Ask ChatGPT about your business and there is a real chance something in the answer is wrong: hours you changed two years ago, a service you stopped offering, prices scraped from an archived page, or — worst of all — a description that actually belongs to a competitor with a similar name. AI researchers call these errors hallucinations: confident statements that are not grounded in accurate, current facts. When the hallucination is about your brand, it costs you customers who never call to double-check. This playbook covers why it happens, how to find the wrong claims, and the fix hierarchy that actually moves the needle.
Why do AI engines get your business wrong?
Almost every brand hallucination traces back to one of three root causes: stale sources, an ambiguous entity, or thin coverage. Diagnosing which one you have determines the fix.
Stale sources
Answer engines combine a trained language model with live web retrieval. The model's training data has a cutoff date, and even the retrieval layer leans on whatever pages are easiest to crawl — cached copies of your old hours page, a directory listing from before you moved, or press coverage of a product you discontinued. If the most crawlable mention of your pricing is a blog post from three years ago, that is the price the AI quotes today.
Ambiguous entity
An entity is the machine-readable concept of your business — the thing all your mentions across the web are supposed to point at. If your name is generic, shared with another company, or spelled inconsistently across your site, Google Business Profile, and directories, the model can blend two businesses into one answer. This is how a law firm gets credited with a rival firm's practice areas, or a franchise location inherits another location's reviews and hours.
Thin coverage
When there is little authoritative information about you, the model fills the gap with plausible-sounding inference from your category. A med spa with no crawlable services page gets described with the "typical" med spa menu — whether or not you offer any of it. The AI is not lying so much as guessing, and thin coverage invites guessing.
How do you find out what AI says about your brand?
You audit it the same way a customer would encounter it: ask the engines your key questions and write down what comes back. A one-hour session gives you a defensible baseline.
- List 10–15 questions customers actually ask. "What are [business]'s hours?", "How much does [service] cost at [business]?", "What does [business] specialize in?", "Is [business] still open?", and "[business] vs [competitor] — which is better?"
- Run them across the major engines. ChatGPT, Claude, Gemini, Perplexity, Microsoft Copilot, and Google AI Overviews. Answers differ per engine because each uses different retrieval sources.
- Log every claim in a spreadsheet. Date, engine, the exact claim, whether it is correct, and — critically — any source the engine cites. The cited source tells you exactly which page to fix.
- Rephrase and re-ask. Answers vary by wording and session, so try each question two or three ways before concluding an error is consistent.
Grade each claim as correct, outdated, or fabricated. Outdated claims usually mean stale sources; fabricated ones usually mean thin coverage or entity confusion.
What is the fix hierarchy?
Work from what you fully control outward: your own site first, third-party data second, platform feedback third, and monitoring always. Skipping straight to "report the error" rarely works, because the engine will keep regenerating the mistake from the same bad sources.
1. Publish authoritative facts on your own site
Your website is the one source you control completely, and most answer engines retrieve from the live web before answering. Put your hours, address, pricing, and current service list in plain, crawlable HTML — not buried in images or PDFs — and date the pages so freshness is visible. Add schema.org structured data (Organization or LocalBusiness markup) so the facts are machine-readable, and redirect or update the stale pages your audit spreadsheet flagged. If a discontinued service still has a live page, mark it clearly as discontinued rather than deleting it, so crawlers see the correction instead of a gap.
2. Make third-party sources agree
Engines cross-reference. If your site says one thing and Google Business Profile, Bing Places, Yelp, industry directories, and LinkedIn say another, the model has no way to know which is current — and it may average the disagreement into a wrong answer. Align your name, address, phone, hours, and business description everywhere they appear, and claim unclaimed listings. If you are being conflated with a similarly named competitor, add explicit differentiators (location, founding year, specialty) to your descriptions across these profiles. This is entity consistency, and it is the single highest-leverage fix for confusion-type hallucinations.
3. Use each platform's feedback mechanism
Every major engine has a way to flag a bad answer: ChatGPT and Claude have thumbs-down and report options on responses, Perplexity lets you report an answer, and Google AI Overviews has a feedback link on each overview. Use them — with the correct fact and a link to your authoritative page — but hold realistic expectations. Feedback influences systems slowly and is never guaranteed to change a specific answer. Treat it as a supplement to fixing the underlying sources, not a substitute. For seriously harmful errors, the major providers also operate formal content-report channels through their help centers, which are worth the extra effort.
4. Monitor for regressions
Corrections regress. A fixed answer can break again when an engine's index picks up a new stale source, a directory re-syncs old data, or a model version changes. Re-run your question set monthly, keep the spreadsheet, and watch which sources get cited over time — rising citations of your own site is the leading indicator that fixes are sticking. Our guide on tracking AI citations covers the tooling; AEO Fail's $19/month monitoring runs this loop for you and alerts you when an answer changes.
Frequently asked questions
Can I make an AI company delete wrong information about my brand?
Not directly, in most cases. Wrong answers are usually generated fresh from retrieved sources rather than stored as a fact you can delete, so the durable fix is correcting the sources the engine reads. Provider feedback and report forms help, especially for harmful claims, but they are slow and discretionary.
Why does AI confuse my business with a competitor?
Because the two entities look similar in the data: overlapping names, the same city or category, or inconsistent spellings that blur the boundary between you. Strengthening entity signals — consistent naming everywhere, structured data on your site, distinct descriptions on your profiles — is the fix.
How long do corrections take to show up?
Engines that retrieve live web results can reflect on-site fixes within days to a few weeks of a recrawl. Facts baked into a model's training data lag much longer — sometimes until the next model version — which is why retrieval-visible sources are where you focus.
Does fixing my website really change AI answers?
For retrieval-based engines, yes — it is the most reliable lever you have. Most flagship assistants now browse or cite live pages, and a clear, current, machine-readable fact on your own domain routinely displaces a stale third-party mention. No one can guarantee a specific answer, but you can decisively change what the engines have to work with.
Find out what AI is saying about you right now
The audit spreadsheet above takes an afternoon. If you would rather skip to the answers, our free AEO audit checks how the major answer engines see your business — what they get right, what they hallucinate, and which sources are feeding the errors — so you know exactly which fixes in this hierarchy to start with.