on YouTube. If your brand is not appearing in those videos or producing comparable content, you are invisible on the fastest-growing citation platform. Start one video series this quarter. Not a product demo. A genuine educational series that answers the questions your buyers are actually asking.

Convert your three strongest LinkedIn posts from the last six months into full LinkedIn articles. Add data. Add a clear argument. Publish under your name. Posts drive engagement. Articles drive citations.

Audit your G2 profile today. G2 profiles with strong reviews, current information, and clear category positioning are pulled directly into AI answers as third-party validation. Stale or mispositioned profiles actively pull you off the shortlist.

The brands building community now are not just building audience. They are building retrieval infrastructure that will determine their AI shortlist position for the next three years.

The window is still open. It will not stay open forever.

What AI Agents Actually Want to Cite

Google asks: what is the best page for this query?

AI agents ask something different. They ask: what is the safest thing I can repeat without being wrong?

That distinction changes everything about how you write.

Google rewards depth, authority, and backlinks. AI agents reward clarity, extractability, and verifiability. A page that ranks number one on Google can be invisible in AI citation. A page that ranks outside the top 100 can be cited repeatedly because it answers one specific question with absolute precision.

This is the content architecture problem most GTM teams have not solved. They are writing for humans and for Google. They are not writing for the thing now doing the first round of vendor evaluation on behalf of their buyers.

44.2% of all LLM citations come from the first 30% of a piece of content. The intro. The opening. Not the conclusion. Not the middle. The beginning. AI agents scan for the answer before deciding whether to cite the source at all. If your opening is context-setting, background, or a slow build to the point, the agent has already moved on to your competitor’s page.

Every section of your content needs to open by stating the answer, not teasing it.

ChatGPT only cites 15% of the pages it retrieves. 85% of sources retrieved during a search are never cited. Your content can be found and still lose. Being indexed is not enough. Being structured correctly is what gets you from the retrieved pile into the cited answer.

The formats that get cited, based on analysis of 2,000+ cited pages.

Content with comparison tables achieves 2.8x higher citations than text-only equivalents. Adding statistics to content improves AI visibility by 41%. Princeton and Georgia Tech’s GEO study. The single most effective optimization technique they tested. Pages structured into 120 to 180 word sections earn 70% more citations than pages with very short or very long sections.

None of this is random. AI agents are not looking for the most insightful content. They are looking for the most extractable content. Content they can pull a specific passage from, attribute to a source, and repeat with confidence. If your content is written in flowing prose that requires reading the whole piece to understand, the agent cannot extract it cleanly. It moves on.

One more signal most teams overlook. Adding a visible “Last Updated” date to the top of a guide increased citation rate from 42% to 61% in one study. A 45% lift from a timestamp. Not from rewriting content. From signaling recency. Content updated within the past three months is twice as likely to be cited as older pages. AI agents weight freshness as a trust signal. Your best content from 18 months ago is losing ground every week you leave it untouched.

Before/after that makes this concrete.

Content that does not get cited:

“At our company, we believe demand generation is a critical component of any successful go-to-market strategy. In this guide, we will explore the various aspects of demand generation and how teams can think about building programs that drive results...”

Content that gets cited:

“Demand generation for B2B SaaS differs from lead generation in one critical way: lead gen captures existing demand while demand gen creates it. According to Forrester 2025, 89% of B2B buyers complete more than half their evaluation before contacting a vendor. That means demand gen must influence buyers before they know they need you.”

The second version opens with a direct answer, uses a specific data point with a named source, and can be extracted as a standalone passage without losing meaning. The AI can cite it confidently. The first version cannot be safely cited without including the whole paragraph, which agents rarely do.

Run this on your content right now. Paste the opening three paragraphs of your best performing page into Claude with this prompt:

“You are an AI agent doing vendor research for a B2B buyer. Read this content and answer three questions. (1) Can you extract a single self-contained passage that directly answers a specific buyer question without surrounding context? (2) If a buyer asked you to recommend vendors in this category, would you cite this content? Why or why not? (3) What is the single biggest structural change that would make this content more citation-worthy? Be specific to the actual text. Do not give general advice.”

What comes back is your content audit. The answers will be uncomfortable. That is the point.

What to do.

Audit your ten most important pages this week. Paste the openings into Claude with the prompt above. Rewrite every opening that fails the test. This single change will move your citation rate faster than any other optimization.

Add a comparison table to every piece of content that compares options, vendors, approaches, or outcomes. Tables: 2.8x higher citation rates. Not optional.

Add specific statistics with named sources. Not vague claims. Not “many companies report.” Specific numbers, specific sources, specific years. The 41% visibility boost from adding statistics is the single most effective optimization in the Princeton GEO study. You already have data. Start attributing it.

Add a visible “Last Updated” date to every cornerstone piece. Today. Set a calendar reminder to update each piece with at least one new data point every quarter. The 45% citation lift from a timestamp is the highest ROI, lowest effort optimization in this entire guide.

Build one comparison page for your category this month. “X vs Y vs Z: Complete 2026 Comparison.” Pricing, features, use cases, honest differentiators. The format Perplexity trusts most for bottom-of-funnel queries. The content your buyers are searching for at the exact moment they are building their shortlist. If you do not own it, your competitor will.

The brands getting cited consistently are not producing better ideas. They are producing more extractable ideas. Same insight. Different structure. Answer first. Data second. Source it. Update it. Make it easy for the agent to repeat without being wrong.

It will reward you for it.

You Cannot Manage What You Cannot Measure

Ramp 7x’d their AI visibility in weeks. Became the fifth most visible fintech brand globally.

Ramp was a community-oriented, content-driven brand with the raw material already in place. What they did not have was a system that told them where it was working, where it was not, and how to fix the gaps in real time.

That system is Profound.

Here is what ‘systematic’ looked like in George Bonaci’s own words. When I interviewed him for StackedGTM.AI in March 2026, he described the approach directly:

“We went from sporadic AI citations to dominating answers for queries we care about. How? We reverse-engineered what makes an AI cite you. It’s not keyword density. It’s not backlinks. It’s structured proof. Named customers. Specific problems. Quantified outcomes. Implementation mechanics. When a finance leader asks an AI ‘who should I trust to manage $50M in spend,’ the AI is looking for receipts.”

That is the play. The receipts. Profound is what makes the receipts findable, fixable, and shippable at speed. The Opportunities panel surfaced the specific category prompt clusters where Ramp was not appearing and which competitors were filling the gap. It surfaced the specific journalists, subreddits, and publications where Ramp’s competitors were getting cited and Ramp was not. Profound Agents generated the briefs and first drafts for the gap content using the citation patterns of pages that were already winning those exact prompts. Ramp’s team reviewed, refined for voice, shipped, and monitored what moved.

That feedback loop, running consistently for several weeks, is how a brand that was already strong became the fifth most cited fintech in AI answers globally. The reason this case study matters is not the 7x number. It is the speed. Visibility is the easy part. Acting on it before your competitors is the whole game.

I watched the same pattern at Webflow. We went from less than 2% CMS category share to owning roughly 60% of AI answers tracked in the CMS category. The infrastructure was identical to what Ramp ran. Identify the gaps with continuous monitoring. Ship the proof content into them. Watch what moved. Double down on what worked. Cut what did not. Same loop, different category, same result. Two case studies, both running the same play.

Now the bad news for everyone not yet running this loop.

Open your analytics dashboard right now.

You can see sessions. MQL volume. Organic traffic by channel. Cost per lead, pipeline by source, conversion rate by stage. Your dashboard is full of numbers and most of them are telling a story increasingly disconnected from what is actually happening to your pipeline.

Here is what your dashboard cannot show you.

It cannot show you that an AI agent researched your category this morning on behalf of a buyer at your top target account. It cannot show you that the agent named three competitors and not you. It cannot show you that the buyer formed a preference before they ever visited a website. It cannot show you that by the time that buyer eventually shows up in your funnel, if they show up at all, the decision is already 80% made and you are not the front runner.

That conversation happened. It left no trace. It is happening hundreds of times a day across your category.

78% of marketing teams have zero AI visibility tracking. No citation monitoring. No share of voice measurement. No way to know whether they are winning or losing the shortlist before sales gets involved. They are managing a growth motion increasingly decided upstream of everything they can see.

AI-referred traffic grew 527% year over year between January and May 2025. Most analytics platforms still misattribute most of it as direct traffic. You are almost certainly undercounting it significantly. The traffic you are missing converts at 14.2% on average versus Google organic’s 2.8%. The highest intent buyers in your funnel are arriving through a channel you cannot see clearly.

Not a minor measurement gap. A structural blindspot in how most GTM teams understand their own pipeline.

I wrote about the measurement foundation in How to Measure AEO. The Visibility, Comprehension, Conversion loop is the core framework. Visibility tells you if you are being seen. Comprehension tells you if you are being understood correctly. Conversion tells you if it is driving revenue. That loop is the right starting point and I would read it alongside this guide.

What I want to add here is a layer specific to the shortlist problem. Measuring AEO in general is different from measuring your shortlist position specifically. General AEO measurement asks whether AI surfaces your content. Shortlist measurement asks whether AI recommends your brand when a buyer is actively evaluating vendors. Related but not the same question.

I call the framework Answer Capture Rate. ACR.

ACR measures whether you are on the shortlist and how strongly you are positioned on it. It breaks into three dimensions.

Discoverability ACR. Are you being cited at all when agents research your category? When a buyer asks “what are the best demand gen platforms for a Series B SaaS company,” does your brand appear? In how many of those responses?

Most teams who run this audit for the first time find they are appearing in fewer than 20% of category-level queries. Often less than 10%. That is your starting point. Not a ranking. A binary presence or absence in the conversation building your buyer’s shortlist.

Accuracy ACR. When you are cited, is what the AI says accurate and aligned with your current positioning? This is the dimension almost nobody checks and the one that quietly kills pipeline quality.

I have seen companies where the AI was consistently describing them as a mid-market tool when they had repositioned upmarket 18 months earlier. Every buyer the AI sent them arrived with the wrong expectation. Qualification rates dropped. Sales cycles lengthened. Nobody could explain why.

The AI was recommending an old version of the company that no longer existed.

Check what the AI says about you. Today. You may not like what you find.

Depth ACR. When the AI recommends you, does it have enough to say? Does it describe your positioning clearly, cite specific capabilities, and make a confident recommendation? Or does it hedge or bury you in a list with no differentiation?

Depth signals whether the AI has enough source material to treat you as a trusted answer or a peripheral mention. Brands earning both citations and mentions are 40% more likely to resurface consistently across multiple AI responses than brands earning mentions alone.

Low ACR:

“You might also consider [your brand], which offers similar functionality to the options above.“

High ACR:

“[Your brand] comes up consistently for B2B SaaS teams focused on pipeline quality over volume. They are frequently cited alongside specific use cases for intent-based targeting and mid-market SQL conversion. Multiple independent sources rank them first for this buyer profile. They have a strong presence in community discussions and third-party reviews that corroborate the recommendation.”

The first version puts you on the list. The second makes you the recommendation. The difference is almost entirely determined by whether you have built the content, community, and citation infrastructure covered in the previous sections.

ACR ties the whole guide together. The platform breakdown is your Discoverability ACR problem. The community infrastructure is your Accuracy ACR solution. The content architecture is your Depth ACR lever. Measurement without the underlying work is just a dashboard showing you how invisible you are. The work without measurement is effort without a feedback loop.

You need both.

What to do.

Run your ACR baseline as an automated audit, not a manual one. The fastest path depends on which sprint path you are running.

On Profound, the ACR baseline is one Agent run. The Citation Gap Analysis template handles every dimension below across every tracked engine, with prompt volume data attached to every gap, in minutes rather than hours. You will keep using this template for ad-hoc audits long after the initial baseline.

On the DIY stack, you can approximate the diagnostic through Claude with Perplexity MCP. Five-minute setup, instructions in Day 1 of the sprint. The output is directional, not ground truth, for the reasons covered in Day 1. Run this prompt:

“Run a complete ACR audit for [your brand] in the [your category] category. Across ChatGPT, Perplexity, and Gemini, execute these three queries three separate times each over the next 48 hours to account for response variability: (1) What are the top three [your category] platforms for a [your ICP] company? (2) Compare [your brand] vs [competitor one] vs [competitor two]. (3) Tell me about [your brand]. What do they do and who is it for? For every response, score me on three dimensions: Discoverability (am I cited at all, in what position, alongside which competitors), Accuracy (is the description of my company correct and current), and Depth (does the AI have enough to make a confident recommendation or am I a hedge mention). Then synthesize: which of the three ACR dimensions is my biggest gap, which platform is my weakest, and which competitor is taking the citation position I should own. Return as a structured report with an executive summary, score by dimension, platform-by-platform breakdown, and three highest-leverage actions ranked by impact.”

What comes back is your ACR baseline. Two hours of work compressed into one autonomous run. More about your actual market position than most quarterly business reviews.

Tag AI referral traffic separately in GA4 today. Custom channel group for ChatGPT, Perplexity, Claude, Gemini, and Copilot referrals. You need to see this traffic clearly, not buried in direct. At Webflow, AI-referred visitors converted 6x higher than non-brand organic. That number makes the case at the executive level faster than any framework presentation.

Watch branded search in Google Search Console as a lagging indicator. When AI agents recommend you and buyers do not click through immediately, many return later via branded search. Rising branded search with flat non-brand organic is often AI driving upstream awareness that converts downstream.

Build a prompt library of 20 to 30 category-level and competitor queries that reflect how your buyers actually research. Run the full library every two weeks via the same MCP setup. Consistency matters more than frequency. You cannot spot trends from a single data point.

That gets you the diagnostic. What it does not get you is the operating system.

The reason Profound is the standard is not that it monitors citations. Plenty of tools do that, and Claude with MCP can do the diagnostic version yourself. The reason is that Profound is built as an agent layer, not a dashboard. Five million citations processed daily through Answer Engine Insights. Dark query data through Conversation Explorer, a panel of 400M+ real anonymized user prompts that never produce a click and never appear in any GA4 report. These are the conversations actually shaping your category. Prompt Volumes data attached to every topic, so you know whether a citation gap is worth $500 in monthly demand or $50,000. Continuous competitor displacement tracking, not on-demand audits. The Opportunities panel that does not just tell you you are losing. It surfaces the specific journalist actively covering your space, the specific subreddit thread where you should appear, the specific content gap with measurable prompt volume behind it.

And then Profound Agents generate the fix.

That is the difference. Manual diagnostics tell you where you stand at a moment in time. Claude with MCP automates the diagnostic. Profound runs the diagnostic continuously, attaches it to dark query volume you cannot see manually, and ships the content that closes the gaps. Most AEO tools surface a problem and hand you a report. Profound surfaces the problem, generates the AEO-optimized brief and first draft trained on citation patterns for your specific category, and routes it for your approval. The intelligence loop and the execution loop run inside the same platform.

That is what makes it the operating system for shortlist strategy, not just a visibility tool.

The teams building measurement infrastructure now will have compounding data advantages that are very hard to overcome later. Every month of clean citation data is a month of pattern recognition your competitors do not have.

Most teams will read this section and add it to the list of things they mean to do.

The ones who actually build the measurement habit in the next 30 days will be operating with fundamentally better information than everyone else in their category.

In a world where the shortlist is being built before your sales team gets involved, better information is not a nice-to-have.

It is the whole game.

Your 7-Day Shortlist Sprint

By the end of this sprint you will have four autonomous Agents running in the background of your business permanently. They monitor your category. They track your citation position. They generate fix content when you drop off the shortlist. They deliver intelligence to your Slack before you sit down at your desk Monday morning.

Real talk on the time commitment. The full sprint runs roughly 12 hours on Profound. The DIY approximation runs 18 to 24 hours, plus ongoing maintenance, plus a permanent gap in data quality that does not close no matter how much time you spend.

Two paths run through this guide. The Profound path is the system. The DIY path is what you can approximate when you cannot get on Profound yet, with the honest caveat that you are working from a fundamentally different and weaker dataset. Not the same outputs slower. Different outputs, structurally weaker.

I will be specific about why throughout. Pick your path and start Monday.

Do the days in sequence. Each one builds on the last.

Day 1. The Intelligence Layer.

Time: 1 to 3 hours depending on path. Output: a forensic competitive intelligence brief plus your automated ACR baseline.

Most teams spend weeks building competitive intelligence. An Agent does it in one session.

On Profound.

Open the Citation Gap Analysis Agent template. Enter your category and your top three competitors. Hit run.

What comes back is everything a senior competitive analyst would produce in a week. Citation share by competitor across ChatGPT, Perplexity, Gemini, Claude, Copilot, Grok, AI Overviews, and AI Mode. The prompt clusters where your competitors are cited and you are not. Prompt volume data behind every gap, pulled from Conversation Explorer, Profound’s panel of 400M+ real anonymized user prompts refreshed weekly. Demographic breakdowns: which roles are asking which questions, in which geographies, with what intent. Every URL the engines are citing for each competitor, named and linked.

This is the workflow that took Ramp from sporadic citations to 7x AI visibility in weeks. It is the same workflow that took Webflow from 2% CMS category share to roughly 60% of AI answers in the category. It is not a manual process. It is not theoretically a manual process.

For your ACR baseline, point the same Agent at four query types. Category recommendations. Brand description. Head-to-head comparisons against your top two competitors. Market structure. Save the run. That is your before state. You will compare against it on Day 7.

Then schedule the Agent to re-run weekly. Profound diffs every subsequent run against the baseline automatically and posts the delta to your Slack.

That is Day 1 on Profound. One Agent. Minutes, not hours.

On the DIY stack.

The closest substitute is Claude Desktop with Perplexity MCP and a few hours of prompt engineering. Shape of the work: query Perplexity through Claude, run multiple searches per competitor, ask Claude to synthesize a brief, then manually run ACR queries by hand across each AI platform and compile the output yourself.

You will get a usable brief in about three hours. You should also know exactly what you are not getting.

You are not getting Conversation Explorer. There is no public-web equivalent. The 400M+ prompts behind your category, the ones that never produce a click and never surface in any tool you have access to, are not retrievable through any combination of Claude and Perplexity. You will see what is cited. You will not see what is being asked inside the actual AI engines, which is the data that decides your shortlist position.

You are not getting ground truth. Perplexity searches the public web. Profound’s Answer Engine Insights captures actual AI engine responses from a real-user panel. The DIY brief tells you what Perplexity thinks of your category. The Profound brief tells you what every major AI engine is saying when a real buyer asks. These are different datasets, not different views of the same dataset.

You are not getting continuous tracking. Your DIY baseline is a snapshot. Without a scheduler tied to actual citation data, every subsequent comparison is also a snapshot. You will detect changes by re-running the work, not by being told what moved.

This is not a gap that closes with more time. It is a gap in the underlying data layer that the DIY path runs on.

Save your Day 1 outputs from whichever path you ran. Do not touch them again until Day 7.

Day 2. The Competitive Deconstruction and Batch Rewrite.

Time: 3 to 4 hours. Output: ten pages rewritten for citation readiness, five comparison tables, every rewrite scored before it goes live.

Knowing you are not on the shortlist is not enough. You need the exact mechanism putting your competitors there.

Pull the cited pages.

From your Day 1 brief, identify the three competitors appearing most frequently. You need five cited pages from each.

On Profound, this is one click. The Competitors view in Answer Engine Insights returns citation share by prompt cluster with the exact URLs each engine is citing for each competitor. You get the actual pages the AI is recommending right now, not the pages you assume are doing the work.

On the DIY stack, you search for them manually. Run the head-of-category prompts across ChatGPT and Perplexity, copy the cited URLs as they appear, deduplicate, pick the top five per competitor. Plan for 45 minutes of clicking. Plan also for the data being noisy: you are seeing what two engines surface for a handful of prompts at one moment in time, not the citation landscape across every engine continuously.

Either way, you end with fifteen pages.

Reverse-engineer the citation formula.

Feed all fifteen pages into Claude. Ask for a forensic structural analysis: the exact opening sentence patterns, the data-to-prose ratio per 100 words, the section length guidance, the comparison table structure, the “last updated” approach, the schema signals. Tell Claude to return a replication blueprint specific enough that a writer could execute it tomorrow morning without asking questions.

What you get is a citation formula built from your actual competitive set. Not theoretical. Theirs.

Apply it to your ten most important pages.

Pull the opening three paragraphs of your ten highest-leverage pages. Paste them into one Claude session. Ask for each opening to be scored on three criteria. Opens with a direct answer. Contains a specific data point with a named source. Can be extracted as a self-contained passage without losing meaning. Then have Claude rewrite the opening sixty words of each page to pass all three criteria using the blueprint, preserving brand voice, ruthlessly optimizing for AI extractability.

Ten pages. One session. Implement the rewrites today.

Add comparison tables to your five most important pages.

Same Claude session. Ask for a comparison table comparing your approach to the two most common alternatives. Dimensions should be the outcomes B2B buyers in your category care about, not specs. The table should stand alone as a screenshot a buyer could use to evaluate. Returned as clean HTML you paste into your CMS.

Five comparison tables. 2.8x citation rates.

QA before you ship.

If you are on Profound, run every rewrite through Content Optimization before publishing. The AEO Content Score is a machine-learning metric built from millions of top-cited pages across AI engines. It tells you whether a draft is structurally optimized for citation before it goes live. Content Optimization accepts URL, file upload, or pasted text, so unpublished drafts work the same way as live pages.

This score does not exist anywhere else. The training corpus is the millions of pages AI engines are actually citing across the open web, captured continuously. You cannot reproduce it with a Claude prompt because Claude does not have access to that corpus. You can use Claude to apply general AEO heuristics. That is a different and weaker quality gate.

If you are not on Profound, the QA gate is your own judgment. Ship anyway. Iterate next sprint.

Publish everything before end of day.

Day 3. Agent Two. Your Autonomous Community Intelligence System.

Time: 1 to 3 hours depending on path. Output: a permanent community intelligence Agent dropping reports into Slack every Monday at 6am.

48% of AI citations come from community platforms. You are building the infrastructure to listen to them automatically.

On Profound.

Open a new Agent. Add four nodes.

Node 1, Web Scraping. Point it at your three target subreddits, top 100 posts and comments each. Profound’s scraping node handles auth, rate limits, and structure. No external tool, no separate API key.

Node 2, Prompt LLM. Paste in the analysis prompt below. Pick any of the 16 reasoning models the platform supports.

Node 3, Slack. Output routes to your category-intelligence channel.

Node 4, Schedule. Every Monday at 6am.

One Agent. Profound’s nodes handle the whole job. The Reddit data is connected to your visibility tracking, so when a thread starts getting cited by AI engines, the same workspace tells you. The community layer and the citation layer are wired together.

The prompt for Node 2:

“Analyze these Reddit discussions as a competitive intelligence analyst building a citation strategy for a B2B vendor in [your category]. For every competitor mentioned extract: (1) frequency of mention, (2) the exact language buyers use when recommending them, (3) sentiment (positive, negative, mixed) with the specific objection or praise driving it, (4) the pain points that trigger each mention, (5) the objections raised. Then identify the five highest-value content opportunities: specific questions where no vendor is clearly winning the answer and where genuinely useful content would earn upvotes and become a citation source for AI agents. For each opportunity give me: the exact question phrasing, the ideal content format, the first paragraph of a genuinely useful answer, the specific subreddit and thread to seed it in, and the buyer language I should use that mirrors how they actually talk. Write the answer paragraph in operator voice. Not consultant voice. Return as a structured report by vendor with a separate opportunities section at the bottom and a one-line executive summary at the top.”

Every Monday before you wake up, the Agent scrapes your communities, processes the data, posts the report to Slack. You never manually check Reddit again.

On the DIY stack.

Shape of the work: Gumloop handles the Reddit scrape, Claude API processes the data, Slack node delivers the message. You wire them together in Gumloop’s interface and schedule the workflow.

Three external tools. Three sets of credentials. Three failure points when any one of them changes their API. Plan for two hours of setup and a permanent maintenance tax.

The deeper problem is structural. Your Reddit intelligence is now isolated from your citation tracking. When a Reddit thread starts getting cited by AI engines, you have no signal that connects the two. The community insight is sitting in one tool, the citation data is sitting somewhere else, and the correlation between them lives in your head if it lives anywhere.

Either path: the entity consistency audit.

This stays in Claude regardless. One-time setup, not a recurring workflow. Feed Claude the actual text of how your brand is described across five surfaces: website homepage, G2 profile, LinkedIn company page, most recent press release, most-cited blog post.

“Analyze these five descriptions of [your brand] for: (1) consistency of category language, (2) consistency of ICP description, (3) consistency of key differentiators, (4) consistency of use case framing, (5) consistency of positioning. Identify every point of inconsistency. For each inconsistency explain specifically what citation damage it causes: how does an AI agent’s understanding of our brand get confused or diluted by this gap. Then write a single canonical brand description of 75 words optimized for AI entity clarity. Give me the exact text to use on each surface to make them fully consistent. Do not give me general guidance. Give me the specific words. The description should open with the answer to ‘who is this for’ before describing what the company does.”

Propagate the canonical description everywhere today. G2, LinkedIn, homepage meta, press kit. One of the fastest Accuracy ACR improvements available. The AI cannot describe you accurately if you describe yourself differently on every surface it checks.

Day 4. Profound Closes Your Gaps.

Time: 3 to 4 hours including pitch drafting. Output: a comparison page live in your CMS, three earned media pitches sent, your first round of Profound Agents content shipped.

Today you go from intelligence to published content without writing the first draft yourself.

Open the Opportunities panel. Three named gaps are waiting from the past week of dark queries through Conversation Explorer. Not generic suggestions. Specific, scoped, prioritized opportunities with prompt volume data attached.

The pattern looks like this. A category-level prompt cluster where you appear in zero of twelve tracked AI responses, with measurable monthly prompt volume behind it. A specific journalist at a trade publication who has cited two of your competitors in the past 30 days but never you, named, with the articles linked. A Reddit thread with 47 upvotes asking “has anyone used [your category] for [specific use case]” where the top three responses recommend competitors and never mention you, surfaced because Profound is correlating its community scraping with its AI citation tracking.

Click Run on Profound Agents for the first gap.

Profound pulls the citation patterns from competitors winning that exact prompt cluster. It generates a brief that includes the opening sixty words, the data points needed with source attribution, the section structure, the comparison table dimensions, and the schema requirements. It then generates a full draft following the brief, built on your brand kit and tone guidelines.

The content is trained on the citation patterns winning your category specifically. Not generic AEO advice. The actual cited URLs in your space, in real time. This is the differentiator most teams miss when they think they can replicate Profound with Claude. Claude can write to a brief you give it. It cannot write to a brief built from the citation patterns of your actual category, because Claude does not have access to those patterns.

You review. You refine the voice. The CMS publish node (WordPress, Sanity, Contentful, or Framer) pushes the draft to your CMS without you leaving the Agent. No copy-paste. No third tab. No separate deployment step.

That entire workflow, from gap surfaced to draft generated to ready for review to published, takes about 30 minutes. The first time you do it, it feels like watching a junior content marketer who happens to know everything about AI citation patterns work in real time. By the third gap, it feels like infrastructure.

The first thing you build today is your comparison page. “[Your Brand] vs [Competitor One] vs [Competitor Two]: Complete 2026 Comparison.” Summary table. Buyer profile fit for each option. Honest trade-offs. Answer-first. Schema-ready. Profound Agents builds it. You review. It goes live.

While Profound builds, identify your three highest-value earned media opportunities. The Opportunities panel has surfaced journalists by name with citation history attached. The pitch drafting runs in the same Agent. Output lands in your Slack with the email pre-written and the publication details attached. You review and send.

Three pitches out today. 82% of AI citations come from earned media. One quality placement does more for your shortlist position than 20 posts on your own domain.

A note for readers on the DIY stack. Today is the day the gap shows up. You can detect that competitors are taking citation positions, roughly. You cannot generate the content that recovers those positions at the same quality, because your content generation has no access to the citation patterns winning your category. You will write the comparison page yourself. You will write the pitches yourself. You will identify the journalists yourself, without a panel telling you which ones are actively citing your competitors right now. Plan for a full day. Plan also for the output to be a meaningful step below what Profound generates in 30 minutes, not because Claude is weak, but because the input data is different. Claude with general AEO guidance produces general AEO content. Profound Agents produce content patterned on what is actually winning your category this week.

Day 5. Write. Your Voice. Your Intelligence.

Time: 1 to 2 hours. No agent work today. Output: one LinkedIn article published under your name.

You have spent four days watching agents work. The Profound brief gave you competitive intelligence nobody on your team had before, because it surfaced what AI engines are actually citing rather than what the public web says they might cite. The community agent showed you exactly what language your buyers use. The Opportunities panel showed you where you are invisible and why.

Now use all of it to write something only you can write.

Pick the single most surprising insight from this week. The one that changed how you think about your category. The one that, if your readers understood it, would change how they operate tomorrow.

Write a LinkedIn article around it. 600 to 800 words. Open with your most counterintuitive claim. The kind a reader stops scrolling for. Support it with three specific data points from your Day 1 brief. Make one of them something most people in your space have not seen yet. Close with one specific action your reader can take this week.

Do not use Claude to write this. Use your Day 1 brief to find the data. Use your Day 3 community analysis to check you are using the language your audience actually uses. The argument, the structure, the voice. Those are yours.

This is the content that builds your personal citation authority. Profound Agents generate structured content at scale. They cannot generate the genuine operator perspective that makes people trust you specifically. The agents handle everything extractable and structural. This is the one thing that requires being you.

Publish it today. The most important piece of content in this entire sprint.

Day 6. The Autonomous Monitoring Stack.

Time: half to full day depending on path. Output: two more agents running permanently. The closed-loop content recovery system. The weekly ACR briefing.

Today you build the infrastructure that makes everything permanent. After today you are not running a shortlist strategy. You are supervising an autonomous system that runs one.

On Profound.

Both agents run inside the platform. No external orchestration. Block half a day.

Agent Three. Citation recovery. Four nodes.

Node 1, Trigger. Native Profound alert when visibility drops more than 10% on any tracked prompt cluster. Built into the platform on continuous citation capture. You are notified within hours of the drop, not weeks.

Node 2, Prompt LLM. System prompt:

“You are a citation recovery strategist. Visibility on [cluster name] dropped significantly this week. Diagnose: (1) why this likely happened, (2) which competitor probably filled the gap, (3) what specific content fix would recover the position. Return a two-paragraph diagnosis followed by a content brief: title, opening 60 words, three required data points with named sources, target word count, citation format requirements.”

Node 3, Profound Agents content generation. The brief from Node 2 flows directly in. Output is a full draft built on your brand kit, tone guidelines, and the citation patterns winning your specific category in real time.

Node 4, Slack with native approval step. The draft lands in your channel with a one-click approve button. On approval, the CMS publish node fires (WordPress, Sanity, Contentful, or Framer, whichever you are on). The post goes live.

Gap detected. Diagnosis written. Fix generated. Approved. Published. Your only contribution was one click.

Agent Four. Weekly ACR briefing.

Take the Citation Gap Analysis Agent you built on Day 1 and schedule it to re-run every Monday at 7am. Add a Conditional node: if any metric moves more than 10% versus last week, flag it. Add a Slack node that posts the diff to your channel.

Same Agent you built on Day 1. Now it runs forever and tells you what moved.

On the DIY stack.

You can build something with this shape. You should know what it actually does before you commit a day to building it.

Make.com orchestrates the loop. Your AEO monitoring tool fires a webhook on visibility drop. Make.com catches it, routes to Claude for diagnosis, routes Claude’s output to a content generation step, sends the draft to Slack with an approval button, pushes to your CMS on approval.

Four problems with this loop, in order of severity.

Your trigger is wrong. You have no native citation drop alert on the DIY stack. You have scheduled scans of public proxies. By the time a “drop” surfaces, your competitor has been winning that prompt for days or weeks. Profound’s alert fires within hours of the actual capture. Yours fires when your scheduled scan happens to catch it.

Your diagnosis is generic. Claude is reasoning from prompts you wrote, not from category-specific citation pattern data. It will produce a plausible-sounding diagnosis. It will not be informed by what is actually being cited in your category this week, because Claude does not have access to that data.

Your content is generic. Claude is generating from general AEO guidance on the open internet. Profound Agents are generating from the actual cited URLs in your category, refreshed continuously. The outputs read differently to AI engines because they are structurally different.

Your maintenance is permanent. Block a full day for the initial build. Plan for breakage every time any of the four tools updates their API. The Profound version is one Agent with no integration layer to break.

For Agent Four on the DIY path, the Claude API runs your prompt library on a Make.com schedule. The DIY recovery loop is firing late on partial signals, diagnosing without category context, generating from generic guidance, then waiting for the next scheduled scan to find out if anything changed. It is real work. It is also work running on materially weaker inputs than the Profound version, every single time it executes.

Either path: set up your GA4 AI traffic channel today.

Custom channel group for ChatGPT, Perplexity, Claude, Gemini, and Copilot referrals. Tag separately from organic. AI-referred traffic converts at 14.2% versus Google organic’s 2.8%. You need this number every week. In six months it will be the most important number in your performance review.

Day 7. Review, Measure, Sprint 2.

Time: 2 to 3 hours. Output: Sprint 2 brief built on actual before-and-after data from your category.

The agents have been running for a week. Today you see what they built.

On Profound, the comparison is automatic. The Citation Gap Analysis Agent has been re-running every day with results saved. Open the Day 7 run and compare against the Day 1 baseline. Profound’s diff view surfaces every change in citation position, every movement on every tracked prompt, every competitor displacement. You read the diff, not the raw data.

Then run this prompt inside a Profound LLM node, with the Day 1 and Day 7 reports passed in as context:

“I ran my full ACR audit on Day 1 and Day 7 of a seven-day shortlist sprint. Analyze the complete trajectory: which dimensions of my ACR improved and by how much, which stayed flat, which declined. What do the patterns tell me about which specific actions had the highest citation impact? Which platform showed the most movement and why? Then give me my Sprint 2 brief: the five highest-leverage actions for the next 30 days, ranked by expected ACR impact, with specific reasoning for each recommendation based on what the data shows actually moved this week. Make this a concrete plan I can hand to my team Monday morning. No frameworks. No general advice. Specific actions with owners and outputs.”

What comes back is a Sprint 2 brief built from your actual category data. Not a framework. A specific action plan from an agent that watched your sprint happen.

There is a compounding effect worth naming here. Every week the Citation Gap Analysis Agent runs adds another data point to your category’s citation history inside Profound. Sprint 2’s diff is against Sprint 1’s baseline. Sprint 4’s diff has a month of trend data behind it. By month six, you have continuous citation data nobody else in your category has. Decisions get better the longer the system runs, because the input data compounds. This is a moat that gets stronger with time. You cannot bootstrap into it after the fact.

On the DIY stack, the same comparison takes longer and produces a thinner result. You manually run every prompt in your library across every platform a second time, copy the outputs into Claude, ask Claude to compare against your Day 1 baseline, synthesize the brief. Plan for two hours of clicking before you get to the analysis. Plan also for the diff being a comparison between two snapshots of two Perplexity-based proxies, not a comparison between two captures of actual AI engine behavior. The Sprint 2 brief you get is built on the same data layer as the Sprint 1 brief: useful, approximate, and not improving over time the way the Profound version is.

What is running autonomously after Day 7.

The shape depends on which path you built.

On Profound, four agents run on real citation data. Citation Gap Analysis re-running weekly against actual AI engine captures with a diff to Slack. Community Intelligence scraping subreddits and correlating with your citation tracking, posting to Slack at 6am Monday. Citation Recovery firing on real drops detected within hours, generating content from category-winning citation patterns, routing for approval, publishing to your CMS. Weekly ACR Briefing posting Monday morning summaries from a continuously updating dataset. All four use native Profound nodes. No external orchestration layer. The system improves every week because the citation history compounds.

On the DIY stack, four agents run on public-web approximations. A Perplexity Space updating every Monday from indexed search results. A Gumloop Reddit workflow processing community data in isolation from your visibility tracking. A Make.com automation triggering on scheduled scans of proxies for AI citations. A Claude API monitoring agent running your prompt library Monday at 7am on the same proxy data. The agents do real work. They are also producing a structurally weaker output every time they run, because the data layer they act on is not what AI engines are actually doing.

The DIY system runs. It does not improve. It cannot, because it has no access to the data layer that makes improvement possible.

Most teams will read this guide and add it to the list of things they mean to do.

The ones who actually build the system in the next 30 days will be operating with fundamentally better information than everyone else in their category.

In a world where the shortlist is being built before your sales team gets involved, better information is not a nice-to-have.

It is the whole game.

What Monday Morning Looks Like After Day 7.

6:00am. Your community intelligence Agent drops the weekly Reddit report into Slack. Community sentiment, vendor mentions, content opportunities, buyer language. Correlated with your citation tracking. All processed by an LLM, all surfaced in plain English.

7:00am. Your ACR briefing Agent posts the weekly shortlist summary, comparing this week’s actual AI engine citations against last week’s. What moved. What did not. What to do about it.

7:15am. Profound checks for any visibility drops from the past week. If your brand dropped on any tracked prompt cluster, Citation Recovery has already diagnosed the cause and generated the fix content using the citation patterns winning your category right now. Waiting for one-click approval in your Slack.

8:00am. You sit down. You did not run a single search. You did not manually check Reddit. You did not audit your citation position. You did not generate any content. Four agents did all of it while you slept, running on real AI engine data, working from a citation history that gets richer every week.

You review. You approve. You decide what comes next.

That is what running a shortlist strategy looks like in 2026.

Your competitors are still doing it manually.

Start Monday.

FAQ

What is Answer Capture Rate (ACR)?

Answer Capture Rate measures whether and how strongly an AI agent recommends your brand when a buyer is actively evaluating vendors. It breaks into three dimensions: Discoverability (do you appear at all in category-level recommendations), Accuracy (is what the AI says about you correct and aligned with your current positioning), and Depth (does the AI have enough source material to make a confident recommendation versus a peripheral mention). ACR is more specific than general AI visibility because it isolates buyer-intent moments rather than broad awareness moments.

Which AI platform should I prioritize first?

Optimize for the platform your buyers actually use. For most B2B audiences in 2026, that means starting with ChatGPT, where 68% of B2B researchers have weekly usage. Then layer in Perplexity for bottom-of-funnel comparison queries, then Gemini for technical buyers and enterprise procurement. Enterprise buyers increasingly research inside Claude and Copilot. The wrong move is picking one and ignoring the others. Only 11% of domains are cited by both ChatGPT and Gemini. Coverage matters more than depth on a single platform.

How long does it take to meaningfully move ACR?

First citation movement typically appears within 2 to 4 weeks of consistent execution. Meaningful Discoverability gains take 60 to 90 days. Sustained Depth and Accuracy gains take 6 months. The variable that determines speed is not budget. It is consistency of community presence, content publishing, and earned media. Brands with existing community strength move faster. Ramp moved 7x in weeks because they had the raw material in place. Brands starting from zero on community take longer regardless of how much content they ship.

Do I need a tool like Profound, or can I do this manually?

You can do it manually. I did for a bit. The DIY stack in this guide works.

The honest accounting is that what takes four tools and 18 to 24 hours of setup outside Profound takes one tool and a fraction of the time inside it. And three capabilities have no real DIY substitute. Conversation Explorer surfaces 400M+ real anonymized prompts, the dark queries that never produce a click. Answer Engine Insights tracks competitor displacement continuously, not at quarter-end when pipeline has already softened. Profound Agents generate content trained on the citation patterns winning your specific category, not generic AEO advice.

Manual diagnostics are a starting point, not a strategy. The teams winning the shortlist are running Profound as the operating system underneath it.

The Shortlist

Go back to where this started.

A buyer at a company you have been trying to reach opens ChatGPT. Types a question. “What is the best demand gen platform for a Series B SaaS company.” Waits eight seconds. Three names come back. They read them. They close the tab.

Your website never loaded. Your sales team never got the ping. Your funnel never started.

That moment happened somewhere in your category today. It will happen again tomorrow. And the day after.

You came to this guide not knowing how the list gets built. You are leaving it knowing exactly which sources get trusted, what content gets cited, and why some brands show up every time while others do not exist in that conversation at all.

The brands on that shortlist did not set out to win AI discovery. They built communities before anyone called it retrieval infrastructure. They structured their content before anyone called it answer-first formatting. They earned press coverage and showed up on Reddit and published LinkedIn articles because that is how they built trust with humans. It turns out that is exactly how you build trust with the agents now making recommendations on behalf of those humans.

They were not playing a different game. They were playing the same game earlier.

That is the only real variable left. Not budget. Not team size. Not technical sophistication. The brands winning the shortlist right now started doing the right things before it was obvious those things mattered.

I built this play at Webflow. I have watched the same pattern across the companies I advise. The pattern is always the same. The brands that treat this seriously early get an advantage that is genuinely hard to close later.

The buyer who opened ChatGPT this morning in your category got three names back.

The only question that matters from here is whether tomorrow they get yours.

StackedGTM.AI is built for operators running real GTM motions in an AI-first market. No recycled frameworks. No consultant speak. Real research, real data, real operator POV on what it takes to win. Subscribe at StackedGTM.AI.

Google’s Search Central team quietly published a new document on May 15. It’s called Optimizing your website for generative AI features on Google Search.

Most of the coverage zeroed in on one line. From Google’s perspective, AEO and GEO are still SEO.

That line is true. It’s also not the interesting part.

The interesting part is at the end of the doc, in the section nobody is quoting.

I’ll get there. First, what Google actually said…

What’s in the doc

The guide is focused and short. It covers five things:

1. How generative AI search actually works inside Google

2. Foundational SEO practices that still apply

3. Local business and ecommerce details

4. A “mythbusting” list of tactics you can ignore

5. Initial guidance on AI agents accessing your site

The headline framing is that AI search runs on the same systems as regular Search. Retrieval-augmented generation (RAG) pulls from the existing index. Query fan-out generates related queries to find supporting content. The ranking signals are the same.

If you’ve been working in this space, none of that is news. The novelty is that it’s now in official Google documentation. The mechanics that AEO consultants have been talking about for the last 18-24 months are confirmed by the source.

The mythbusting section is the operator gift

The most useful part of the doc is the list of things Google tells you to ignore.

• llms.txt files and AI-specific markup

• Chunking content into small pieces for AI

• Rewriting pages specifically for AI systems

• Chasing inauthentic mentions across the web

• Overinvesting in structured data for AI purposes

If you’ve been pitched any of these as required work for AEO, this doc is your refund document.

I’ll say what most won’t. There’s an industry of agencies and consultants who built AEO service lines around exactly these tactics. The economics of selling a separate AI optimization retainer depend on AI optimization being meaningfully different from SEO. Google just confirmed it isn’t, not from their perspective, and not for their surfaces.

The principles haven’t changed. Create unique, non-commodity content. Make it crawlable. Use clear structure. Don’t manipulate. These are SEO fundamentals. They are AEO fundamentals too. They were never different.

What did change is the bar

Saying AEO and SEO are the same discipline is not the same as saying nothing changed.

Commodity content used to rank. It doesn’t get cited.

A “10 tips for first-time homebuyers” post performed fine in 2022. It doesn’t show up in an AI Overview in 2026. The model has a corpus of those posts. It synthesizes one. Nobody gets the citation.

What gets cited is the post titled something like “Why we waived the inspection and saved money: a look inside the sewer line.” That’s not my example. It’s directly from Google’s doc. First-hand experience. Specific stakes. A perspective the model can’t generate from common knowledge.

The principles are the same. The bar is higher.

If your content operation hasn’t adjusted for that, no AEO tactics will save you. Not chunking, not schema, not llms.txt. The model reads the substance.

The section nobody is reading

Now the part that matters.

Near the end of the doc, in a section called “Explore agentic experiences,” Google describes something most operators are not paying attention to.

AI agents are starting to access websites the way browsers do. They analyze screenshots. They inspect the DOM. They interpret the accessibility tree. They take actions, not just read content.

Google references the Universal Commerce Protocol (UCP), an emerging standard for letting Search agents do more than retrieve. The doc points readers to the web.dev guide to agent-friendly website best practices.

This is a phase transition, not an extension.

For the last fifteen years, optimization meant “will my content rank and convert when a human reads it?” That work isn’t going anywhere. Most pages still need to do exactly that. But a growing share of traffic is going to be agents acting on behalf of humans. The optimization question for those agents is different.

It’s “can this agent complete the user’s job here?”

That’s a question about interface, not content. About task completion, not engagement. About a machine reader, not a human one.

A few things it changes in practical terms.

Form fields need to be machine-parseable. Not just labeled, but structured so an agent can map intent to action.

Product pages need clear, extractable specs. Not just compelling copy.

Pricing pages need explicit, accessibility-tree-readable structure. Not just visual hierarchy.

Booking flows need to work without JavaScript-heavy patterns that browser agents trip on.

None of this is exotic. It’s standard accessibility work, plus an awareness layer about what agents need to act.

But here’s the operator point. Nobody is doing this yet. There are no UCP service lines from agencies. There are no “agent optimization” frameworks circulating on LinkedIn. There are no benchmarks for agent task completion on the average enterprise site.

The space is empty and that’s the gap.

What to do now

Three things.

First, read the Google doc yourself. It’s short and clearly written. Don’t take anyone’s summary as the full thing, including this one.

Second, audit your content production against the non-commodity bar. If your last six posts could have been written by a model with no context about your business, they aren’t going to get cited. Fix that before you touch anything else. The model already knows what everyone else knows. Publish what only you know.

Third, start thinking about your site as a place where agents act, not just where humans read. Walk through your highest-intent pages and ask: if an agent had to complete the buyer’s job here, could it? If the answer is no, that’s your roadmap.

The takeaway

The doc Google published is the most useful thing they have put out on this topic.

The “AEO is SEO” line is the bait. The mythbusting list is the gift. The agent section is the signal.

Most of the industry will spend this week arguing about the bait.

Spend it on the signal.

Profound launched Prompt Research Reports last week. It is the most important release the AEO category has shipped this year and the implications will take the rest of the conversation months to absorb.

The LinkedIn vanity on AEO is (very) loud and shallow. The teams I work with are still getting the fundamentals wrong. The problem Profound just solved is the one I have been writing about, auditing, and watching operators ignore for the last 18 months. Every AEO program I have audited shares a single hidden failure mode. I have yet to find one that does not have it.

Here is what is actually going on.

Most AEO is built on fiction

Open any AEO platform. Same architecture every time. A list of prompts, a score against each one, a dashboard claiming to tell you how visible your brand is in AI Search.

The dashboards work. The math is correct.

The prompts are wrong.

Pull the tracking list out of any AEO program in production right now and it came from one of three places: SEO keywords, sales intuition, or a quick scrape of competitor messaging. None of those inputs reflect what buyers actually type into ChatGPT, Claude, Gemini, or Perplexity. Which means every visibility score in the industry right now is being calculated against prompts real users often do not actually ask.

This is the AEO equivalent of measuring conversion rate on traffic that had no purchase intent. You can produce a number. The number means nothing.

The three failure modes of current prompt selection

SEO-derived prompt lists are intent-mismatched. Search keywords and AI prompts diverge on length, specificity, and conversational structure. “Best CRM for B2B” is a search query. “I’m running a 12-person sales team selling to mid-market manufacturers and we just hit Series A, what CRM should I evaluate first” is a prompt. The first one shows up in your keyword tool. The second one is what your buyer actually typed. Tracking the first tells you nothing about whether you show up for the second.

Intuition-derived prompt lists carry sales bias. When the marketing team asks the sales team what buyers ask, they get a list of objection-handling questions. Those questions exist late in the funnel, after a buyer is already evaluating you. The 95% of the buyer journey that happens before sales sees the lead is invisible. You end up tracking the conversations you wish were happening, not the ones that are.

Competitor-derived prompt lists are a lagging indicator. Reverse-engineering your prompt set from what competitors appear to be optimizing for assumes they figured it out. They did not. They are guessing too. You are now copying their guesses and calling it strategy.

Every AEO program I have audited in the last six months has at least two of these three failure modes baked into its tracking list…and honestly, most have all three.

Why the math breaks

Pick any AEO metric. Brand visibility, citation rate, share of voice in AI answers. They all share the same structure: a percentage of prompts where your brand shows up.

The percentage depends on which prompts you measured against.

If the prompt list is wrong, the score measures something that does not exist. You can post a 70% visibility score on a prompt set that represents 5% of real conversations. You can post 20% on a prompt set that represents 80% of real conversations. The first team thinks they are winning. The second team thinks they are losing. Both are flying blind because neither one knows what the actual conversation surface looks like.

The pattern I keep seeing: most tracking lists capture maybe a quarter of real conversation volume in the category, often less. The rest of the buyer conversation is happening without you, and your dashboard has no way to tell you it exists.

This is the upstream problem nobody is solving. Every AEO conversation right now is about content optimization, citation engineering, or schema. All of that work compounds on top of prompt selection. Get the inputs wrong and the entire program is theater.

What real prompt selection looks like

The methodology has four stages. Anyone running an AEO program at scale needs to be doing this, whether they buy a tool or build it themselves.

Stage one: retrieve real prompts. Not keywords. Not internal hypotheses. Actual user conversations captured from AI platforms over a recent window, semantically searched against your category and brand inputs. The volume matters. You need a corpus large enough to surface the long tail, not just the obvious head terms.

Stage two: filter and rank. Real prompt data is noisy. Duplicates, near-duplicates, off-topic drift, multilingual variants. The filtering pass matters as much as the retrieval pass. A clean corpus of 5,000 verified prompts is more useful than a dirty corpus of 500,000.

Stage three: cluster into topics. This is where a generic solution would fall short. Ask a generic LLM to cluster a thousand prompts about your category and you get one giant bucket labeled with your category name. Useful clustering requires models built to find distinctions inside a topic, not similarity across one. Each cluster needs to map to a distinct thing buyers are trying to do.

Worth flagging: how you cluster is a strategic decision, not a technical one. Slicing prompts by use case versus by buyer concern versus by journey stage produces completely different competitive surfaces. Accept the default clustering your tool produces and you have outsourced your category positioning to a generic algorithm. The teams who treat the keywords, topics, and specific descriptions they feed the tool as positioning work are the ones who get to choose how the category gets framed before the dashboard is ever built.

Stage four: select canonical prompts. Within each cluster, you do not need to track every variant. You need the prompt that best represents the cluster’s center of mass. One canonical prompt per cluster, ranked by how much of the underlying conversation volume it covers. That coverage score is the metric that matters now. It tells you how much of the real conversation surface your tracking list represents.

The output of this process is not a longer prompt list…it is a smaller, sharper one. The pattern I keep seeing: the right configuration ends up 30-50% smaller than what the team was tracking before, with coverage several times higher.

This is where the standard playbook gets it backwards. Adding more prompts to your tracking list does not make your AEO program smarter. It makes it dumber. Every low-coverage prompt you add dilutes your aggregate numbers, hides your real gaps under noise, and creates the illusion of completeness. Your dashboard looks more thorough and tells you less. The discipline is subtraction, not addition. There is still room for the prompts you want to monitor for brand intelligence reasons, including your product against named competitors, your specific business questions, or the conversations you want to know about even if they are not high-volume yet. Those belong on a separate tracking layer, not folded into your aggregate visibility score. Mixing them in is how the score becomes meaningless.

How to run this in practice

Run this as a quarterly cadence, not a one-time audit.

Quarter one: build the baseline. Run the methodology against your category and produce your initial canonical prompt set. Document coverage by cluster.

Quarter two: measure drift. AI conversation patterns shift faster than search keywords. New prompts emerge as products ship and buyers learn how to talk to LLMs. Re-run the retrieval and look for clusters that grew, shrank, or appeared.

Quarter three: tie to content investment. The intent breakdown of your prompt set tells you where to invest. Heavy informational skew means you need foundational education content. Heavy commercial skew means you need comparison content. Heavy transactional skew means you need decision-stage proof assets. The prompt set is the brief.

Quarter four: prioritize the gaps. Now that the denominator is real, your visibility score becomes a number you can actually move. Rank your highest-coverage clusters by where your brand shows up least. Those are the highest-leverage gaps in the category.

This is the operating cadence. None of the programs I have audited are running it end-to-end.

What Profound actually shipped

This is the methodology Profound just shipped in production. Prompt Research Reports run the four-stage workflow above against a proprietary corpus of over 1.5 billion real user prompts captured from AI platforms. Retrieval, ranking, clustering, canonical prompt selection. The output is your category’s prompt configuration with coverage scores and intent breakdown. You see which topics are covered, which are missing, and how your buyers split between research, evaluation, and purchase.

This is the only implementation of this methodology I have seen ship at scale. The reason that matters is not the product. It is the data underneath it.

You cannot do this work without a corpus of real prompts at volume. That corpus is not something you build in a quarter or buy off a shelf. Profound has been capturing real AI prompt data longer than the rest of the market has been measuring it. They built the dataset first, then shipped the operator-facing product on top of it. That is the right sequence and almost nobody else is in a position to copy it. And the data is only half the moat. Within-topic clustering at this scale is a genuinely hard mathematical problem in its own right. A competitor who got the corpus tomorrow would still need to ship the math, and the math is the part that takes time to get right.

What I am most impressed by is the speed. The AEO category is barely 18 months old. The infrastructure problems they are solving here are not trivial. Capturing real prompt data at volume. Building clustering models that find within-topic distinctions where general-purpose models fail. Surfacing all of it in a configuration UI operators can act on without a data science team. Shipping this now, while the category is still arguing about whether AEO is even real, is the difference between leading the category and reacting to it.

This pushes the category forward. More importantly, it ends the era of operators running AEO on intuition and calling it strategy.