Why does AI get facts about your company wrong?

Why can a specific AI answer still be wrong?

A feature comparison can look convincing even when one cell describes the wrong plan. The product names are right. The table is readable. The missing condition changes the answer.

That is a useful place to start an investigation. An AI answer is a statement to check, even when it includes a citation. OpenAI’s own search guidance warns that sources and citations can be incomplete or incorrect.

This article replaces an earlier account that presented aggregate error rates and customer anecdotes without a linked, reproducible evidence record. Those figures are not used here. The distinctions below are a practical review method, not a claim about how frequently each error occurs.

Is the information outdated, or was it never supported?

Compare the statement with the fact’s effective date. Old pricing can have been correct when an article was written and still be wrong in today’s answer. That calls for a current reference and an update request, not an accusation that the publisher fabricated the amount.

A statement with no supporting evidence needs a different label. Check the source before deciding it was invented: an inaccessible page leaves a gap in the record, not proof that no source ever existed.

Did the answer drop an important condition?

Plan, region, version, and deployment mode can change a product claim. “Not available on the free plan” and “not available” are different statements. Verify the conditions alongside the feature name.

A correction should restore that scope. It should not replace a narrow negative claim with an equally broad positive one.

Did the answer confuse two products?

Similar names or overlapping descriptions can make a comparison difficult to verify. Check the product, company, and implementation the source actually discusses. If the answer assigns one company’s capability to another, preserve both the answer and the source passage before proposing a correction.

The evidence can establish a mismatch. It may not establish why the model produced it.

Is the competitor’s recommendation a factual error?

A preference for a competitor is not automatically wrong. The buyer’s requirements may favor that product. Inspect the reasons the answer gives and separate factual statements from opinions.

If the answer says your product lacks a required feature, verify the feature. If it says another interface feels easier to use, consider whether the page explains the trade-off fairly. Those observations require different responses.

What should you do first?

Pick one disputed statement and attach the evidence needed to check it. Preserve the original answer and any reliable source connection. Then decide who owns the correction.

The guide to fixing wrong company information covers the process. Use the source-tracing guide when attribution is unclear, or competitor claim monitoring when a statement keeps appearing on comparison pages.

KnitKnot’s claim intelligence keeps the disputed statement beside the evidence your team approves. Use that record to decide what needs correcting.