Every check AEOlens runs

Measures factual density versus vague marketing language.

Models cite pages that state facts directly instead of relying on hype or abstract positioning.

Checks whether the page contains enough supporting detail to answer follow-up questions.

Thin pages are less likely to be used as a source when models need substance, definitions, and implementation detail.

Looks for product names, model names, concepts, and related entities that anchor the topic clearly.

Stronger entity coverage helps AI systems match your page to a specific query instead of a vague category.

Determines whether important answers can stand alone without surrounding context.

Citation systems prefer passages that can be quoted cleanly in a response without extra interpretation.

Checks whether the page answers core questions with declarative sentences near the top.

Answer-first copy is easier for models to extract, summarise, and cite in a single pass.

Flags vague references like “it”, “this”, or “they” when the subject is unclear.

Clear noun references reduce ambiguity when models chunk and quote the page.

Looks for concrete counts, limits, timelines, and score ranges.

Specific numbers make the page easier to cite and improve trust compared with unspecific claims.

Reviews robots.txt directives for the major AI crawler user agents.

A blocked crawler cannot fetch the page, which removes it from the citation set entirely.

Checks whether core content appears in raw HTML before client-side rendering.

If the value proposition or answers are JavaScript-only, some crawlers miss them or index weaker fragments.

Looks for secure delivery and mixed-content problems.

Broken or partially insecure pages are less reliable sources for crawlers and answer engines.

Checks whether the page remains readable and functional on mobile layouts.

Many crawlers fetch responsive variants, and broken mobile markup often correlates with missing content blocks.

Verifies that the page declares a canonical URL consistently.

Canonical tags help AI systems consolidate duplicate URLs into a single authoritative citation target.

Checks for a sitemap and whether important public pages are discoverable.

Sitemaps improve crawl discovery and reinforce which pages matter most.

Measures whether the page responds quickly enough for reliable crawling.

Slow pages are more likely to time out, truncate content, or degrade the crawl budget available to AI fetchers.

Separately tracks OpenAI-specific crawler access and policy alignment.

OpenAI uses multiple fetch patterns, and misconfiguration can block citation while leaving standard SEO intact.

Checks whether `Google-Extended` is explicitly allowed.

Gemini and AI Overviews use different access signals from standard Googlebot crawling.

Determines whether missing pages return proper 404 status codes.

Soft 404s pollute crawl signals and weaken trust in the site’s URL structure.

Looks for language and locale signals when multiple variants exist.

Consistent language targeting reduces duplicate or mismatched citations across regional variants.

Checks for structured data such as `SoftwareApplication`, `Organization`, or similar core entities.

Schema reduces inference and gives models explicit product, company, and page context.

Requires visible question-and-answer content plus FAQPage schema.

FAQ blocks are one of the clearest answer-engine citation formats when the questions match user intent.

Looks for About information, contact details, and evidence that the publisher is identifiable.

Pages with explicit ownership and expertise signals are easier for models to trust and cite.

Checks for page title, description, image, and URL metadata used by social and preview systems.

Consistent OG metadata reinforces the canonical summary and improves entity matching.

Looks for `twitter:card`, title, description, and image coverage.

This is especially relevant for Grok because X-native metadata shapes how pages are previewed and discussed.

Checks whether `/.well-known/llms.txt` or `/llms.txt` exists and provides useful site context.

llms.txt helps models interpret what your site does, which pages matter, and how it should be cited.

Looks for visible update dates and `dateModified` schema properties.

Freshness signals help answer engines prefer newer, maintained sources over stale landing pages.

Checks that the page has exactly one descriptive H1.

A precise H1 anchors the topic for extraction, summaries, and snippets.

Verifies sequential heading levels with no skipped structure.

Consistent headings make answer sections easier to chunk, rank, and quote.

Checks whether the value proposition appears early in the document instead of after decorative blocks.

Important content buried deep in the DOM is more likely to be missed or weighted down.

Checks whether the description exists, fits length guidelines, and states the page purpose clearly.

A precise meta description reinforces the page summary used in previews and citations.

Looks for structural elements such as `main`, `section`, and `article` instead of anonymous wrappers only.

Semantic markup gives AI systems stronger signals about page regions, content blocks, and primary topics.