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 means AI quotes you. Low means your words stay buried.
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. Pages with structured data get up to 40% more AI citations than pages without it.
The report highlights specific lines and sections that AI models will struggle to parse. You get fix-it recommendations ranked by impact. Most pages can gain 15 to 25 points by adding extractability basics: schema markup, consistent headings, and clear entity definitions.
AI models do not read content the way humans do. They parse structure first, then meaning. A wall of text with no headings is nearly invisible to extraction algorithms. The same information broken into labeled sections with consistent formatting becomes highly citable.
Heading hierarchy matters more than keyword density. Models use H2 and H3 tags as topic boundaries. If your headings are vague or inconsistent, the model cannot 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. Models extract the first complete sentence after a heading more than any other.
FAQ sections are extraction gold. Question-and-answer pairs map directly to how users query AI. A well-structured FAQ can generate citations across dozens of different prompts. 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 the single fastest way to improve extractability. Most pages gain 15 to 25 points from schema alone. Google's structured data documentation covers the formats AI models also rely on.
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. AI models follow internal links to build entity relationships. Pages that link to related content with descriptive anchor text create a knowledge graph that models can navigate. After fixing extractability, run the LLM Visibility Checker to see if your improvements translate into actual AI recommendations.
After optimizing extractability, check whether AI models are actually 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.