AEOlens groups its audit into four categories: Content, Technical, Schema, and Structure. Together these checks measure whether AI systems can fetch your page, identify what it is about, extract direct answers, and trust the result enough to cite it.
Every check has a practical implementation target. This page documents what the audit tests, why the signal matters, and which AI models benefit when the issue is fixed.
Content checks measure whether AI systems can quote your page confidently and without guessing.
What it tests: Measures factual density versus vague marketing language.
Why it matters: Models cite pages that state facts directly instead of relying on hype or abstract positioning.
Models helped: ChatGPT, Perplexity, Gemini, Grok, Claude
What it tests: Checks whether the page contains enough supporting detail to answer follow-up questions.
Why it matters: Thin pages are less likely to be used as a source when models need substance, definitions, and implementation detail.
Models helped: ChatGPT, Perplexity, Gemini, Claude
What it tests: Looks for product names, model names, concepts, and related entities that anchor the topic clearly.
Why it matters: Stronger entity coverage helps AI systems match your page to a specific query instead of a vague category.
Models helped: ChatGPT, Perplexity, Gemini, Grok, Claude
What it tests: Determines whether important answers can stand alone without surrounding context.
Why it matters: Citation systems prefer passages that can be quoted cleanly in a response without extra interpretation.
Models helped: ChatGPT, Perplexity, Claude
What it tests: Checks whether the page answers core questions with declarative sentences near the top.
Why it matters: Answer-first copy is easier for models to extract, summarise, and cite in a single pass.
Models helped: ChatGPT, Perplexity, Gemini, Claude
What it tests: Flags vague references like “it”, “this”, or “they” when the subject is unclear.
Why it matters: Clear noun references reduce ambiguity when models chunk and quote the page.
Models helped: ChatGPT, Claude, Gemini
What it tests: Looks for concrete counts, limits, timelines, and score ranges.
Why it matters: Specific numbers make the page easier to cite and improve trust compared with unspecific claims.
Models helped: ChatGPT, Perplexity, Gemini, Grok, Claude
Technical checks confirm that crawlers and AI fetch systems can access and index the page correctly.
What it tests: Reviews robots.txt directives for the major AI crawler user agents.
Why it matters: A blocked crawler cannot fetch the page, which removes it from the citation set entirely.
Models helped: ChatGPT, Perplexity, Gemini, Grok, Claude
What it tests: Checks whether core content appears in raw HTML before client-side rendering.
Why it matters: If the value proposition or answers are JavaScript-only, some crawlers miss them or index weaker fragments.
Models helped: ChatGPT, Perplexity, Gemini, Claude
What it tests: Looks for secure delivery and mixed-content problems.
Why it matters: Broken or partially insecure pages are less reliable sources for crawlers and answer engines.
Models helped: ChatGPT, Perplexity, Gemini, Grok, Claude
What it tests: Checks whether the page remains readable and functional on mobile layouts.
Why it matters: Many crawlers fetch responsive variants, and broken mobile markup often correlates with missing content blocks.
Models helped: Google AI, Perplexity, ChatGPT
What it tests: Verifies that the page declares a canonical URL consistently.
Why it matters: Canonical tags help AI systems consolidate duplicate URLs into a single authoritative citation target.
Models helped: ChatGPT, Perplexity, Gemini, Claude
What it tests: Checks for a sitemap and whether important public pages are discoverable.
Why it matters: Sitemaps improve crawl discovery and reinforce which pages matter most.
Models helped: ChatGPT, Perplexity, Gemini, Grok, Claude
What it tests: Measures whether the page responds quickly enough for reliable crawling.
Why it matters: Slow pages are more likely to time out, truncate content, or degrade the crawl budget available to AI fetchers.
Models helped: ChatGPT, Perplexity, Gemini, Claude
What it tests: Separately tracks OpenAI-specific crawler access and policy alignment.
Why it matters: OpenAI uses multiple fetch patterns, and misconfiguration can block citation while leaving standard SEO intact.
Models helped: ChatGPT
What it tests: Checks whether `Google-Extended` is explicitly allowed.
Why it matters: Gemini and AI Overviews use different access signals from standard Googlebot crawling.
Models helped: Gemini
What it tests: Determines whether missing pages return proper 404 status codes.
Why it matters: Soft 404s pollute crawl signals and weaken trust in the site’s URL structure.
Models helped: ChatGPT, Perplexity, Gemini, Claude
What it tests: Looks for language and locale signals when multiple variants exist.
Why it matters: Consistent language targeting reduces duplicate or mismatched citations across regional variants.
Models helped: Gemini, Perplexity, ChatGPT
Schema checks measure whether the page provides explicit machine-readable context and trust signals.
What it tests: Checks for structured data such as `SoftwareApplication`, `Organization`, or similar core entities.
Why it matters: Schema reduces inference and gives models explicit product, company, and page context.
Models helped: ChatGPT, Perplexity, Gemini, Claude
What it tests: Requires visible question-and-answer content plus FAQPage schema.
Why it matters: FAQ blocks are one of the clearest answer-engine citation formats when the questions match user intent.
Models helped: ChatGPT, Perplexity, Gemini, Claude
What it tests: Looks for About information, contact details, and evidence that the publisher is identifiable.
Why it matters: Pages with explicit ownership and expertise signals are easier for models to trust and cite.
Models helped: Gemini, Claude, ChatGPT
What it tests: Checks for page title, description, image, and URL metadata used by social and preview systems.
Why it matters: Consistent OG metadata reinforces the canonical summary and improves entity matching.
Models helped: Grok, ChatGPT, Perplexity
What it tests: Looks for `twitter:card`, title, description, and image coverage.
Why it matters: This is especially relevant for Grok because X-native metadata shapes how pages are previewed and discussed.
Models helped: Grok
What it tests: Checks whether `/.well-known/llms.txt` or `/llms.txt` exists and provides useful site context.
Why it matters: llms.txt helps models interpret what your site does, which pages matter, and how it should be cited.
Models helped: ChatGPT, Claude, Gemini, Perplexity
What it tests: Looks for visible update dates and `dateModified` schema properties.
Why it matters: Freshness signals help answer engines prefer newer, maintained sources over stale landing pages.
Models helped: Gemini, Perplexity, ChatGPT, Claude
Structure checks focus on how quickly AI systems can identify the page purpose and parse the document hierarchy.
What it tests: Checks that the page has exactly one descriptive H1.
Why it matters: A precise H1 anchors the topic for extraction, summaries, and snippets.
Models helped: ChatGPT, Perplexity, Gemini, Claude
What it tests: Verifies sequential heading levels with no skipped structure.
Why it matters: Consistent headings make answer sections easier to chunk, rank, and quote.
Models helped: ChatGPT, Perplexity, Gemini, Claude
What it tests: Checks whether the value proposition appears early in the document instead of after decorative blocks.
Why it matters: Important content buried deep in the DOM is more likely to be missed or weighted down.
Models helped: ChatGPT, Perplexity, Gemini, Claude
What it tests: Checks whether the description exists, fits length guidelines, and states the page purpose clearly.
Why it matters: A precise meta description reinforces the page summary used in previews and citations.
Models helped: ChatGPT, Perplexity, Gemini, Grok, Claude
What it tests: Looks for structural elements such as `main`, `section`, and `article` instead of anonymous wrappers only.
Why it matters: Semantic markup gives AI systems stronger signals about page regions, content blocks, and primary topics.
Models helped: ChatGPT, Perplexity, Gemini, Claude
The methodology page explains how AEOlens crawls raw HTML and rendered output, assigns points, and ranks structural issues by citation impact.
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