Why does ChatGPT get facts about my company wrong?

Because the model assembles its picture of you from whatever the web says, and the web about you is stale, contradictory, thin, or ambiguous. Those are the four causes, and each has a distinct fix. ChatGPT does not have a file on your company it maliciously corrupted. It has fragments: your old pricing page in an archive, a directory listing from 2021, a competitor with a similar name. When fragments conflict, the model guesses, and the guess is what your buyer hears. The repair is making the truth easier to find than the noise.

First, why this is urgent, not cosmetic

When ChatGPT tells a buyer you only serve enterprise when you serve everyone, or quotes a price you retired two years ago, that buyer walks before you ever knew they existed. AI misinformation is a silent filter on your pipeline. And unlike a wrong article you could email someone about, there is no editor to correct. The only lever is changing what the model reads. The good news: that lever works.

The four causes, and how to tell them apart

Cause one: stale sources

The model, or the pages it searches, reflect your past: old services, old prices, old locations. Diagnosis: the wrong facts were true once. Fix: update every surface that still carries the old truth, your own pages first, then profiles, directories, listings. Ask Perplexity the same question and read its citations: it shows you exactly which stale pages are feeding the answer, giving you a literal to-do list.

Cause two: contradictory sources

Your site says one thing, your LinkedIn another, a directory a third. The model meets the conflict and either averages it into mush or picks the wrong one. Diagnosis: the wrong facts match some real surface you forgot. Fix: pick one canonical description, name, category, services, market, and enforce it everywhere, word for word where possible. This is entity consistency, and it is the highest-yield repair on this list.

Cause three: thin sources

There is simply not much about you to read, so the model fills gaps with plausible guesses based on businesses like yours. Diagnosis: the wrong facts sound generic, like a template of your category. Fix: give the machine substance, a real About page, an llms.txt stating the facts in your words, structured data making them machine-explicit.

Cause four: ambiguity

You share a name with another business, and the model braids you together. Diagnosis: the wrong facts are true, about someone else. Fix: disambiguation, schema with sameAs links tying your name to your exact profiles, location and category stated everywhere, so the machine can tell the entities apart.

The repair sequence that works

In order: run the audit, ask all three engines about yourself and log every wrong fact. Trace each to its cause with the diagnoses above, Perplexity's citations do half this work for you. Fix your own surfaces first, site, About, llms.txt, schema, since they are the sources you fully control and the ones models weight as canonical for self-description. Then chase the third-party stragglers, old directories, stale listings. Then recheck monthly: corrections propagate on recrawl schedules, weeks not days, and confirming the fix landed is part of the job. Stubborn errors that survive this sequence usually mean a contradictory source you have not found yet, keep following the citations.