Why Doesn't ChatGPT Know Your Startup? 48 Tested, 4 Known

Quick answer: ChatGPT doesn't know your startup because name recall comes from training data, and a young product has few of the third-party mentions (G2, Crunchbase, Reddit, press) that models learn names from. But a search-grounded model can still find you by category. In a test of 48 AI-built startups, a model named only 4, yet recommended 28 of them when asked for the best tools in their category. I ran 48 AI-native products through a recognition test in one afternoon. A language model with no web access described exactly 4 of them correctly. The other 44, including startups that have raised serious money, came back with the same shrug: "I do not have reliable information about the software product named [X]." Then I asked the category question instead of the name, and 28 of the 48 showed up, many at number one. That gap is the whole story, and most founders asking why doesn't ChatGPT know my startup are watching the wrong half of it. The two questions I asked each product The experiment keyed on the difference between two questions a buyer's AI actually gets asked. The first went to a model with no live web access. Just its training. "What is [brand]?" This measures memory: does the model carry your product the way it carries ElevenLabs? The second went to a search-grounded model, but it never received the brand name. It got the category question a real buyer types: "what are the best tools for [the thing this product does]?" Then I checked whether the product appeared, and where. Here is the whole sample in one view. The first two rows are the name test. The rest are what happened on the category question. | What the model did | Products | Share | |---|---|---| | Described it correctly by name | 4 / 48 | 8% | | Drew a blank on the name | 44 / 48 | 92% | | Ranked it in its category (name never given) | 28 / 48 | 58% | | Unknown by name, yet ranked in its category | 24 / 48 | 50% | | In a category list, but left off it | 9 / 48 | 19% | | No category list produced at all | 11 / 48 | 23% | The bold row is the finding. Half the sample was invisible by name and visible by function at the same time. AI forgets your name, even the funded ones Only four products passed the name test: ElevenLabs, Suno, Runway, Cursor. The names you already know. For everyone else, the model returned a near-identical sentence. Sierra got it. Decagon got it. Lindy, Mercor, Adomate, and 39 others got it too. "I do not have reliable information about the software product named Decagon (decagon.ai) and cannot provide accurate details about its features, use cases, or pricing." Decagon is a funded customer-service AI company. Mercor is a talent marketplace that has raised at a valuation most founders would trade a kidney for. The model could not describe either from its name. Brand recall in a model follows funding and press, and both take years. It is a lagging signal, so if your product is a year old, the model drawing a blank on your name is exactly what you should expect, and no reflection on what you built. Why ChatGPT doesn't know your startup yet A model's memory of names is built from its training data, and training data is mostly the open web talking about you. Established products have a Wikipedia entry, hundreds of G2 and Capterra reviews, a Crunchbase profile, Reddit threads, and press. A startup shipped last quarter has a homepage and maybe a Product Hunt launch. There is almost nothing for the model to have read, so there is almost nothing for it to recall. Three forces stack on top of that: - Training cutoff. A base model only knows the web up to its last training date. Anything you published after that is invisible to its memory until the next training run, which is why live-retrieval (search) matters more than recall for a new product. - Generic names. Sierra, Wonder, Peek, Ray, Brew, Marx, Nora were all in the sample. Each is also a common word or a surname. Asked "what is Brew," the model has nothing to separate the email tool from the drink. A distinctive name will not get you recognized on its own. A generic one actively works against you. - An unreadable page. If your site renders only in the browser, most AI crawlers, including GPTBot and PerplexityBot, do not run JavaScript, so they receive an empty body. The one source that is unambiguously about you, your own site, tells the model nothing. The first two you fix slowly, with time and earned mentions on the sources models train on. The third you fix this afternoon, and it is the one that also decides the question that matters more. It still finds you by category Ask the same model, with search on, for the best tools in a category, and the story flips. Decagon is unknown by name and sits at number one for AI customer service automation, listed next to Ada, Intercom's Fin, and Zendesk AI. Lindy, unknown by name, ranks first for AI automation platforms, ahead of Zapier and Make. Sierra, unknown by name, shows up fifth for customer experience platforms. Share of voice, in AI answers, is whether your product appears when someone asks the model for the best tools in your category, and in what position. It is the metric tied to revenue, because it runs on the query your buyer actually types: the category. Nineteen products in the sample landed at number one for their category while the model had no idea who they were by name. | Product | What it does | Knew the name? | Category rank | |---|---|---|---| | Decagon | AI customer service automation | No | #1 | | Lindy | AI automation platform | No | #1 | | Granola | AI meeting notetaker | No | #1 | | Adomate | AI ad creative | No | #1 | | Brew | AI-native email platform | No | #1 | | Octolane | AI-driven CRM | No | #1 | | Ray Finance | AI personal finance advisor | No | #1 | | GitHired | Developer hiring | No | #1 | | Mockin | AI interview prep | No | #1 | | Kraflio | Multi-platform content engine | No | #1 | Nine more won their category the same way: Crono, Cleanlist, River, Flowstep, Wonder, Clera, NotesXP, PodPrime, and Tadka. All indie or early. All beat the recall test by ignoring it and winning on function instead. This is why the "does ChatGPT know my name" panic points at the wrong target: nobody types your name until they already heard it. The buyer with the problem you solve types the category. Who is actually invisible Nine of the 48 showed up in neither answer. Unknown by name, absent from the category list. I am not naming them, because the point is not to dunk on a founder who shipped a real product into a hard market. The point is the shape of the failure, which was almost always one of two things. Either the homepage never stated the category in plain language, so the model filed it wrong or not at all. Or the product got sorted into a category it does not really belong in, and then lost to the incumbents who own that category. One product builds a talent marketplace and got read as data labeling, where it naturally did not appear. The model was not wrong to look. It was pointed at the wrong shelf by the page itself. A separate 11 products hit a third outcome: the model would not produce a category list at all for their space. No list, no ranking, no data. I read those as unresolved. The model refusing to list a category does not prove you are absent from it. What to do about it Generative engine optimization (GEO) is the work of making a page that answer engines can read, categorize, and cite. Answer engine optimization (AEO) is the narrower half of it: structuring content so an engine can lift a direct answer from the page. For the gap this test exposed, both come down to the category question, and it usually closes at the page level. Say what you are in a sentence a machine can file, and put it where the crawler reads first, in the served HTML, so it is there before any JavaScript runs. Stop making the model guess your category from a clever tagline. The products that ranked all had a literal, plain statement of what they do near the top of the page. The ones that lost were vague about themselves or picked a name that fights them. Backing that up with the sources models train on, a Crunchbase profile, a few review-site listings, a consistent one-line description everywhere, is the slow half that pays off at the next training run. You can watch the fast half directly. Tabkeel, the checker I build, runs an AI mirror that asks a model what your product is, stores the answer word for word, and checks it against your own site, then tracks whether you surface when someone asks for the best tools in your category. You fix the page, run it again, and watch the answer move. Point it at your site from the Tabkeel exam and the reading problem, whether a crawler even receives your category, shows up in the same pass. To isolate that one front first, the AI readability tool reports what a model can determine from your served HTML. The deeper fix belongs to the launch itself. A page that renders only in the browser hands most AI crawlers an empty body, which is one of the seven fronts in the pre-launch checklist for AI-built sites. And once you rank, the click side of the same problem, low click-through on queries you already win, is what the Search Console side of Tabkeel surfaces. The full method behind the checks is in the methodology. Frequently asked questions Why doesn't ChatGPT know my startup? Because a base model's memory of names comes from training data, and a young startup has few of the third-party mentions (reviews, Crunchbase, Reddit, press) that models learn names from. In a test of 48 AI-built startups, a no-web model recognized only 4 by name. A generic brand name and a page that renders only in JavaScript make it worse. How do I check if AI can find my SaaS? Run two prompts. Ask a plain model "what is [your product]?" to test name recall, and a search-capable model "best tools for [your category]?" to test whether you surface for the query buyers actually type. Tabkeel's AI mirror runs both against