Lumenyl fetches your page HTML, robots.txt, llms.txt, and sitemap in parallel, then scores the page across 6 weighted dimensions that determine whether AI answer engines can retrieve and cite your content.
According to Princeton and IIT Delhi GEO research published at ACM KDD 2024, pages with verifiable statistics and citation-rich passages see 30% to 41% higher visibility in AI-generated answers.
Gartner projects traditional search traffic will decline 25% by 2026 as users shift to AI-generated answers.
Checks 5 AI retrieval crawlers (OAI-SearchBot, Claude-SearchBot, PerplexityBot, Googlebot, bingbot), 5 training crawlers, and 3 on-demand fetchers. Separates citation access from training policy so you can pursue AI citations while maintaining separate training-use controls. Validates sitemap discovery and robots.txt parseability.
Evaluates answer-first executive summaries, verifiable statistics paired with sources, authoritative outbound citations, concise extractable paragraphs under 80 words, FAQ-style question-answer coverage, expert evidence language, original data signals, and content freshness dates. Research shows numeric claims with citations are disproportionately extracted by AI systems.
Analyzes JSON-LD and schema.org coverage including Organization, Product, Person, FAQPage, and SoftwareApplication types. Checks primary entity machine-readability, title and meta description quality, heading hierarchy for extractable answer sections, and Open Graph metadata for consistent entity wording.
Validates HTML content type, server-rendered content availability without JavaScript, response speed under 1.5 seconds for crawler budgets, canonical URL declaration, document language metadata, and mobile viewport configuration. Most AI crawlers execute limited JavaScript; app-shell pages often look blank to retrieval indexes.
Checks brand name consistency across title, H1, and schema. Evaluates sameAs and entity links for LinkedIn, Crunchbase, Wikidata, and GitHub. Looks for author or reviewer expertise signals, authoritative outbound references, and community or knowledge-base mentions.
Checks for llms.txt with valid Markdown headings and absolute links, XML sitemap hygiene, AI content disclosure and provenance headers, and early AI content-usage preference signals aligned with IETF AIPREF work. These signals are low-cost insurance for future LLM retrieval workflows.
Modern discovery is shifting from ranked links to AI-generated answers. When a user asks ChatGPT, Perplexity, or Google AI Overviews a question, the AI system retrieves, evaluates, and synthesizes content from the web. Pages that are technically accessible, structurally clear, citation-worthy, and entity-unambiguous are far more likely to be extracted and cited. According to Semrush AI search research, pages with structured data, answer-first content, and authoritative citations consistently outperform in AI-generated answers. Lumenyl translates these patterns into an actionable readiness brief that answers one question: can frontier AI systems discover, understand, trust, extract, and cite your page?