See how easily AI models like ChatGPT and Perplexity can parse, extract, and cite your content. Get a 0-100 score with actionable improvements.
Answer density, structure, snippet readiness, entity clarity, schema markup, and conciseness
Include headings and structure in your paste. AI models care about hierarchy!
6 categories · 0-100 score · Extractable snippets · Before/after examples
Can AI systems understand and cite your content? We score your structure, markup, and architecture. High extractability makes your content easier to quote accurately. Low extractability suggests your best points may be harder to find and reuse.
Paste your content or enter a URL. The scorer analyzes heading hierarchy, definition clarity, schema markup presence, entity consistency, and content structure. Each factor is scored individually, then rolled into a single 0-100 extractability grade.
High-extractability content follows a predictable pattern: clear H2/H3 headings that match common questions, concise definitions in the first sentence of each section, structured data that labels entities explicitly, and FAQ blocks that mirror how people query AI models. Structured data can make content easier for AI and search systems to parse accurately.
The report highlights specific lines and sections that may be harder for AI models to parse. You get fix-it recommendations ranked by impact. Many pages can improve their extractability score by adding basics: schema markup, consistent headings, and clear entity definitions. Learn how the AI Extractability Score works.
AI models do not read content the way humans do. Research suggests they often parse structure first, then meaning. A wall of text with no headings can be harder for extraction algorithms to process. The same information broken into labeled sections with consistent formatting tends to be more citable.
Heading hierarchy matters more than keyword density. Models often use H2 and H3 tags as topic boundaries. If your headings are vague or inconsistent, it can make it harder for models to determine what each section is about. Keyword density helps traditional search, but heading clarity helps AI extraction.
Definitions belong in the first sentence. When you define a concept, put the definition up front. "AI extractability is the ease with which language models can parse and cite your content" beats burying the definition in paragraph three. Clear first-sentence definitions are easier for AI systems to extract and summarize.
FAQ sections support citation readiness. Question-and-answer pairs map directly to how users query AI. A well-structured FAQ can make answers easier to parse and reuse. Combine this with the Content SEO Analyzer to cover both traditional and AI search signals.
Start with schema markup. Adding Article, FAQ, or HowTo structured data is often one of the fastest structural improvements to make when extractability is weak. Google's structured data documentation covers the formats that search and AI systems may use structured signals from.
Next, audit your heading hierarchy. Every page should have one H1, logical H2 sections, and H3 subsections where needed. Headings should read like a table of contents. If you stripped everything else away, a reader should understand the page from headings alone. The Blog Post Analyzer catches heading structure issues alongside content quality.
Finally, check your internal linking strategy. Internal links can help clarify entity relationships and topic clusters. Pages that link to related content with descriptive anchor text are easier for users and crawlers to navigate. After fixing extractability, run the LLM Visibility Checker to check whether your AI visibility improves. For related terminology, see AI Citation.
After optimizing extractability, check whether AI models are surfacing your content in their responses.
Combine AI extractability with traditional SEO optimization for comprehensive search visibility.
Ensure your blog content meets quality standards that both AI systems and human readers value.