AI Visibility audit suite
Structured data depth, llms.txt presence, AI crawler robots.txt allowance, entity sameAs network, FAQ schema, and citation-friendly content structure.
What is an AI visibility audit?
An AI visibility audit checks whether a website gives AI answer engines enough crawler-visible evidence to understand the brand, trust the claims, and cite the right pages. AuditHQ checks structured data, llms.txt, robots.txt access for AI crawlers, entity sameAs signals, author and date signals, semantic HTML, internal links, and citation-friendly page structure.
AI visibility vs SEO
Classic SEO focuses on search-engine crawling, ranking, snippets, and link equity. AI visibility overlaps with SEO, but adds signals that matter to ChatGPT, Perplexity, Claude, Gemini, and Google AI Overviews: entity clarity, structured Q&A, quotable passages, verifiable sources, current dates, and AI-readable site files.
What AuditHQ checks for ChatGPT, Perplexity, and Google AI Overviews
AuditHQ looks for crawler-visible page copy, Organization and Person schema, Article and FAQ schema, BreadcrumbList markup, llms.txt and llms-full.txt, AI crawler access, internal anchor text, definitions, tables, statistics, citations, author bylines, and current modified dates. Those are the signals that make a page easier for answer engines to retrieve and cite.
Typical AI visibility findings
Common findings include a JavaScript-only shell hiding the real content, robots.txt blocking an AI crawler, missing llms.txt, weak sameAs links, no Wikidata or Crunchbase reference where one exists, thin pillar pages, missing author or updated date, no glossary definitions, and quantified claims without citations.