1. Find what answer engines say
Start with AI perception summary: the claims that answer engines make about you. Expect a mix of positive, neutral, and incorrect claims.2. Separate the three types
- True but not helpful: for example, “enterprise-focused” when you want mid-market customers. This is a positioning problem.
- False: for example, a missing feature that you actually have, or an old price. This is a source problem: a cited source is incorrect.
- True and damaging: an honest weakness. Content cannot correct this type. Possibly, the product can.
3. Monitor the important claims
Use Monitor perceptions for the claims that are important. A tracked perception keeps a history. Thus, you can prove that a change had an effect. Also, you can find a reversal in weeks, not at the next review.4. Find the source of a false claim
Open the conversations that contain the claim, and read the cited sources. Almost always, a false claim comes from a cited source that contains an error. The fix is to correct that page. Do not publish a rebuttal on your own site, because answer engines give it less weight.5. Use sentiment as an early warning
Sentiment tells you the general mood, not the claim. A decrease usually means that a new source has an effect on the answers. Find that source in Most cited sources.6. Make it a routine
- Run conversations weekly.
- Track a small number of perceptions.
- Record what you changed, and when.