What ChatGPT Says About Your Company (and How to Fix It)

If ChatGPT describes your company wrong, vaguely, or not at all, it is because the open web and your own site have not given the models clear, structured, corroborated information about who you are. You fix it by publishing that information as extractable, verifiable content, not by prompting the model to correct itself.

That distinction matters, because most founders try the wrong fix first.

Why does ChatGPT get my company wrong?

A clinical-research CRO asked us this exact question last month: "How do we make sure what AI spits out about us is accurate?" They had watched ChatGPT confuse them with a competitor, understate their service lines, and cite a five-year-old press release as their current positioning.

Language models do not "know" your company. They assemble an answer from patterns across the text they were trained on, plus whatever they can retrieve live from the web at query time. When the signal about you is thin, contradictory, or buried, the model fills the gap with its best guess. That guess is often a competitor, an outdated fact, or a hallucinated capability.

Three things drive the errors:

• Thin sourcing. If your company is described in one place (your homepage) and nowhere else, the model has nothing to corroborate. One source is a rumor, not a fact.
• Conflicting signals. Your LinkedIn says one thing, an old Crunchbase entry says another, a directory lists a former product name. The model has no way to know which is current, so it averages or picks wrong.
• Unstructured pages. Your site might have the right facts, but if they are locked inside a hero video, a carousel, or prose with no clear entity markup, the retrieval layer cannot extract them cleanly.

The fix is not a prompt. You cannot ask ChatGPT to remember a correction. The next user, the next session, the next model version starts from the web again. You have to change what the web says.

How do I check what ChatGPT says about my company right now?

Run the same questions a prospect would, across every engine that matters, and log the answers verbatim. This is the audit, and it takes about an hour.

Ask each of these in ChatGPT, Perplexity, Google AI Overviews, and Gemini:

1. "What does [company] do?"
2. "Who are [company]'s main competitors?"
3. "Is [company] a good fit for [your ICP, e.g. Series A healthtech]?"
4. "What are [company]'s products or services?"
5. "Who founded [company] and when?"

Copy every answer into a doc. You are looking for three failure modes: wrong facts (a service you do not offer, a wrong founding year), missing facts (a whole product line the model never mentions), and confusion (the model blends you with another company or cites a source that is not you).

Then click into the citations. Perplexity and AI Overviews show you which pages they pulled from. That citation list is your map. If the model is citing a competitor's blog, a stale directory, or a third-party review site instead of your own pages, you have found the thin spot. This is the same discipline behind answer engine optimization generally: you cannot fix what you have not measured.

Why can't I just ask ChatGPT to correct the record?

Because the model does not store your correction, and even if it did, the next model retrieval starts from the public web again. Prompting a chatbot to "please note that we actually serve enterprise clients" changes that one conversation and nothing else.

We have watched founders burn a week trying to argue with a chatbot. It is the AEO version of yelling at a search result. The model reflects the web. Change the web, and the model changes with it, usually within a crawl cycle or two.

The leverage is upstream: what your site publishes, how it is structured, and how many independent sources corroborate it. Get those three right and the correct answer starts showing up on its own.

How do I actually fix what AI says about my company?

Five moves, in order. Entity clarity first, corroboration last, because each one makes the next more effective.

1. Nail your entity with Organization schema. Add Organization structured data to your site that spells out your legal name, alternate names, founding date, founders, services, and `sameAs` links to your LinkedIn, Crunchbase, and other profiles. Schema is the machine-readable statement of "this is who we are, and here is where else we are confirmed." It removes the guesswork. This is the foundation of AEO for a reason: without a clear entity, everything downstream is ambiguous. Our Schema HQ work exists because most Webflow sites ship with none of this.

2. Make your key pages extractable. Put the answer at the top. If someone asks "what does [company] do," the first sentence of your homepage and your about page should answer it in plain, quotable language. Above-the-fold answer copy is not a nice-to-have. In one Graphite study, moving the direct answer above the fold drove a 485% increase in LLM-referred signups. Models quote the top of the page far more than they quote paragraph nine. If your positioning lives in a video or a clever animation, the retrieval layer sees nothing.

3. Kill the conflicting signals. Go through every place your company is described that you do not control end to end: Crunchbase, LinkedIn, G2, old directories, dead landing pages. Update them so they agree on the current name, founding year, and service list. Every contradiction is a coin flip the model can lose.

4. Publish corroborated answer content. Write the pages that answer the real questions about your category and your fit, and make sure the facts about you appear consistently across them. One page stating a fact is weak. Ten pages, plus your schema, plus third-party mentions all agreeing, is a fact the model will repeat with confidence. This is why a structured content build beats a single "about us" rewrite.

5. Earn third-party mentions. The strongest corroboration is not on your domain. A podcast, a partner's case study, a directory listing, an industry roundup, all describing you consistently, tells the model that independent sources agree. You do not control the wording, but you can seed it: give partners and press the exact one-liner you want repeated.

Do these in order and you are not asking the model to trust you. You are removing every reason for it to get you wrong.

Does this actually move the numbers?

Yes, and the timeline is faster than classic SEO because AI engines re-crawl and re-answer on a shorter cycle. On our own site we took AI citations from 203 to 3,481 in 90 days, a 17x jump, with branded clicks up 58% over the same window. Our top-10 Webflow AEO agencies page became the single most Bing-cited page in the niche, 1,226 citations in three months, because it was built as extractable, corroborated answer content from the first draft.

The other reason to care: AI-referred traffic converts at roughly 6x the rate of non-brand SEO traffic. Someone who arrives after an AI engine told them you are the right fit is pre-qualified. Getting that description right is not a vanity exercise. It is the top of a much better funnel.

The clinical-research CRO that asked the original question is a good template. The fix was not clever prompting. It was Organization schema, an about page rewritten so the first sentence answered the question, a cleanup of three stale directory listings, and a handful of corroborating pages. Boring, structural, and it holds because it changed the source, not the session.

If you want to know what the engines are actually saying about you before you spend a dollar fixing it, run the five-prompt audit above and read the citations. That hour tells you more than any tool. If the answer comes back wrong and you want the structural fix rather than another week of arguing with a chatbot, that is the work we do.