Monitoring

2026-07-06 Updated 6 July 2026 10 min read

ChatGPT says wrong things about my business: how to fix incorrect AI information

An AI engine confidently telling prospects the wrong price, a discontinued product line, or facts that belong to a similarly named company is a real commercial problem: buyers act on those answers without ever visiting your site. You cannot email OpenAI a correction, but you are not powerless. There is a specific set of levers that change what AI says about you, and a right order to pull them in.

First, diagnose which layer the error lives in

AI engines produce answers from two different places, and the fix depends on which one is wrong:

Live retrieval errors. The engine browsed the web, read a page (yours or a third party’s) and repeated something outdated or wrong from it. These are the good kind: fix or outrank the source page and the answer can change within days or weeks, as soon as the engine re-reads it.
Training-memory errors. The model absorbed wrong or stale information before its knowledge cutoff and repeats it with browsing off. These are slower: the memory itself only changes when the provider ships a new model or retrains, so your strategy is to make the live layer overrule it and to seed correct facts everywhere the next training run will read.

A quick test: ask the same question with web search enabled and disabled (in ChatGPT, toggling Search changes which layer answers). If the error only appears without browsing, it is baked into memory. If it appears with browsing, follow the citations: one of the cited pages is carrying the wrong fact, and that page is your target.

The correction levers, in order of leverage

1. Make your own site state the correct facts, plainly

Engines treat your site as the primary source for facts about you, but only if those facts are actually on it, in plain extractable text. Audit your about page, pricing page, contact page and footer for the facts AI keeps getting wrong: legal name, locations, what you sell, current pricing, what has been discontinued. If a fact changed, do not just delete the old claim, state the new one explicitly (“As of 2026, X is included on every plan”). A surprising share of “AI is wrong about us” cases trace back to a site that never clearly stated the right answer anywhere.

2. Fix your structured data

Schema markup is the machine-readable version of your identity, and it is what engines use to tell you apart from similarly named businesses. Make sure your Organisation schema carries the correct legal name, URL, logo, address and sameAs links to your real profiles, and that it says the same thing on every page. Contradictory schema across your own pages teaches engines contradictory facts. See schema markup for AI for the full setup.

3. Correct the knowledge-graph sources: Wikidata and the profiles engines trust

If ChatGPT confuses you with another company or repeats an old founding fact, the error often lives in the public entity graph rather than on your site. Check your Wikidata item (if one exists) and the profiles engines lean on, such as Companies House for UK businesses, LinkedIn, Crunchbase and major directories, and correct them at the source. These sources feed both live retrieval and future training runs, so a fix here pays twice. Our guide to how AI knowledge graphs work explains which sources carry the most weight.

4. Get fresh third-party coverage carrying the correct fact

Engines weigh corroboration. If the only page saying the new, correct fact is yours, and five old third-party pages say the outdated one, the majority can win. Update what you control (partner pages, directory listings, review-site profiles), and where the wrong fact lives on a page you do not control, ask for a correction; publications update factual errors more often than people expect. New coverage that states the correct fact adds fresh, retrievable evidence on your side of the ledger.

5. Wait for, and be ready for, model refreshes

Training-memory errors ultimately expire when providers ship new models trained on more recent data. You cannot schedule that, but you can make sure that when the next crawl happens, everything it reads agrees on the correct facts, which is precisely what levers 1 to 4 accomplish. In the meantime, live retrieval increasingly fronts most engines’ answers, so a corrected live layer masks a stale memory for a growing share of real queries.

What does not work

Honesty saves you time here:

You cannot submit a correction to the model provider and have a fact hand-edited. There is no form. (Genuine legal issues, such as defamation, have separate reporting routes, but “our pricing is out of date” does not qualify.)
Arguing with the chatbot does not persist. Correcting ChatGPT in a conversation changes that conversation only. The next user gets the same wrong answer.
Blocking AI crawlers makes it worse, not better. If engines cannot read your current site, they fall back entirely on memory and third-party pages, which is where the wrong fact lives. The correction strategy depends on being readable.

Keep watching: wrong facts drift back

A correction is not a one-off event. Engines re-crawl, models update, and a fact you fixed can resurface from an old source months later. This is a monitoring problem, and it is the reason we built sentiment and accuracy checking into the SearchScore Tracker. The Tracker asks six engines (ChatGPT, Claude, Gemini, DeepSeek, Grok and Perplexity) your customers’ questions every week and shows you exactly what each one said, with per-citation sentiment. Its Citation Trust view shows what share of your citations come from AI reading your live pages versus its training memory, which tells you directly whether engines are answering from the layer you can fix quickly. And when an engine states something false, you can flag the citation as a hallucination or correct a sentiment call, so your adjusted view reflects reality while you work the levers above.

Frequently asked questions

Why does ChatGPT give wrong information about my business?

Usually one of three causes: your own site never states the correct fact plainly, so the engine fills the gap from elsewhere; a third-party page it retrieves carries outdated information; or the error is baked into the model’s training memory from before its knowledge cutoff. Diagnosing which layer is wrong, by comparing answers with browsing on and off, tells you which fix applies.

Can I contact OpenAI to correct facts about my company?

There is no fact-correction submission route for business information. The reliable path is to fix the sources the models read: your own site, your structured data, Wikidata and the trusted profiles, and third-party pages carrying the wrong fact. Corrected sources change live-retrieval answers first and feed future training runs.

How long does it take for AI to stop repeating a wrong fact?

Live-retrieval errors can correct within days to weeks of the source pages changing, as engines re-crawl. Training-memory errors persist until the provider ships a model trained on newer data, which you cannot schedule, so the practical strategy is to fix the live layer and ensure every page a future training crawl reads agrees on the correct fact.

How do I know if AI engines are saying wrong things about me right now?

Ask them your customers’ real decision-stage questions and read the answers, or automate it: the SearchScore Tracker runs those questions weekly across six engines and shows every citation with its exact wording, position and sentiment, so a wrong or negative claim shows up in your digest instead of in a lost deal.

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