It is a specific kind of stomach drop. You ask ChatGPT about your own company and it confidently states the wrong price, a feature you killed two years ago, or a founding story that belongs to someone else. The instinct is to argue with the chatbot. That does nothing. Here is what actually works.
Why models get your facts wrong
A model is not looking you up in a database. It is assembling an answer from what it absorbed in training plus whatever it can pull from the web right now. Wrong answers usually trace to one of three causes.
- ✓Stale training data. The model learned an old version of you and has not been corrected since.
- ✓Bad third-party sources. An outdated listing, a wrong review summary, or a years-old article is being read as current truth.
- ✓No authoritative source to anchor to. If you never clearly stated the fact in a place the model can read, it guesses, and guesses drift.
The core idea: you cannot edit the model, only its inputs. Every fix below is about changing what the model reads, so that the next time it assembles an answer about you, the right facts are the easiest ones to find.
Step one: find exactly what is wrong
Ask each engine to describe your company, your pricing, and your main features, and write down every error. Do it on ChatGPT, Claude, Gemini, and Perplexity, because they disagree, and do it more than once per engine, because the answers vary run to run. One pass will miss things. A few passes show you the pattern and which errors are sticky.
Step two: fix the sources, in order
- Your own pages first. State the correct fact plainly, in text, on a crawlable page. Put pricing in real HTML, not an image. Add Organization and Product schema so the fact is machine-readable, not just visible.
- Your third-party profiles next. Update the listings and directories buyers and models both read: review sites, your LinkedIn and Crunchbase, any industry database. Consistency across them is what makes a fact look true.
- The structured-knowledge layer. Where you qualify, getting your basics right in widely-cited reference sources carries weight, because those get quoted heavily in AI answers.
- Then confirm nothing is blocking the fix. If your robots.txt blocks AI crawlers or your page only renders after JavaScript, the model never sees the correction you just published.
Step three: re-check, and be patient
Corrections are not instant. Live web sources update on their own schedule, and training-baked errors only fade as models refresh. Re-run your checks every few weeks across all four engines. You are looking for the wrong fact to get rarer and the right one to take over, which is a gradual shift, not a switch you flip.
Start by confirming the model can even reach your corrected pages. The free AI crawler checker tells you in seconds whether your robots.txt is quietly blocking the engines you are trying to correct, which is the most common reason a fix never lands.