AI visibility starts with a simple question: can an answer engine reach the right page, understand what it says, and trust it enough to use it as evidence? A useful audit follows that sequence instead of beginning with a score.
Confirm that important pages are reachable
Test the canonical URL as an unauthenticated visitor. Review status codes, redirects, robots rules, CDN challenges, and internal links. A page that works in a normal browser can still be difficult for a crawler when protection rules require JavaScript or treat unfamiliar user agents as hostile.
Make the subject explicit
Each important page should identify its subject, intended audience, and purpose early. Titles and headings should agree with the visible copy. Product names, organizations, locations, dates, and authors should be written consistently so retrieval systems do not have to infer basic relationships.
Check the evidence behind the claim
Direct answers are useful, but unsupported claims are weak source material. Add methods, definitions, examples, dates, comparisons, and links to original evidence where they help a reader verify the statement. Keep promotional language separate from factual assertions.
Use structured data as clarification
JSON-LD can make the page type and its entities explicit. It should describe content that users can see and should not introduce claims that are absent from the page. Structured data improves interpretation; it does not guarantee selection or citation.
Measure the answer layer
Track a representative set of prompts by platform, country, language, and date. Record whether the brand appears, how it is described, which sources are cited, and whether the answer is accurate. Connect those observations to qualified visits and conversions before deciding what to improve next.