The audit combines technical SEO, answer engine optimization, entity clarity, structured data, trust signals, citation readiness, topical authority, and realistic prompt coverage. Scores organize the findings; they do not guarantee that an independent AI platform will index, cite, mention, or recommend a website.
The scanner reviews public robots and sitemap resources, status codes, redirects, canonical references, meta robots directives, internal discovery, page depth, and optional llms.txt availability. Use these findings to identify access contradictions before investing in more content.
Review the crawler accessibility checklist →Question headings, direct definitions, instructional passages, evidence language, source links, meaningful dates, authorship, and page clarity help reveal whether content is easy to understand and verify. Every suggested answer still requires expert editorial review.
Learn practical answer engine optimization →The report looks for clear organization and offer descriptions, contact and policy pages, schema types, supporting topics, thin pages, and realistic informational, commercial, local, comparison, transactional, and problem-solving prompts.
Build a complete AI search strategy →Start with failed access, noindex, canonical, redirect, or broken-page issues because inaccessible information cannot become a dependable source. Next, clarify the organization, authors, products, services, locations, and contact details. Improve high-value pages with direct answers, examples, primary references, transparent methods, review dates, and descriptive internal links. Finally, map uncovered prompts to the most appropriate existing page before creating another URL. The audit methodology explains how the sample is collected, while the visibility disclaimer explains the limits of scoring and generated suggestions.