Name the product being observed
A model API completion is not automatically equivalent to a consumer product with web search. Record model version when exposed; otherwise mark it unknown.
LLM Visibility Audit
Separate model knowledge from search evidence.
The free scan checks public HTML; it does not measure what every language model knows about your brand.
What RankFortune checks
- Raw HTML metadata and declared canonical
- Generic and matching-agent indexing directives
- Named search, training and user-fetch robots preferences
- One sitemap candidate, internal links and JSON-LD syntax
What you get back
- Observed issues and unknown checks, kept separate
- Sources, timestamps, scope and versioned rules
- Verification steps; actual citations and conversions remain not measured
Avoid universal prescriptions
Useful source-backed content matters to readers, but there is no universal word count, FAQ quota or set of page types that proves an LLM will recommend it.
What happens after the scan
Review justified actions against the actual page goal. Add only truthful, applicable content or markup. The free scan does not crawl competitors, connect analytics or query target AI products; independent review and observations can proceed alongside it.
FAQ
Questions this audit answers
Does this scan measure actual AI visibility?
No. It is a public-page readiness review. The report marks actual AI mentions, citations and business outcomes as not measured.
Are FAQ, pricing and comparison pages mandatory?
No. They are only candidate content patterns. Their absence is not a failure or a reason to publish low-value variations.
Can I compare two scans?
Compare the same final URL and rule version, with the same intended scope. Preserve unknown states and confirm important changes with the appropriate external evidence.