An AI visibility audit can test the public foundation around a brand. It cannot establish what an answer engine has said, will say or will recommend.
The audit examines public accessibility, entity consistency, technical implementation and structured-data evidence. It can identify a mismatch between markup and visible facts, absent organization details or pages that may be difficult to retrieve.
Prioritize factual corrections and access issues first, then document ownership, make claims specific and connect primary sources. Treat findings as an implementation backlog rather than a prediction of answer-engine exposure.
It does not measure live mentions, citations, sentiment, recommendations or rankings. For documented observations in selected engines, a human analyst must conduct a Deep AI Engine Investigation; no outcome is guaranteed.
Find observable technical and entity gaps before making claims about live AI behavior.
Start an automated scan