How do you audit entity consistency when AI systems describe a company incorrectly?

Treat the incorrect answer as an entity-resolution problem.

List each wrong or ambiguous fact and identify the likely entity it was confused with. Then search the web for sources supporting both the correct and incorrect versions.

Build an entity evidence map covering the official website, About/Contact/Location pages, Organization/LocalBusiness schema, major business profiles, social profiles, industry databases, press coverage, partner pages, old domains, and archived/stale listings.

Correct first-party sources first and make the canonical facts explicit. Use consistent naming, stable entity URLs, clear descriptions, current leadership/location/service data, and accurate sameAs references.

Then fix high-authority third-party sources where possible. An outdated directory page or press bio can continue to reinforce old facts even after the website changes.

For renamed/restructured businesses, publish a clear transition statement rather than silently replacing all historical context. Machines need evidence that Brand A became Brand B or that a location moved.

Use structured data to reinforce relationships, not to contradict visible text. Stable @id values can help connect repeated references to the same entity.

Retest a fixed set of AI prompts over time and record not only the answer but the cited/source pages. Corrections may propagate unevenly across systems because their retrieval indexes and training sources differ.

The goal is web-wide convergence: authoritative sources repeatedly state the same current facts, making the correct entity interpretation easier than the outdated one.