AI misinformation and reputation
What you can and cannot do when a model states something false about your company
Start here: answers cannot be edited
When a generative model describes your company incorrectly, there is no mechanism to rewrite that answer from the outside. Treating it like a search-result removal request leads to the wrong plan.
What you can change is the material the model reads. You fix the sources consulted during answer generation and reduce the supply of the error, so the answer changes as a consequence. The lever is indirect.
So the operative question is not "can this be corrected" but "where is the error coming from, and can that be fixed".
Four sources of a wrong answer
The remedy differs completely by cause, so separate them first.
A third-party article carries outdated pricing, a former company name, or an incorrect description, and the model reads it correctly. The model is behaving normally; the source is what needs fixing.
The current fact is missing from your pages, or different pages contradict each other. Given no way to choose, a model may take the older statement.
The information is simply not on the web and the model generalizes — a hallucination. The fix is to supply the fact, not to argue against the output.
Answers produced without a web lookup reflect training-time knowledge. Fixing sources does not propagate immediately; expect a lag.
Cases 1 to 3 are distinguishable by whether the answer shows source URLs. If it does, read them. If it does not, suspect 3 or 4.
What you can actually do
- Establish reproducibility. Answers vary. An error appearing once is not the same problem as one appearing in eight of ten runs. Measure the rate first.
- Collect the source URLs. Gather what the wrong answers cite. This determines which of causes 1 to 3 you are dealing with.
- Correct your own pages. State the current fact in body text, not only in metadata, and reconcile contradictions between pages.
- Request corrections from third parties. Where an external article is the supply, ask the publisher to correct it. Where that is not possible, strengthen your own accurate page so it is more likely to be consulted instead.
- Use each provider's feedback channel. Most services accept reports on an answer. Do not expect speed, but it creates a record.
- Keep observing. Propagation lags the fix. Track whether the error rate falls.
Content that may constitute defamation or rights infringement needs a legal track separate from the information work described here. This page covers the technical scope only.
Things that do not work
It is corrected within that conversation and nowhere else. Sessions are generally closed to one another.
Writing "we are not X" repeatedly puts the false term on your page and can strengthen the association. State the correct fact instead of negating.
One clean answer is not evidence of a fix, and one bad answer is not evidence of a systemic problem.
Measuring the error rate
Ask the relevant questions repeatedly and track the share of runs containing the false statement. As with citations, the unit is a ratio across trials, not a single check. Metric definitions are in What is AEO; the citation side is covered in Getting cited in AI search.
Sighted samples ChatGPT, Gemini, and Perplexity answers daily and records both the answer text and its source URLs, so you can track how often an error appears and where it comes from.
Read next
- Getting cited in AI search — conditions and how to improve
- What is AEO — how answers are generated, and the metrics
- What is LLMO — how the terminology relates