Check how well your content is recognized by AI search engines
6 key criteria that AI search engines use to evaluate content
AI crawlers must be able to actually access content for citation and summarization to be possible. We measure whether JS rendering is needed, robots.txt/X-Robots-Tag blocking, and whether core text exists in the initial HTML to ensure 'readability'. This goes deeper than basic SEO health checks by considering LLM crawler characteristics (no rendering support).
Search engine indexing prohibition settings can block AI crawler access. If noindex directives are found in HTML meta robots tags or HTTP X-Robots-Tag headers, AI won't index that content, significantly lowering citation probability. Real-time search-based AI like Claude or Perplexity strictly follow these directives.
Whether a website's main content is server-side rendered (SSR) and included in the initial HTML, or appears only after client-side rendering (CSR) with JS execution is crucial. AI crawlers generally don't execute JS and only collect HTML source, so CSR-dependent sites have significantly lower citation probability.
If web application firewalls (WAF) like Cloudflare, anti-bot scripts, or CAPTCHA are active, AI bots have difficulty accessing content, lowering citation probability. HTTP 503 responses, challenge pages, User-Agent based blocking can prevent AI crawlers from accessing actual content.
Evaluates whether site content is well-indexed and has a discoverable structure. XML sitemaps, Common Crawl allowance, internal link structure, RSS/Atom feeds directly affect the probability of AI discovering and citing various pages from that site. Claude particularly considers sites with RSS feeds as active and trustworthy sources.
Schema.org-based structured data (JSON-LD, etc.) greatly helps AI understand content accurately and extract snippets. Bing's GPT-4-based Copilot uses schema markup to interpret web content, and Perplexity also confidently cites content when schemas are present. Additionally, visual structural elements like question-style subheadings (H2/H3) with 40-80 character summaries, tables, lists, and code blocks are key factors for LLMs to generate accurate answers and increase trust scores.
Accurately analyzes how AI crawlers actually perceive content
Provides fast and efficient analysis based on static parsing
Provides detailed analysis reports including improvement points and official documentation links