It is rarely about your product being bad. It is about where the AI looks.

First, check you are asking the right question

Most founders type their own product name into ChatGPT. They get a decent description back and relax. That is a branded question, and the AI answers it with a quick look at your own site.

The question that matters is the one your buyer types before they have heard of you. “Best X for Y.” “Alternatives to [the big competitor].” “I run a small shop alone, what should I use for Z.”

One founder on r/SaaS ran both kinds. The brand-name test looked fine. The buyer-question test told “a completely different story”.

If you have only tried your own name, try three buyer questions now. Use a fresh chat, signed out. Our free check does the same thing 60 times over.

The three usual reasons

1. You are not in the AI’s search results

ChatGPT, Gemini and Perplexity usually search the web before answering a buying question. A 2026 study looked at how GPT decides which brands to mention. Brands missing from its search results, with no cited site, were mentioned 2.8 percent of the time. Brands whose site was cited and who showed up in branded searches: 91.4 percent.

Ordinary search rank still counts. A separate analysis covered 815,484 query and page pairs. 58 percent of pages at position one were cited, against 14 percent at position ten.

So the first question is boring but decisive: does your site come up when the AI searches for what you do? Blocking AI crawlers in robots.txt can make the answer no. So can pages that load their words with JavaScript. Neither has anything to do with your product.

2. Nobody else is talking about you yet

AI answers lean hard on what other people say. For US software questions, 72.7 percent of the sources AI search engines cited were earned media (reviews, lists, articles). Not the brand’s own pages.

Muck Rack counted 207 million citations. More than 84 percent came from earned or third-party sources. Only 0.3 percent came from paid content.

A new product usually has a nice homepage and nothing else. No review profile with ten reviews. No spot on the “best X” list a blogger wrote two years ago. No Reddit thread where someone said “I switched to this and it was fine”. Those are what AI reads, and they do not exist yet.

3. Your page says nothing quotable, or the big name wins the tie

When an AI does find you, it needs a clear sentence to repeat. “Reimagine your workflow” is not one. It will skip you for the competitor whose page says “invoicing for freelancers, $12 a month”.

Then there is the tie-break. In a 2026 experiment with identical product specs, the well-known brand was recommended 100 percent of the time. A rating edge of under 0.1 stars was enough to flip it. New products rarely have the reviews to break that tie.

What works, and what does not

Works, with evidence behind it:

• A spot on the “best X” lists the AI already cites for your category. Ahrefs analysed 26,283 cited URLs: list articles were 43.8 percent of the page types ChatGPT cited.
• Honest reviews on the one or two review sites the AI cites for your category. G2 or Capterra for B2B, the App Store or Product Hunt for apps. A small rating edge beats a bigger brand.
• Real answers in real Reddit threads, under your own name.
• A homepage whose first paragraph says what you are, for whom, and what it costs.
• Showing up in ordinary search results for the questions AI searches.

Does not work, despite being widely sold:

• An llms.txt file on its own. Ahrefs found 97 percent of the llms.txt files it tracked received zero requests.
• Schema markup on its own. Ahrefs tracked 1,885 pages that added schema: no statistically significant change in ChatGPT citations.
• Submitting to 100 directories. ChatGPT and Gemini rarely cite directories.
• Fake reviews, persona accounts, hidden text. That is manipulation, and it gets found.

A note on consistency

AI answers move around. SparkToro ran the same prompts again and again. The same brand list came back less than one time in a hundred.

So a single screenshot proves nothing, in either direction. We run every question three times on five assistants. We report how often you are named, never a rank.

What we would do

1. Find out where you stand. Five buyer questions, three runs each, on ChatGPT, Gemini, Claude, Perplexity and Muse: 75 answers. For each one, who was named instead of you and which sources were cited. Free, by email within 2 working days. A person checks every report.
2. Map the sources. The audit widens that to 25 questions. It turns the cited sources into a prescription: the exact lists, review sites and threads to get into for your category. $149, credited toward the project within 30 days.
3. Do the work and re-check. The project pitches 25 named lists and sites, with two follow-ups each. It writes three pages for you and sets up review profiles on the two sites AI cites most in your category. You also get a founder-led Reddit plan. Then we ask the same questions at day 30, 60 and 90. $799.

We cannot make an AI say your name. We can get your story into the places it reads, and measure whether it starts to.

The pattern was simple. Muse answered from the open web. When it found a small business’s own website, it read it and quoted it. When the answer came from pages that never mention the business, the business was not in it.

What we ran

• The model: muse-spark-1.3 through Meta’s developer API, with its web_search tool on. The model decides when to search. It returns its sources as citations.
• The questions: 5 buyer questions per site, the same daily set the other four assistants get. 15 answers in all.
• Follow-up probes: 9 extra questions on the same day, to check how Muse handled the businesses by name.
• The cost: about $1.54 for the day.

The three sites:

1. Daily Dumplings (dailydumplings.com), a one-person frozen dumpling business in Hollywood, Florida. It is also our first case study.
2. A one-person sticker shop on Etsy, with its own small website. It is mine, which is why it is in the test.
3. Heard of You (heardofyou.io), this site.

Results by site

Daily Dumplings: named when the question matched its site

When Muse won, it was because of the website. It opened dailydumplings.com and repeated the delivery zones, prices and pickup times correctly. Asked about the business by name, it got every fact right.

The two misses are telling. The Fort Lauderdale and khinkali answers were built from delivery apps and restaurant guides, where a home kitchen does not appear. The site sells khinkali, but it had no page about them, so Muse treated the question as a restaurant question.

The sticker shop: quoted, but credited to Etsy

This was the strangest result. Muse quoted three of the shop’s own listings word for word, prices included. It found them on Etsy’s search pages and gave the credit to “Etsy”.

A probe confirmed it. Asked who sells one of those exact sticker sheets, Muse named the shop correctly. So Muse knows the shop exists. It just never sees the shop’s name while answering a category question, because the listing titles do not contain it.

Muse never opened the shop’s own website for a category question.

Heard of You: not in the pages Muse read

Asked about Heard of You by name, Muse read heardofyou.io and summarised it correctly. Asked buyer questions, it named other vendors, found through their own pricing pages and through “best AI visibility tools” lists. It quoted prices word for word.

Three of our five questions were “how do I get recommended” questions. Muse answered those with a list of steps and named no vendor at all, ours or anyone’s.

What Muse searched

Across the day, Muse made 104 web searches, opened 56 pages and saw about 1,000 search results.

• Daily Dumplings questions: 390 results. The most common sources were dailydumplings.com (26), guide.in.ua, a Ukrainian diaspora directory (24), DoorDash (23) and Postmates (15). Then ordering pages, MapQuest, Wanderlog, restaurant directories and food guides.
• Sticker questions: 134 results, almost all marketplaces: Etsy (38), Redbubble (21), TeePublic (11).
• Heard of You questions: 335 results from 238 different sites, spread thin across vendor blogs, Medium and dev.to.

What was missing matters too. In about 1,000 results we saw no Reddit, almost no Yelp, no Google Maps and no Facebook.

When we asked about Daily Dumplings’ Instagram, Muse opened the public Instagram page like any other web page and said it could not see much without logging in. It did not pull anything from inside Meta’s apps.

What Meta says about its crawlers

Meta’s crawler documentation lists several user agents. The ones that matter here:

All three of our sites allow every crawler (User-agent: * with Allow), so meta-webindexer was already let in.

Where the search results come from is less clear. Meta has not named a search provider for 2026. In October 2024, Meta AI used Google and Bing, and Meta was reported to be building its own crawler and index. Since December 2025, paid deals with publishers supply news.

Our results looked more like a separate index than Google’s: lots of low-quality pages that Google usually filters out, and only one of about 1,000 result links carried a Google shopping tag. That is an inference from one day, not proof.

The app can do more than what we measured

We measured the API. The Muse app, launched in September 2026, can also draw on accounts you follow on Instagram, Facebook and Threads. It can shop through partner retailers and book tables. None of that shows in our numbers.

So a Meta presence still matters for real app users. It just did not decide anything in the answers we could see.

What we are changing

Only things that are true and that we can do on our own sites:

• Daily Dumplings now has a khinkali page and a Fort Lauderdale and Broward delivery page, in English, Russian and Spanish, built only from facts already on the site.
• Heard of You rewrote its pricing page so the prices and the words buyers use (“one-time”, “done-for-you”, “for solo founders”) sit in plain sentences. We also published an honest Peec AI alternatives comparison.
• The sticker shop is considering putting its name in a few Etsy listing titles. If it does, we will check after two weeks whether Etsy views drop.

No fake reviews, no paid placements dressed up as editorial, no new accounts pretending to be customers.

Limits

• One day: 15 answers and 9 probes. Muse’s answers change from run to run, so these are early signals, not rates.
• Three sites, all run by me or my family. That is why we can share every number, and also why it is a small sample.
• The API model only. The consumer app may answer differently.
• Nothing here says why Muse chose a source, only which sources it chose.

We will re-run the same questions daily and publish the 30-day picture in December. The data on this page is free to reuse under CC BY 4.0 with a link to this page.

We ask ChatGPT, Claude, Gemini, Perplexity and Google’s AI Overviews and AI Mode 27 fixed questions. They are questions a founder might ask when looking for a service like ours. Each runs three times, and we record how often we are named.

The October 2026 baseline has not run yet. We expect it at or near zero. The site went live on October 31 2026, and no third-party page mentions us yet. The table below has one row so far.

Method

• Prompt set: 27 prompts, fixed. Prompts 1 to 25 are “Heard of You’s own competitor prompts” from the research we launched the site on, unchanged. We added 26 and 27 on October 31 2026, for the two closest competitors missing from the original set. A prompt is never edited. New ones get new numbers.
• Engines (5): ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews / AI Mode. We weight Claude and ChatGPT when reading results. Founders pay for Claude more than any other assistant (59%, Supabase) and use ChatGPT most (72.1%, Lenny’s Newsletter).
• Runs: 3 per prompt per engine, in a fresh, signed-out chat, with a United States location set. Location changes answers (Chen and Koudas, 2026). We record the model version shown. 27 x 3 x 5 = 405 answers per full run.
• Why three runs: the same prompt returns the same brand list less than 1 time in 100 (SparkToro). One answer is not a measurement.

Cadence

What we record for every answer

• Named (yes or no)
• Recommended versus only cited
• Position words (“top pick”, “also worth a look”)
• Competitors named
• Sources cited, with URL
• Whether one of our own pages was cited, and which
• Tone
• For the branded prompts 23 to 25: whether what the assistant said about us (prices, what we do) was accurate

The headline number is the mention rate: the share of answers that name us, per engine and overall. We never report a rank.

Results

Each monthly row links to its raw run log (CSV, CC BY 4.0) once published.

The 27 prompts

Shapes follow how real people prompt: short, with “best”, a budget or a personal detail (Stella Rising). ★ marks the five weekly-pulse prompts.

The competitors in these prompts are real companies. They are there only because a founder comparing services would name them. We have no relationship with any of them.

What we expect, honestly

Near zero at baseline on prompts 1 to 22. No third-party page mentions us yet. Absent from the assistant’s live search almost always means absent from the answer.

On the branded prompts 23 to 25, we expect the assistants to either not know us or to describe the homepage. What we check is whether what they say is accurate.

The interesting part is the monthly change. Does it track what we do: the research we publish, the places that write about us, the reviews from pilot clients? If it does not move, that goes on this page too.

What to do next

Run the same method on your own product. The do-it-yourself guide walks through it. The prompt library has the templates. The free check does the first five prompts for you.

Why bother? It is the evidence behind what we sell. Nobody with an indie-founder audience publishes it. And original numbers are what other people quote, which is exactly what we help clients earn.

The first study is in progress and publishes the week of November 16 2026.

What we publish, and why

Every study on this page follows the same rules:

• A fixed, published prompt list, so anyone can repeat the runs.
• Every prompt run three times per assistant, because single answers do not repeat (SparkToro: the same brand list less than 1 time in 100).
• Mention rates, never ranks.
• Raw data as a downloadable CSV, with the assistant, model version, date, prompt, run number, products named and sources cited.
• Data collection dates and model versions shown on the page, and a limits section that says what the study cannot show.
• CC BY 4.0 on all of it.

A study costs hours, not money. One assistant query costs us roughly five cents in API fees. A 600-answer study is about $27 of compute and several days of reading answers.

Flagship study: do AI assistants recommend small products?

Status: in progress. Publishing the week of November 16 2026.

We asked 4 AI assistants (ChatGPT, Claude, Gemini and Perplexity) 600 buyer questions across 10 indie software categories. That is 50 real buyer questions, 5 per category, each run 3 times on each assistant.

The question is the one every solo founder asks first: does ChatGPT recommend indie or small products at all, or only the big brands?

The 10 categories. In each one, solo founders compete with incumbents:

1. Invoicing and finance for freelancers
2. Project management for small teams
3. Note-taking and personal knowledge
4. Email marketing for creators
5. Scheduling and booking
6. Form builders
7. Screen recording and screenshots
8. AI writing assistants
9. Habit and focus trackers
10. Website builders for indie makers

What the study will report, per category and per assistant:

• How often a small, independent maker’s product was named at all, against the share of answers that named only incumbents.
• Recommended versus merely cited.
• Which kinds of pages were cited: “best X” lists, Reddit, review sites such as G2 and Capterra, YouTube, vendor sites, directories. That includes the answer to “do directories help?”, which gets its own page in month two.
• How much the product list changed between the three runs.

What it cannot show: why a product was named, whether being named led to a sale, or anything about categories outside these ten. The study page prints those limits.

The prompts come from the prompt library template. The exact 50 are published with the data. The dataset will be a CSV under CC BY 4.0, with a short method page and a notebook on GitHub.

First Muse data

On November 1 2026 we added Muse, Meta’s AI assistant, to our daily checks. Day one, across three small sites: one was named in 3 of 5 answers, two in none. What it searched, what it cited and what Meta says about its crawlers: What Meta’s Muse recommends: first data from 3 small sites.

Our own visibility log

Every month we run our own test prompts against ourselves and publish the results, good or bad: Heard of You visibility log.

Our first client case study starts at day 0, published before the work: a home dumpling business, named in 5 of 20 answers on day 0.

It starts near zero, because the site did not exist until October 31 2026. That is the point. A service that sells AI visibility should show its own.

Coming later

Data licence

All original data and charts on this site are published under Creative Commons Attribution 4.0. Reuse, adapt and republish, including commercially. Credit “Heard of You (heardofyou.io)” with a link to the study page. Third-party figures we quote belong to their authors and link to the original.

Third-party research we rely on

We did not discover how AI recommends products. These people did. Our guides and methods lean on the studies below most.

Vendor studies are marked. They come from companies that sell AI visibility, SEO or PR tools, so they have a commercial interest in the result. They tend to be large and correlational.

Peer-reviewed and academic

• Tannenbaum, 2026 (pre-print): brands absent from the assistant’s live search were named 2.8% of the time; with their site cited, 91.4%.
• Chen et al., University of Toronto, 2025: 72.7% of AI sources for US software questions were earned media.
• Chu and Hou, 2026 (pre-print): identical specs, the known brand wins 100%; a rating edge under 0.1 stars flips it.
• Aggarwal et al., KDD 2024: citations, quotations and statistics raised visibility 30 to 40%; keyword stuffing did not.
• C-SEO Bench, NeurIPS 2025: most content tricks were ineffective; being first in the model’s context mattered most.
• Columbia, What Is Your AI Agent Buying?: ratings, review counts, badges and position drive agent picks.
• Wharton GAIL, 2026: a single Wirecutter or Reddit source can override a model’s pick.

Industry and vendor studies

• SparkToro: the same prompt repeats the same brand list less than 1 time in 100.
• AirOps and Kevin Indig: 58% of position-1 pages cited, 14% at position 10; 44.2% of citations from the first third of a page.
• Muck Rack, October 2026 (vendor): over 84% of 207 million citations were earned; 0.3% paid.
• Ahrefs, 75,000 brands (vendor): YouTube mentions correlate most with AI visibility.
• Ahrefs, best lists (vendor): listicles were 43.8% of cited page types.
• Peec (vendor): most-cited domains per engine; Reddit and YouTube in every top five.
• GetIntel (vendor): directories rarely cited across 9,978 answers.
• SE Ranking and Ahrefs (vendor): llms.txt shows no correlation with citations; 97% of files got zero requests.

Who uses which assistant

• G2, The Answer Economy: 51% of software buyers start in an AI chatbot more often than Google.
• McKinsey: 53% of about 21,000 consumers have used AI for shopping.
• Supabase, State of Startups 2026: Claude is the top paid assistant among startup builders (59%).
• Stella Rising via Search Engine Land: how real people phrase prompts (n=524).

What to do next

Want to know where your own product stands before the study lands? The free check applies the same method to you: 5 buyer questions, 3 runs, 5 assistants, results in 2 working days.

Writers and researchers who want early access to the flagship data: email hello@heardofyou.io.

What does not work, or cannot be sold honestly: llms.txt on its own, schema on its own, keyword stuffing, bulk directory submissions and hidden text. The evidence for each is below, with vendor studies labelled.

What works

1. Get named on pages you do not control

AI answers lean on earned and third-party media. For US software questions, 72.7% of cited sources were earned media. A normal Google results page: 45.4% (Chen et al., University of Toronto). Across 207 million citations, over 84% were earned or third-party and 0.3% paid (Muck Rack, vendor).

“Best X” lists alone were 43.8% of the page types ChatGPT cited, across 26,283 source URLs. Recently updated lists were most prominent. Appearing high on several lists correlated with being recommended (Ahrefs, vendor, correlational).

Which pages? Reddit and YouTube are in every engine’s top five cited domains. G2 is in Perplexity’s (Peec, vendor). Across 75,000 brands, YouTube mentions had the strongest correlation with AI visibility (about 0.737). Branded web mentions came next (Ahrefs, vendor).

On LinkedIn, long articles were 50 to 66% of cited LinkedIn content. About 95% of cited posts were original, not reshares (Semrush, vendor).

The pattern: other people, writing specifically, in their own names.

2. Collect honest reviews, because small edges beat big names

With identical specs, the well-known brand won 100% of the time. A rating edge under 0.1 stars for the smaller competitor flipped it (Chu and Hou, 2026, pre-print).

In Columbia’s study of shopping agents, +0.1 in rating moved a 10% pick rate to 15 to 20%. A reviewer “pick” badge took it to 20 to 43%. A “Sponsored” tag dropped it to 8 to 9%.

A single Wirecutter or Reddit source could override a model’s favourite (Wharton GAIL). Social proof raised recommendation rates in controlled tests. Scarcity and “exclusive” cues lowered them (Bias Beware, EMNLP 2025).

3. Show up in the assistant’s live search

Brands absent from the assistant’s web results were mentioned 2.8% of the time. With their own site cited plus branded searches: 91.4% (Tannenbaum, 2026, pre-print). Inside those results, position 1 was cited 58% of the time, position 10 only 14% (AirOps and Kevin Indig).

In C-SEO Bench (NeurIPS 2025), simply being first in the model’s context beat every rewriting trick. So ranking in ordinary search for “best [category] 2026” is still real work. Not old-fashioned work.

4. Put a clear answer in the first third of the page

44.2% of citations come from the first third of a page. Definite wording gets cited more often (AirOps and Kevin Indig).

In the GEO experiments on 10,000 queries, specific numbers, quotations and named sources raised visibility by 30 to 40%. Lower-ranked pages gained the most (Aggarwal et al., KDD 2024).

This is the one on-page tactic with experimental support. It boils down to: say plainly what you are, back it up, do it near the top.

5. Show real dates on real updates

Models favour newer dates. A more recent publication date moved passages up in all seven rankers tested, by as much as 95 places (Fang et al.). Cited content is fresher than average (Ahrefs).

This only works when the date is honest. Bumping the date on an unchanged page is manipulation. If a model ever learns to check, it will cost you.

Our rule: a visible date, changed only when the content changes, logged in the changelog.

What doesn’t work

llms.txt on its own

llms.txt is a plain-text file listing a site’s important pages for AI agents. It is a reasonable courtesy and takes an hour. It is not a visibility lever.

• SE Ranking found no correlation between having one and AI citations, across about 300,000 domains. Their model got more accurate without it (vendor).
• Ahrefs’ server logs: 97% of llms.txt files got zero requests in May 2026 (vendor).
• Google says it does not use llms.txt for Search or AI features.

We have one at /llms.txt because agents can use it. We will publish our own server logs on whether anything reads it. Anyone whose deliverable is an llms.txt file is selling a placebo.

Schema markup on its own

Structured data (JSON-LD) describes what a page is: an article, an organisation, a product with a real price. Use it accurately.

No evidence shows it makes an assistant cite or recommend you. Google says no special AI markup exists. Invented ratings in markup are useless and against Google’s rules (Search Engine Land’s GEO myths review; Google’s documentation). Markup that matches the visible page is hygiene, not strategy.

Keyword stuffing

It did not help in the GEO experiments. Citations, quotations and statistics did (Aggarwal et al., KDD 2024). Most AI-specific content tricks did little in C-SEO Bench, and every trick’s gain shrank as more competitors used it.

When every brand applied the same optimisation in Chu and Hou’s tests, the benefit collapsed from +0.802 to +0.007.

Directory blasts

Services that submit you to 100 or 500 directories sell volume. ChatGPT and Gemini rarely cite directories. In GetIntel’s 9,978 answers they were a minor source next to lists, reviews and community threads.

A directory may still give you a backlink for classic SEO. It will not get you recommended. We never sell them.

Hidden text, prompt injection, fake reviews

These do work in the lab. Hidden text and prompt injection pushed a low-ranked product to the top, including on Perplexity (Kumar and Lakkaraju; Pfrommer et al., EMNLP 2024).

That is exactly why they are off the table. They are manipulation, they get found, and they bet the founder’s name on a trick that dies at the next model update. Our clean methods page lists everything we refuse to do.

The honest summary

Limits

Most of the “works” evidence is correlational. Much of it comes from companies that sell AI visibility tools (Ahrefs, Muck Rack, Peec, Semrush, SE Ranking). The experiments ran on specific models at specific dates. Effects shrink as tactics spread.

None of this lets anyone promise what an assistant will say about you.

What to do next

Find out which third-party pages your assistants cite for your category. That list is the whole to-do list.

The free check produces it: 5 buyer questions, 3 runs each, 5 assistants. You get who was named instead and the sources cited, within 2 working days. Or run it yourself with the do-it-yourself guide.

So I built a small agent. Every morning it visits a handful of pages, asks Claude what changed since yesterday, and emails me only when it matters. It costs about $7.80 a month. In two weeks it logged 41 changes across three sites. 5 were worth knowing about.

Here is how it works, and how to point it at what actually moves AI recommendations: the sources behind your competitor.

What the agent does each morning

1. 6:00 am. A cron job on a $6 server starts a headless browser.
2. Capture. It visits each watched page and waits for it to settle. It takes a full-page screenshot and saves the title and visible text. Three pages take about 12 seconds.
3. Store. Each capture goes into a snapshots table, screenshot in storage.
4. Diff. Claude gets yesterday’s text and today’s. It returns one paragraph of at most four sentences, ending with a significance score from 1 to 5.
5. Log. The summary, the categories it touched and the score go into a diffs table.
6. Notify. At about 7:30 am, an email goes out for anything that scored 3 or more. Everything else waits in the table for when I am curious.

The prompt lists what to watch for: pricing changes, new products, new linked pages, removed features, positioning shifts, sign-up flows, new social proof.

It also carries an ignore list, and the ignore list does 80 percent of the work. Cookie banners, dynamic counts, A/B copy swaps, randomised testimonials, the footer year, ads. Adding it cut false alerts roughly in half.

I used a real browser, not a plain fetch. Most sites now draw their pricing tables and comparison blocks with JavaScript. A fetch sees an empty box. A headless browser sees what a buyer sees, for roughly half a cent a page.

What it costs

On a free build runner it would be nearer $2 a month. I keep the server because it does other jobs.

What it caught in two weeks

I watched three small business sites in a category I was curious about. Of 41 changes, 5 scored 3 or higher:

• A price test in public. One site moved its middle tier three times in eight days: $24, $28, $26. The $26 stuck. That is the number their data supported.
• A quiet move upmarket. One site added a “for teams” page, linked from the footer and indexable, with no announcement. An assistant will read that page next time someone asks for a team tool.
• A swapped page. One site removed its “about” page and put shipping and returns in its place. Nobody announces that either.

The other 36 were footer tweaks, copy tests, image swaps, a font experiment and a payment-icon change.

The surprise was how often small businesses change things. My own site, at the time, would have produced zero alerts. That was the real finding: a site that never changes gives an assistant nothing new to read.

Point it at what AI reads

Watching a competitor’s homepage is interesting. Watching the pages that made the assistant choose them is useful.

Our free check records, for every answer, who was named instead and which sources were cited. That source list is what to watch:

• The “best X” lists that name your competitor. When the editor updates one, you want to know that week. That is when a pitch lands.
• The review profiles the assistant cites. New reviews, a changed rating, a new category tag.
• The community threads it keeps pulling from. A thread with a new answer naming a product is one you can answer honestly, under your own name.
• Your competitor’s comparison and alternatives pages. Assistants quote “X vs Y” and “alternatives to X” pages often, and competitors add them quietly.

Set the score threshold at 3, keep the ignore list, and let the table fill up. Once a month, re-run the same buyer questions and see if the mention rate moved. We explain the counting on how we measure: named in X of 60 answers, never a rank.

Be a polite robot

I only look at pages a normal visitor can see. No logging in. No scraping behind a paywall. No fake sign-ups to read a welcome sequence. No buying and returning to see the checkout.

If you would not do it by hand, do not automate it. The same rule runs through our clean methods.

A first version of this appeared on my studio blog, dashbuilds.dev.

What to do next

If a competitor keeps getting named, start with the sources, not the agent. A competitor keeps getting named instead explains how we find the pages behind them and get you onto the same ones.

The free check produces the list to watch: 5 questions, 3 runs, 5 assistants. You get who was named instead and what was cited, by email within 2 working days.

I learned this by building a small business app for my mom. Over five working sessions, it turned into nine products.

Here is that process. And the sixth step I did not know about then: making the product something ChatGPT, Claude, Gemini or Perplexity can name when someone asks.

It started as an app for my mom

My mom makes dumplings for a living. She ran the business out of notebooks and chat messages.

I built her an app: receipts, invoices, freezer inventory, and an assistant she could talk to in Russian. It worked because she used it every day and told me when it did not.

It became a product the day I noticed almost nothing in it was about dumplings. Receipts, invoices, inventory and a chat assistant are universal. Only the branding and a few hard-coded words were hers.

The five sessions

1. Remove the hard-coded parts. Every reference to her business became a setting. One session, mostly search and replace, plus a settings screen.
2. A setup wizard. Seven steps on first run: business name, what you sell, currency, tax, language, the basics. The wizard turns “my mom’s app” into “an app”.
3. Teach the assistant from the database. Her business was written into the AI prompts. Now they read the business settings at run time. The AI features got better, because the context was explicit instead of assumed.
4. Feature flags. With features behind switches, one codebase could split into smaller products that each do one job.
5. Package. A README, a setup script, a build script and a clean bundle for each product.

One codebase, nine products. Every bug fix lands in all nine. That part still feels like a trick.

Five tests before you productise anything

• It solves a workflow problem, not a curiosity.
• Someone already uses it and would notice if it stopped.
• The domain-specific parts are thin.
• The tech stack is one other people use.
• You can explain the value in one sentence.

That last test matters more than I knew. It is not just a pitch test. It is the sentence an assistant needs.

The sixth step: make it nameable

The five sessions gave an AI assistant no reason to know the products existed.

Assistants decide what to recommend by searching the web, reading what other people wrote, and breaking ties for names they already know. A freshly packaged product has none of that.

So here is the sixth step, in the order I would do it now.

Pick the category word. Buyers ask for “an invoicing app” or “a freezer inventory tool”, never for your clever name. If your product does not sit in a category a buyer would type, the assistant has no question to match you to. Choose the plain word, even if it feels small.

Write the one sentence, and put it at the top. “[Name] is a [category] for [who], that [one thing].” On the homepage, first paragraph, as text, not an image. That is the sentence an assistant quotes. Our guide on why AI doesn’t recommend your product has a test for it.

Make the site readable. A page drawn entirely by JavaScript, or blocked to crawlers, is invisible to an assistant. Our AI crawler check tells you in a minute.

Get three honest mentions.

• A review from the real person you built it for.
• A thread where you answer, under your own name, a question your product solves.
• One list or comparison page that names you.

Three is not many. It is the difference between zero third-party pages and some. What works and what doesn’t shows why this beats anything on your own site.

Then measure. Ask each assistant five buyer questions, three times, and count how often you are named. A rate, not a rank. The method is on how we measure. The do-it-yourself version is how to check if ChatGPT recommends you.

Expect zero the first time. The point is a number to beat at day 30.

What I would tell the me with the dumpling app

Build it for one person. Notice when the specific parts are thin. Split it only if the split is clean.

And before you spend a weekend on packaging, spend an hour on the category word, the sentence and the first three mentions. Packaging makes a product real to you. Mentions make it real to an assistant.

A first version of this appeared on my studio blog, dashbuilds.dev.

What to do next

Is your side project already a product? Find out whether any assistant knows it exists. The free check runs 5 buyer questions, 3 times each, on ChatGPT, Gemini, Claude, Perplexity and Muse. It shows who was named instead and which sources they cited.

Built it with AI coding tools and wondering what comes next? The vibe coders page is written for you.

1. It runs a few web searches of its own.
2. It reads what other people wrote about the options: lists, reviews, forum threads. Far more than vendor sites.
3. It writes an answer that leans toward brands it already knows. Small review and rating edges decide the close calls.

Your product gets named when it shows up in those searches and on those third-party pages. Below is each step with the evidence, and what it means for a small product.

Step 1: it usually searches first

Modern assistants do not answer product questions from memory alone. In one data set, ChatGPT searched the web in 31% of prompts. That was about 2.17 searches per prompt, averaging 5.48 words each. “Reviews”, “comparison” and the current year were common words (Nectiv, via Search Engine Land). For software questions, 76% of ChatGPT’s searches added a year, as in “best invoicing app 2026” (StatWharf).

One finding matters more than any other. In Tannenbaum’s 2026 study, a brand missing from the assistant’s live search results got named by GPT 2.8% of the time. When the brand’s own site was cited and branded searches happened too, 91.4%.

Being in the material the assistant pulls in is most of the game.

Ordinary search rank still counts inside that. Across 815,484 query-page pairs, 58% of pages at position 1 got cited, against 14% at position 10 (AirOps and Kevin Indig).

The same study found that covering lots of related sub-topics (“fan-out coverage”) barely mattered. Rank mattered. Breadth did not.

Step 2: it reads what other people say about you

AI answers lean hard on pages the brand does not control. For US software questions, 72.7% of cited sources were earned media: reviews, articles, lists by third parties. A normal Google results page: 45.4% (Chen et al., University of Toronto). Across 207 million citations, Muck Rack found over 84% came from earned or third-party sources. Paid content: 0.3%.

Which third parties?

• Reddit and YouTube are in every engine’s top five cited domains.
• ChatGPT leans editorial: Wikipedia, Forbes, TechRadar.
• G2 is in Perplexity’s top five but not the others (Peec, a vendor study).

Across 75,000 brands, YouTube mentions had the strongest correlation with AI visibility (about 0.737). Branded web mentions came next (0.66 to 0.71). Domain authority was weak (0.27) (Ahrefs, a vendor study).

OpenAI describes what ChatGPT uses for shopping: the query plus memory, structured product data, third-party content, and price, availability and reviews. It says product results are not ads.

Step 3: ties go to the brand it already knows

With identical specs, the well-known brand was recommended 100% of the time (Chu and Hou, 2026). That is the incumbent’s advantage, and it is real.

The same study found what breaks it. A rating edge of under 0.1 stars for the smaller competitor flipped the pick.

A Columbia study of shopping agents found the same lever:

• A rating up by 0.1 took a product from a 10% choice rate to 15 to 20%.
• An “Overall Pick” style badge raised it to 20 to 43%.
• A “Sponsored” tag pushed it down to about 8 to 9%.

Agents also favoured products listed first.

One trusted source can override the model’s favourite. In about 26,000 Wharton tests, one Wirecutter or Reddit source changed the pick. The order of the sources mattered too.

Step 4: wording and dates move things too

Small things in the question change the answer. Adding “I love hiking” redirected Claude Opus in three-quarters of runs (Wharton GAIL). Asking for “the best” option nudged models toward AI-flavoured picks by 8 points in one study.

Models also favour newer dates. A more recent publication date moved passages up in all seven models tested, by as much as 95 places (Fang et al.). Where a candidate sits in a list changes whether it gets picked (Bito et al.).

The honest reading: recency and position are biases, not strategies. A real update date on a page you really updated is fine. Faking dates is manipulation, and this site does not do it.

Step 5: the answer changes every time you ask

Two runs of the same prompt gave the same brand list less than 1 time in 100. The same list in the same order: about 1 time in 1,000 (SparkToro, 2,961 responses).

Category leaders still showed up in 55 to 100% of answers. The wobble is at the edges of the list, which is exactly where a small product lives.

Across 536,301 answers, ChatGPT, Claude and Gemini all named the same company only about 3% of the time (BusySeed, via IPS).

So one screenshot of ChatGPT naming you (or not) means very little. That is why we never report a rank. We run each question three times on each assistant and report how often you are named. See how we measure.

What this means if you are small

1. Get into the assistant’s live search results. Your own site for your category’s queries, and the third-party pages that already rank for them.
2. Earn mentions on the sites each assistant cites for your category: “best X” lists, review sites, Reddit threads, a YouTube walkthrough.
3. Collect honest reviews. A fraction of a star can beat a bigger name in a close call.
4. Make your homepage say what it is and who it is for on the first screen. The assistant and the buyer both check there.
5. Measure with repeated runs, not one answer.

Limits of this evidence

Several of the strongest results (Tannenbaum, Chu and Hou) are 2026 pre-prints, not yet peer reviewed. Ahrefs, Peec and Muck Rack sell AI visibility tools or PR software, so their studies are labelled vendor studies here. They are large but correlational.

Models change often, and any figure can shift with the next update. Nothing on this page lets anyone promise what an assistant will say.

What to do next

Run the check. Our do-it-yourself guide shows the exact method. Or we run it for you free: 5 buyer questions, 3 runs each, on ChatGPT, Gemini, Claude, Perplexity and Muse. Results by email within 2 working days.

Already know you are missing? Read why AI doesn’t recommend your product.

I built one page defining 20 terms in my field. In two weeks:

• search impressions went from about 30 to 120 a day,
• clicks went from about 1 to 5 a day,
• distinct queries I appeared for went from 11 to 47.

Here is what I did, what I would change, and why it matters if you want ChatGPT, Claude, Gemini or Perplexity to name you.

Why a definitions page matters for AI

For most product questions, assistants search the web first. They split one question into several smaller searches.

A buyer types “best invoicing app for freelancers”. The assistant runs the searches you would expect. It also runs “what is recurring invoicing” or “what does net 30 mean”. Those are glossary questions.

If your site answers them clearly, the assistant may read you on its way to the recommendation. If not, it reads a competitor’s page. That competitor becomes the brand it already knows.

A glossary catches a wide spread of that vocabulary on one page. No single blog post can. That was the whole trick, and it is not clever. It is a page that answers the small questions plainly.

What I built

I opened my search console, sorted by impressions, and filtered for queries with zero clicks. These were questions I already appeared for but was not winning: “what is an MCP server”, “what is pgvector”, that sort of thing. No page of mine defined any of them. So I made one.

One HTML page, 20 terms, one afternoon. Each entry runs about 120 words, in four parts:

1. The term and a one-line definition. This is what a search engine or an assistant quotes. Write a complete sentence that stands alone.
2. “What it actually means.” About 80 words of plain English, as you would explain it to a smart friend.
3. “You would care about this if…” One line on who needs this and why. It keeps the reader on the page. It also tells an assistant which buyer the term belongs to.
4. A link to the page on your site that uses the idea: a guide, a comparison, your product page.

Every entry has its own anchor, so other pages can link straight to a definition. I added DefinedTermSet and DefinedTerm markup. I added FAQPage markup too, since half the entries were questions anyway.

What happened

I wrote these down as ratios, not screenshots. The baseline is small, so read them as a direction, not a law.

The five new indexed pages were the glossary plus four single entries. Google chose to show those as their own results. Each entry started acting like a small page of its own. I did not expect that, and it explains the jump in queries.

What I would change

• Start from queries you already get impressions for. They show the words buyers actually use. Your marketing words are not on that list. Keep them off the glossary too.
• Add 5 to 10 entries at a time. Search engines index anchors slowly. Batches show you which entries get picked up, so you can write more like those.
• Link first mentions. Whenever a guide uses a term, link it to the glossary anchor. That gives the assistant a path: question, definition, your product.
• Write the “you would care if” line first. If you cannot write it, nobody is asking about the term. Cut it.
• Do not chase uniqueness. Your “net 30” will read like everyone else’s. Clear beats clever. The link to what you built is what makes it yours.

What does not work

A keyword-stuffed glossary is a 2014 trick, and nothing rewards it now. Each definition has to be a real, useful one, or the page is just a list of words.

The markup is housekeeping, not strategy. No evidence shows schema alone makes an assistant cite or recommend a page. We say so in what works and what doesn’t. Add the markup, match it to the visible text, and spend the real effort on the sentences.

Ours

We practise this. Our own glossary defines AI visibility, GEO, mention rate, fan-out, grounding, earned media, llms.txt and the rest. Each has a one-line definition, a plain paragraph and a line on who should care.

We log whether assistants ever cite it in our visibility log. It starts near zero and is published unedited.

A first version of this appeared on my studio blog, dashbuilds.dev.

What to do next

Before you write a glossary, find out which questions assistants already answer about your category, and who they name. The free check runs 5 buyer questions, 3 times each, on ChatGPT, Gemini, Claude, Perplexity and Muse. It shows which sources they cited. The words on those pages are your first 10 terms.

Five questions, three runs, five assistants: 75 answers, about 75 minutes. Your result is a mention rate (named in X of 75), not a rank. This is the exact method behind our free check. Do it yourself today, or let us do it.

Before you start

• Sign out, or use a fresh account. Signed-in assistants use memory. Yours already knows your product, so it will flatter you.
• Start a new chat for every run. No follow-ups in the same thread. The earlier answer biases the next.
• Set your buyers’ location. ChatGPT follows the location the app reports. Gemini follows the network’s country. Recommended businesses change with location (Chen and Koudas, 2026). Selling to the US? Test from a US location or a US VPN.
• Note the date and the model name shown on screen. Models change. Next month’s comparison is only fair if you know what you tested.
• Use the web-enabled modes: ChatGPT with search, Perplexity by default, Gemini and Claude as they come. That is what buyers use.

The five question shapes

Real buyer prompts are short (Stella Rising, n=524):

• two-thirds are 15 words or fewer,
• about a quarter use the word “best”,
• 28% name a price limit,
• 32% say something personal about the asker.

The five shapes below cover that mix. Write one prompt per shape, in the plain words your customers use. Not your marketing words.

Shape 5 matters most for small products. It shows whether the assistant brings you up when nobody named your category. Shape 4 shows whether you appear next to the incumbent at all.

The full 25-prompt version, five per shape, is in our free prompt library.

Run each prompt three times on each assistant

Open ChatGPT, Gemini, Claude, Perplexity and Muse. For each prompt: new chat, paste, read, record. Then twice more. That is 5 prompts x 3 runs x 5 assistants = 75 answers.

Why three runs. Two runs of the same prompt gave the same brand list less than 1 time in 100 (SparkToro, 2,961 responses). One answer is noise. Three per assistant is still small, but it tells “never” from “sometimes” from “usually”. And you can repeat it in a month.

Why five assistants. Across 536,301 answers, ChatGPT, Claude and Gemini all named the same company only about 3% of the time (BusySeed). If your buyers are startup builders, Claude matters: it is the most-paid-for assistant among them. If they are consumers, Gemini and ChatGPT matter most, and Muse sits inside WhatsApp, Instagram and Facebook.

What to record for every answer

Make a sheet with one row per answer and these columns:

The sources column is the most valuable thing you will produce. Across 207 million citations, over 84% came from third-party sources (Muck Rack). The pages cited when you are not named are the pages you need to be on.

How to read your result

Add up the “Named?” column. That is your mention rate: named in X of 75 answers, and X of 15 per assistant.

Then read what the assistant said about you when it did name you. Buyers check. 89.9% of AI shoppers verify AI advice with another source before buying (Product.ai). 94% of B2B tech buyers fact-check it (TrustRadius). A wrong price or audience costs you the buyer who looked you up.

Be honest about the time

Sixty answers, recorded properly, take about an hour. That assumes you type fast and do not get absorbed in the answers. Many founders get to 20 and stop. Fine: 20 answers beat one screenshot. Just write down that you stopped, so next month’s comparison is fair.

Keep the prompt set fixed and run it again at 30, 60 and 90 days. Change the prompts and you will not know whether you improved or just asked differently.

Limits

This is a sample, not a census. Fifteen answers per assistant show a pattern. They cannot measure a 5% change. Assistants also personalise, and vary by location and model version, so your numbers and a friend’s will differ.

Treat the mention rate as a thermometer, not a scoreboard. Our paid audit uses the same method with 25 prompts instead of 5. More answers, same honesty.

What to do next

Rather not spend the hour? We run this check for you, free: 5 buyer questions, 3 runs each, on ChatGPT, Gemini, Claude, Perplexity and Muse. 75 answers, results by email within 2 working days, and a person checks every report.

Ran it yourself and your number is low? Read why AI doesn’t recommend your product to find the cause.

For two weeks, Claude pre-drafted every support email and I reviewed each draft before it went out.

• Average response time fell from 14 hours to 12 minutes.
• 62 percent of drafts went as written, 23 percent lightly edited, 15 percent rewritten from scratch.
• Thank-you replies went from about 2 a week to about 5.

Below: the setup, the rules, and the three emails that taught me the rules. Then why it matters if you want an assistant to recommend you.

Why support is an AI visibility question

Assistants learn what a product is like from pages other people write: reviews, community threads, comparison posts. Customers write those pages after an experience with you. For a small product, that usually means after a support email.

A fast, honest, specific reply becomes a four-star review on the site the assistant cites. A confident wrong answer becomes a thread titled “does X work with Y? (it does not)”. The assistant finds that thread every time someone asks.

Buyers check, too. In Product.ai’s survey, 89.9 percent of AI shoppers verify the assistant’s advice with another source before buying. TrustRadius found 94 percent of B2B buyers fact-check AI research.

Where do they check? Reviews, and your replies to those reviews. Support sits upstream of everything an assistant will later say about you.

The setup

Every incoming support email fires a webhook. A small function bundles the email, my notes and a stored system prompt, and sends them to Claude. The result lands as a draft in my inbox. I read it, then send, edit or rewrite. Nothing goes out without me.

What the model worked from:

• the full product list, with prices and what each includes
• a FAQ of about 3,000 words
• the refund policy, one paragraph
• about 80 past replies to similar questions
• any notes on that customer

What it did not have: permission to send, and permission to invent.

The rules, in order

1. Voice. Lowercase, warm, brief: a friendly person who built the thing herself. No corporate phrasing. No em dashes.
2. Emotional emails. If the customer is angry, frustrated or grieving, write one line of acknowledgement, stop, and tag it for me.
3. Refunds. Quote the policy and offer the refund. Nobody should have to ask twice.
4. Unknowns. Say so. Never invent a feature.
5. Sign-off. Always my name.
6. Length. Four short paragraphs at most, ideally two.
7. Status. Every reply is a draft for review.
8. Added in week two. Unsure whether the product supports a specific tool or version? Write “I think yes but let me double-check” and tag it.

Rule 2 was the most important, and it came late. The model had answered a bereaved customer in a chirpy tone. Rule 8 came from the third email below.

Three emails

The refund it talked someone out of, honestly. A customer bought a product, then realised they used a different editor than the one it was built for. They asked about a return. The draft explained that the product worked with their editor too, and how. It offered the refund anyway, if they still wanted it. I sent it unchanged. They kept the product. That is the model at its best: correct, kind, not pushy.

The one I rewrote. A customer had bought a product for their dad’s business. Their dad had died, and they asked for a refund past the window. The draft was correct and generic. I did not send it.

I wrote my own: do not worry about the refund, it is already done. And if it helps, I can record a ten-minute walkthrough of winding the account down. That offer was a judgment call I would not hand to a model. They wrote back to say thank you.

The near miss. A customer asked whether a technical feature worked with a particular variant of a tool. The draft said yes, confidently. It did not: that variant worked differently under the hood. I only caught it because I recognised the name.

That is the email that would have become a public thread. It is why rule 8 exists. To see what a wrong answer like that looks like once an assistant has learned it, read AI says something wrong about my product.

The numbers

The thank-yous did not go up because the model is charming. They went up because I stopped answering at 11 pm, tired. I had time to read the draft and make it specific.

What to keep for yourself

• Routine replies: the model is a better version of me than I am at the end of a day.
• Emotional replies: still mine.
• Technical claims: checked, every time. A wrong “yes” is the most expensive sentence in support.
• Apologies: mine. An apology has to be specific to mean anything.

That split matches how we run this site. AI helps with first drafts. A person checks every page and every report. We say so openly on our clean methods page.

Saying where the AI stops is not a disclaimer. It is what keeps the reviews honest, and the reviews are what the assistants read.

A first version of this appeared on my studio blog, dashbuilds.dev.

What to do next

Want to know what assistants say about your product now, including any wrong technical facts? The free check records what each one said about you across 75 answers.

Replies are good and the assistants still name someone else? The cause is usually upstream of support. Read why AI doesn’t recommend your product.

The guides

• How AI decides what to recommend: it searches first, reads what others say about you, and breaks ties for the brand it knows. The evidence, in order.
• How to check if ChatGPT recommends you: our free check, done by hand. Five question shapes, three runs each, five assistants, and what to write down.
• Why AI doesn’t recommend your product: five causes in the order they usually happen, each with a test you can run today.
• What works and what doesn’t: third-party proof and reviews work. llms.txt alone, schema alone, keyword stuffing and directory blasts do not. Studies for each.
• Why a glossary page helps AI find you: one definitions page, about 30 to 120 search impressions a day in two weeks. What I built, and the honest link to AI.
• Watching the competitors AI names instead of you: a $7.80 a month agent that checks pages every morning, and what it caught in two weeks.
• AI-drafted support replies, done honestly: 14 hours to 12 minutes, and the rules that kept it honest. Support is where the reviews AI reads come from.
• From side project to a product AI can name: five sessions turned an app for my mom into nine products. The sixth step makes one an assistant can recommend.

Coming later

Getting into the “best X” lists AI cites. Getting recommended by Claude, and by ChatGPT, specifically. Making your own site readable by AI. We add a guide when we have something sourced to say, not before. New guides go in the changelog.

How these guides are written

Dasha writes them, with AI help on first drafts. Every number is checked against the original study before it goes up. Vendor studies (from companies that sell AI visibility tools) are labelled as vendor studies. Found a mistake? Email hello@heardofyou.io. We fix it and log the fix.

So the work has three parts. Show up in the AI’s search results. Get genuinely mentioned on the pages it cites for your category. Make your own site easy to quote. Then measure, because answers move.

That is the whole method. The rest of this page is the evidence, the traps, and what we do about it.

Why it matters now

51 percent of B2B software buyers now start their research in an AI chatbot more often than in Google. In the same G2 research, 69 percent said a chatbot led them to a different vendor than planned. One in three bought from a vendor they had never heard of. The shortlist inside the answer is where that happens.

How an AI decides what to name

1. It searches first. For most buying questions, ChatGPT, Gemini and Perplexity search the web before answering. For software questions, 76 percent of ChatGPT’s searches add the current year (“best invoicing app 2026”). Missing from those results, you are mentioned about 2.8 percent of the time. With your site cited and you in branded searches, that rises to 91.4 percent.
2. It trusts what others say. For US software questions, 72.7 percent of the sources AI engines cited were earned media, not the brand’s own pages. Across 207 million citations, more than 84 percent were earned or third-party. 0.3 percent were paid.
3. It breaks ties on reputation. In a 2026 experiment with identical specs, the well-known brand won 100 percent of the time. A rating edge of under 0.1 stars for the challenger flipped it. Reviews are the tie-breaker a small product can actually earn.
4. It quotes what is quotable. In the original GEO study, pages with specific numbers, quotes and named sources were 30 to 40 percent more visible. Keyword stuffing did nothing.
5. It changes its mind. Run the same prompt twice and the same brand list comes back less than one time in a hundred. Category leaders still show up most of the time. The margins move. That is why we report how often you are named, never a rank.

What works

• Get onto the lists the AI cites for your category. Not every list. The three to ten that appear in the citations for your buyer’s questions. Pitch them with a real reason. Follow up twice.
• Get real reviews on the review site that shows up most. For B2B that is usually G2 or Capterra. For apps, the App Store or Product Hunt. For Perplexity especially, G2.
• Answer real Reddit threads as yourself. Disclosed, useful, no link unless it helps. Reddit is in every engine’s top five.
• Put the facts in your homepage’s first paragraph. What it is, for whom, how much, what it does not do. Most AI referrals land on the homepage.
• Write one honest comparison page and one alternatives page. “Comparison” is one of the most common words in ChatGPT’s own search queries.
• Let the crawlers in. Block Claude’s crawler and you get almost no Claude citations. Check robots.txt. Make sure your words are in the HTML, not loaded afterwards.
• Rank in ordinary search for the questions the AI searches. It still feeds the machine.

What does not work (but is widely sold)

What we would do

Free check. Your five most important buyer questions, three runs each, on ChatGPT, Gemini, Claude, Perplexity and Muse: 75 answers. Per question, you see who was named instead of you and which sources were cited. By email within 2 working days. A person checks every report.

Audit, $149 ($99 for the founding 20). 25 questions, same method. Plus a prescription: the exact lists, review sites and threads to get into for your category, ranked by how often the AIs cited them. Credited toward the project within 30 days.

Project, $799. We pitch 25 named lists and sites, with two follow-ups each. We write three pages for you: your story page, one honest comparison, one alternatives page. We set up review profiles on the two sites AI cites most in your category. You get a founder-led Reddit plan. We re-ask the same questions at day 30, 60 and 90.

Keep growing, $349 a month, 3-month minimum, offered after the day-30 re-check. One new placement a month plus a monthly re-check.

Every deliverable ships within 90 days or your money back. The day-60 target: named in at least your baseline plus 3 of the 20 agreed answers. Never fewer than 3, never more than 7. Miss it, and we keep working at no charge until day 120. We never promise what an AI will say.

Paying is simple at launch. You fill in a short request form. I confirm fit within 2 working days and send an invoice. No card on file, no subscription you forget about.

The fix: find out which pages are on that list for your buyer’s questions. Then earn a place on the ones that matter.

Why it is always the same name

Three things stack up for the incumbent.

They are on the lists. For US software questions, 72.7 percent of the sources AI engines cited were earned media: review sites, “best X” articles, forum threads. An established product has sat on those pages for years.

One analysis covered about 200,000 AI responses. Ranking first in a frequently cited list article raised a B2B software brand’s mention rate by 16.5 percentage points. That is a big lever, and it belongs to whoever got there first.

They win the tie. In a 2026 experiment, two products had identical specs. The well-known brand was recommended every time. A rating edge of under 0.1 stars for the challenger was enough to flip it. No reviews, no tie-breaker.

The sources repeat each other. A list article cites the G2 grid. A Reddit thread links the list. A YouTube review reads the Reddit thread. The AI sees the same name four times and calls it consensus.

In 2026, Wharton researchers found one strong source could override a model’s default pick. They tested Wirecutter and Reddit. The incumbent has many such sources. You need a few.

The step most founders skip: read the citations

When an AI names your competitor, it usually shows where it got that from. Perplexity shows sources on every answer. ChatGPT and Gemini show them when they searched. Claude shows them when it used web search. Those little footnotes are the whole game.

Run the question three times on each assistant. Write down every source cited. After 75 answers, a short list of domains keeps coming back. Maybe a G2 category page, a Zapier or Forbes “best of” list, two or three Reddit threads, one YouTube review. That is the citation map for your category.

One founder on r/SaaS asked ChatGPT for the top tool in his category. He got three competitors and no mention of himself. The useful next step is not to feel bad. It is to open the three pages ChatGPT cited and ask: how do I get on these?

What works, and what does not

Works:

• Pitching the lists the AI already cites, with a real reason to add you. A feature the list lacks, a price point it does not cover, a user they can talk to. Then follow up, twice. Most placements happen on the second or third email.
• Getting your first ten honest reviews on the one review site that showed up most in the citations. Not five sites. One.
• Answering the cited Reddit threads under your own name. Disclosed and genuinely useful. A cited thread gets re-read by the AI.
• A fair comparison page (“Ledgerly vs FreshBooks”) and an alternatives page on your own site. “Comparison” is one of the most common words in ChatGPT’s own searches.

Does not work:

• Posting your product on 50 lists nobody cites. Volume is not the point. The cited ones are.
• Buying a mention on a page that hides the sponsorship. Paid content was 0.3 percent of citations in Muck Rack’s 207 million citation study. Undisclosed placement is a policy problem waiting to happen.
• Asking friends to post fake Reddit threads. Models and moderators both catch repeated patterns. The thread gets deleted, and your name with it.
• Copying the competitor’s keywords onto your page. Keyword stuffing did not help in the original GEO study. It also reads badly to humans.

What we would do

1. Free check. Your five most important buyer questions, three runs each, across ChatGPT, Gemini, Claude, Perplexity and Muse. For every answer: who was named, and which sources were cited. You get the raw list of sources, not a score.
2. Audit, $149. We widen it to 25 questions. Then we rank every cited source by how often it appeared and how reachable it is. You get a prescription: the exact lists, review sites and threads to get into, in order. Credited toward the project within 30 days.
3. Project, $799. We pitch 25 named lists and sites, two follow-ups each. We write three pages for you: your story page, one honest comparison, one alternatives page. We set up review profiles on the two sites AI cites most in your category. You get a founder-led Reddit plan. Then we re-ask the same 25 questions at day 30, 60 and 90 and send you the before and after.

We will never promise the AI stops naming your competitor. We will show you how often it names you, before and after.

November 1 2026

• Card checkout added. The audit ($99 for the founding 20, then $149), the project ($799) and Keep growing ($349 a month) can be paid online through Stripe. Invoices stay available. Not a fit before work starts means a full refund. Prices unchanged. The pricing page shows founding spots left, counted from real payments.

October 31 2026

• Visibility log cadence fixed before the first run. A daily pulse of 20 answers by API replaces the weekly 75. The weekly full run adds 10 branded prompts. Google AI Overviews stays a monthly row, collected by hand. Day 0 is Friday November 6 2026.

• Site launched. Free check, audit ($149, $99 for the founding 20), project ($799) and the keep-growing plan ($349 a month).

• Promise published. Every deliverable within 90 days or your money back. Not named in at least your target number of the 20 agreed answers at day 60? We keep working free until day 120. Full wording on the promise page.
• Methods published on clean methods and how we measure.
• Payment at launch is by invoice, after a fit check. Card checkout comes later and will be noted here.

The AI cannot tell those are out of date. The fix: find the sources behind the wrong answer, correct the ones you control, and outweigh the ones you do not.

This matters more than it sounds, because buyers check. In a survey of 550 AI shoppers, 89.9 percent verify AI advice with another source before buying. Among B2B tech buyers, 94 percent fact-check AI research.

And 58.8 percent of visitors referred by an AI land on the homepage. Say the AI quotes “$29 a month, for agencies” and your homepage says “$12 a month, for freelancers”. The buyer sees the mismatch within one click, and trust goes with it.

The three usual reasons

1. A review profile or list has stale facts

G2’s own research found that AI descriptions of a software product closely track the text on its G2 profile. The same goes for Capterra, Product Hunt and the App Store listing. Set up a profile at launch with “coming soon” features and “free during beta”, never update it, and that is what the AI learned.

List articles are worse, because you do not own them. A “best invoicing apps 2024” article that says you cost $29 will say so forever, unless the author updates it.

2. Your homepage does not state the facts

AI pulls most of what it quotes from the first third of a page. One analysis of 815,484 query and page pairs put it at 44.2 percent of citations. If your first screen is a hero image and a slogan, the AI has nothing to quote. It goes to someone else’s description of you.

The cheapest accuracy fix: a first paragraph saying what it is, who it is for, what it costs and what it does not do.

3. The fresh source is wrong and the correct one is old

Models lean toward recent pages. In one study, newer dates moved passages up the rankings in all seven models tested. Ahrefs found that cited content is fresher than average.

So a newer wrong page can beat an older right one. A blogger wrote “Ledgerly is for agencies” last month. Your accurate about page has not changed since 2024. Guess which one wins.

How to find the wrong sources

Ask each assistant the branded questions a buyer asks once they know your name:

• “is [product] any good”
• “[product] reviews”
• “what is [product] and who is it for”
• “how much does [product] cost”

Run each three times, signed out, on ChatGPT, Gemini, Claude, Perplexity and Muse. Note every wrong fact and the source cited next to it.

Usually two or three pages sit behind the errors. That is the fix list.

What works, and what does not

Works:

• Updating every profile you control: review sites, Product Hunt, the App Store, your LinkedIn page, Crunchbase. Same name, same one-line description, same price, everywhere.
• A plain fact sheet page on your site. What it is, who it is for, prices, what is included, what you never do, contact. Short sentences, no adjectives. AI loves it, and so do tired buyers.
• A homepage first paragraph that answers the three questions: what, for whom, how much.
• Emailing the author of a stale list with the correction and one line on why it matters to their readers. Many will update it. Some will add you properly while they are in there.
• Fresh, honest comparison and alternatives pages, so the newest page about you is yours and correct.

Does not work:

• Correcting the AI inside the chat. It is polite about it and forgets by the next conversation.
• Faking a “last updated” date without changing anything. Models do reward recency, and faking it is manipulation. We do not do it.
• Deleting the stale page yourself. You usually cannot, and cached copies linger. Outweighing beats erasing.

What we would do

The free check runs unbranded buyer questions. For an accuracy problem, the audit is the right tool. Its 25 questions include branded checks (“is X any good”, “what is X and who is it for”). The report has an accuracy row: each wrong fact next to the source it came from. $149, credited toward the project within 30 days.

In the project we write three pages for you: your story page, one honest comparison, one alternatives page. Each puts the correct facts up front. We set up accurate review profiles on the two sites AI cites most in your category.

The 25 named lists and sites we pitch include the stale ones, with a correction request as the opening line. Then we re-ask the same branded questions at day 30, 60 and 90. $799.

We cannot edit an AI’s memory. We can make every page it is likely to read say the same true thing.

Almost everyone who launches without an audience gets the same result. Upvotes from strangers, a few kind comments, a flat line in analytics. That is not a failure of your product. It is what launch sites do.

The fix: stop chasing spikes. Build the handful of lasting places AI reads when a buyer asks for a product like yours.

What actually happened

You are in good company. One r/SaaS founder’s launch “got comments, zero site visits, zero signups”. Another got zero paying users after finishing number one for the day.

An analysis of 41,301 Show HN launches found the median gets two points and zero comments. Supabase surveyed 2,000 startup builders in 2026. 56 percent got their first customers through people they knew. Only 6 percent got them through Hacker News or Product Hunt.

Launch sites are read by founders, not by your buyers. Your buyers are asking ChatGPT.

Why the launch did not stick

1. It made noise, not sources

AI assistants do not remember launch days. They read pages. For US software questions, 72.7 percent of the sources AI search engines cited were earned media: review sites, list articles, forum threads, YouTube.

A launch day leaves one PH page with a few comments, plus tweets that are gone in a week. None of that is on the AI’s reading list unless you turn it into something.

2. The directory blast came next, and flopped too

The usual advice after a flat launch: “submit to 100 directories”. One founder paid $99 for a directory feature and counted 15 visitors, some probably bots. Another submitted to 17 and saw no change except two citations in Perplexity.

Directories can help domain rating a little. ChatGPT and Gemini rarely cite them. That is why we never sell directory submission.

3. Nobody outside your circle has written about you

For a category question, AI cites four kinds of pages. The “best X” lists. The review site with a grid. The Reddit thread where someone asked “what do people use for Y”. The YouTube video comparing three tools.

A new product is on none of them, because getting there is slow, unglamorous outreach. It is also the work that lasts.

What works, and what does not

Works:

• Treating your PH page as a review profile, not a launch page. A correct description, your real pricing, genuine reviews from real users. Roundups of indie tools and AI tools cite PH pages.
• Picking the one review site AI cites most for your category and getting your first ten honest reviews there.
• Pitching the three to five list articles that already appear in AI answers for your category. Two follow-ups each. Most replies come on the second email.
• Answering real Reddit threads, under your own name, where someone asked for a tool like yours. Reddit is in every engine’s top five cited domains.
• Getting one real person to make a short YouTube video. Ahrefs measured 75,000 brands. YouTube mentions had the strongest correlation with AI visibility of anything it measured.
• A homepage first paragraph that says what the product is, for whom, and at what price.

Does not work:

• A second launch with no audience.
• 100 directory submissions.
• Paying a “hunter”. The founders in the threads we read called the $10,000 quotes a scam. We agree.
• Fake upvotes, fake reviews, friends posting as customers. They get caught, and they poison the one asset you were building.

What we would do

1. Free check. Five buyer questions, three runs each, on ChatGPT, Gemini, Claude, Perplexity and Muse. You find out whether anyone outside your circle is being read, and who gets named instead. Free, by email in 2 working days.
2. Audit, $149. 25 questions, the full citation map for your category, and a prescription: which lists, which review site, which threads. Credited toward the project within 30 days.
3. Project, $799. 25 named lists and sites pitched, two follow-ups each. Three pages written for you: your story, one honest comparison, one alternatives page. Review profiles on the two sites AI cites most for your category. A founder-led Reddit plan. Re-checks at day 30, 60 and 90.

A launch is a party. The sources are the house. We help you build the house.

This page shows the starting line. We publish it before the work so nobody can say we picked the numbers afterwards. The same check runs every morning, and this page gets a day 30 row in early December.

What we asked

Five buyer questions a person in South Florida would type, asked from Miami, every day, on ChatGPT, Gemini, Claude and Perplexity. For example: “best pelmeni in Miami”, “where to buy frozen dumplings in Hollywood FL”, “handmade gyoza delivery Miami”. The full set is 18 questions in English, Spanish and Russian; the five daily ones are the target.

Day 0, October 31 2026

The shop’s own site was cited in 5 of the 20 answers. The places named most instead were a European deli in Sunny Isles (6 answers), a restaurant in Hallandale (5) and a Caucasian restaurant (3). Delivery apps came up in 3.

The same night, three questions in Spanish named the shop in 5 of 12 answers: ChatGPT in all three, Gemini in two, Claude and Perplexity in none.

What this tells us

ChatGPT and Gemini already find the shop. They read its website, which was rebuilt this autumn with plain facts AI can quote: what is sold, where to pick up, the delivery zones and minimums.

Claude and Perplexity do not name it at all. Both lean on review sites, maps listings and delivery apps for local food questions. That is where the shop is thinnest, so that is where the work goes.

The plan

• Fix the existing Google Business Profile: the right name, a service area instead of an old address, the market hours, both pickup points, photos.
• Ask real customers at the booth for reviews, in their own words.
• A Spanish version of the site went live on October 31, next to English and Russian.
• Re-check daily; publish the day 30 row here in early December and the day 60 row in January.

Update, November 1 2026: Muse

We added Muse, Meta’s AI assistant, as a fifth assistant. On its first day it named Daily Dumplings in 3 of 5 answers, first place in two. It missed the khinkali and Fort Lauderdale questions. Full write-up: What Meta’s Muse recommends.

1. The assistant cannot read your site.
2. You are not in the web results it pulls in before answering.
3. Nobody else has written about you where it reads.
4. Your homepage does not say what you are and who you are for.
5. A competitor beats you on reviews in a close call.

Below is each cause, the evidence, and a test you can run today. This is the deeper companion to why ChatGPT doesn’t recommend me.

Cause 1: the assistant cannot read your site

First, the assistant’s crawler has to fetch your pages and get readable text back. Three things quietly stop that.

• A robots.txt rule that blocks AI crawlers. OpenAI’s search crawler is OAI-SearchBot, its training crawler GPTBot. Anthropic’s are Claude-SearchBot and ClaudeBot. Perplexity’s is PerplexityBot.
• A firewall or bot-protection page that serves a challenge instead of your content.
• A site that only renders its words with JavaScript. The first fetch returns an empty shell.

Brands that block Claude’s crawler get almost no Claude citations (Muck Rack).

Test today: open your site’s /robots.txt. Look for Disallow lines under any bot name above, or under User-agent: *. Then fetch your homepage as a bot would, with no JavaScript. Check that your product name and description appear in the raw text. The commands are on our AI crawler check page.

Cause 2: you are not in the assistant’s live search results

This is the big one. When the assistant searches the web, it works from the pages that come back. ChatGPT searches in about a third of prompts, and adds the year to 76% of software searches.

In Tannenbaum’s 2026 study, a brand missing from those results got mentioned by GPT 2.8% of the time. When the brand’s own site was cited and branded searches happened too, 91.4%.

Position inside those results matters too. 58% of pages at position 1 got cited, and 14% at position 10 (AirOps and Kevin Indig, 815,484 query-page pairs).

Test today: write down the three or four searches the assistant would run for your buyer question. They look like “best [category] 2026”, “[category] reviews”, “[category] comparison” and “[your name] reviews”. Search each one signed out, in a normal search engine. If no page about you shows up on page one, you found the cause.

You can also ask Perplexity your buyer question and expand its sources. It shows exactly what it retrieved.

Cause 3: nobody else has written about you

Assistants prefer pages the brand does not control. For US software questions, 72.7% of cited sources were earned media (Chen et al.). Across 207 million citations, over 84% were third-party and 0.3% were paid (Muck Rack).

A product that lives only on its own site and a few directories is invisible in that mix. Directories are rarely cited (GetIntel, 9,978 answers).

Test today: search your product name with your own domain excluded, as in "Ledgerly" -site:ledgerly.example. Count real pages: reviews, “best X” lists, Reddit or forum threads, YouTube videos, articles. Fewer than five is normal for a solo product. On its own, it explains a near-zero mention rate.

Then run your buyer question through one assistant and note every source it cites. Those are the pages you are not on.

Cause 4: your homepage does not say what you are

Even when the assistant finds you, it needs a clear sentence to quote. What the product is, who it is for, what it costs.

44.2% of citations come from the first third of a page. Definite wording gets cited more often (AirOps and Kevin Indig). Buyers need it too: 58.8% of AI referrals land on the homepage (Similarweb). The person who arrives wants to confirm what the assistant said, in one glance.

Test today: open your homepage and read only what shows before you scroll. Finish this sentence from it alone: “[Product] is a [category] for [audience] that [main job]; it costs [price].” If you cannot, neither can the assistant.

Then compare the assistant’s description of you with the truth. A wrong price or audience means the pages it read are out of date.

Cause 5: you lose the close calls on reviews

When the assistant weighs you against a bigger name, familiarity wins ties. With identical specs, the well-known brand was recommended 100% of the time (Chu and Hou, 2026).

The same study found the fix: a competitor rating edge of under 0.1 stars broke that lead. In Columbia’s agent study, a 0.1 rating increase moved a product from a 10% pick rate to 15 to 20%. A reviewer “pick” badge took it to 20 to 43%.

Test today: find the review site the assistant cites most for your category. Often that is G2, Capterra, Product Hunt, Trustpilot or an app store. Put your rating and review count next to the two competitors it names most. No profile, or three reviews against their three hundred, is the cause.

Honest reviews from real customers, asked for plainly, are the lever a small product can actually pull.

Two things that are not your fault

Order and position. Assistants react to the order candidates appear in what they read (Bito et al.). Agents favour items in the top row (Columbia). If every list puts the incumbent first, you inherit that bias. You cannot fix it directly. Being on more lists, higher up, is the only route.

Noise. The same prompt gives the same brand list less than 1 time in 100. Named last month and not this month? Run it three times before deciding anything changed.

Limits

The 2.8% and 91.4% figures come from one pre-print, on one model family. The exact numbers will not hold everywhere, but the direction has held across studies. Muck Rack and GetIntel sell PR and visibility tools, so treat their figures as large but vendor-reported.

Fixing all five causes raises your odds. Nobody can promise what an assistant will say.

What to do next

Run the tests in order and stop at the first one that fails. That is your cause.

It is usually cause 2 or 3. Then the work is getting named on the pages the assistant already cites for your category. That is what our project does. The free check is where it starts: it tells you which pages those are.