How easily can AI systems extract and cite your content?
LLMs don't cite random pages. They cite extractable content— structured, chunked, and fresh. Find out your Extractability Score in 30 seconds.
Extractability Analysis is VectorGap's proprietary scoring system that measures how easily AI systems can parse, understand, and cite your content. Unlike traditional SEO metrics that focus on search engine rankings, extractability focuses on RAG (Retrieval-Augmented Generation)—the technology that powers AI citations. When ChatGPT, Claude, or Perplexity needs to answer a question, they retrieve relevant content chunks and synthesize answers. Your content structure determines whether you get retrieved and cited, or ignored entirely. Our scoring methodology is based on peer-reviewed research from Princeton's GEO study (showing listicles comprise 50% of top citations), NVIDIA's RAG benchmarks (optimal chunk sizes of 200-500 words), and LlamaIndex best practices for structured data. Each analysis produces an actionable score with specific recommendations prioritized by impact.
Why does extractability matter for AI citations?
Extractability matters because AI systems use RAG (Retrieval-Augmented Generation) to find and cite sources—and your content structure determines if you get cited. According to Princeton's GEO study, structured content with listicles accounts for 50% of top AI citations, while tables increase citation probability by 2.5x.
Dense prose = invisible
Walls of text don't chunk well. When AI systems split your content for retrieval, dense paragraphs lose context. Your key facts get buried.
No structure = no extraction
AI needs landmarks. Without clear headers, lists, and sections, models can't reliably extract the information they need to cite you.
Stale content = deprioritized
76% of AI's most-cited pages are less than 30 days old. If you haven't updated in months, you're already behind.
What are the 5 factors in the Extractability Score?
The Extractability Score analyzes 5 weighted factors based on RAG research from NVIDIA, Princeton, and LlamaIndex: Format (25%), Structure (25%), Freshness (20%), Chunk Quality (20%), and Schema (10%).
Format Score
Weight: 25%Does your content use AI-friendly formats?
Structure Score
Weight: 25%Is your content organized for AI comprehension?
Freshness Score
Weight: 20%Is your content current?
Chunk Quality
Weight: 20%Are your sections optimally sized for RAG?
Schema Score
Weight: 10%Do you have structured data for AI?
What recommendations does the Extractability Analysis provide?
Every Extractability Analysis provides specific, prioritized fixes with estimated point impact. Recommendations are categorized by priority (High, Medium, Low) and include actionable implementation guidance—not vague suggestions like "improve structure."
Your content is 72% AI-citable
Sections too long (avg 850 words)
Break into 200-500 word self-contained sections. Each section should answer one question completely.
Missing FAQ schema
Add FAQPage JSON-LD markup to your existing Q&A content. AI systems prioritize structured FAQ data.
Format score excellent — 3 lists, 2 comparison tables detected
How does the Extractability workflow work?
The Extractability workflow follows a simple 3-step loop: Analyze (paste URL, get instant score), Fix (implement prioritized recommendations), Verify (re-analyze to confirm improvements). Track your score history over time to measure progress.
1. Analyze
Paste a URL or HTML. Get your Extractability Score with breakdown by factor.
2. Fix
Follow prioritized recommendations. Each fix shows estimated score impact.
3. Verify
Re-analyze to confirm improvements. Track score history over time.
Frequently Asked Questions about Extractability Analysis
Extractability measures how easily AI systems can parse, understand, and cite your content. It matters because AI models use RAG (Retrieval-Augmented Generation) to find sources—and your content structure directly determines whether you get cited. According to Princeton's GEO study, structured content with listicles accounts for 50% of top AI citations, while tables increase citation probability by 2.5x. Content that scores high on extractability is more likely to be retrieved, cited, and recommended by AI systems.
The Extractability Score analyzes 5 weighted factors based on RAG research from NVIDIA, Princeton, and LlamaIndex: Format Score (25%) checks for listicles, tables, and FAQ patterns; Structure Score (25%) evaluates header hierarchy and self-contained sections; Freshness Score (20%) measures content recency (76% of cited pages are under 30 days old); Chunk Quality (20%) assesses section length (200-500 words is optimal); and Schema Score (10%) checks for JSON-LD structured data like FAQPage and HowTo markup.
The optimal structure for AI citation includes: question-based H2 headers ("How does X work?" rather than "Overview"), answer-first paragraphs where the key information comes in the first sentence, self-contained sections of 200-500 words that can be understood without surrounding context, listicles and comparison tables (2.5x citation boost), FAQ patterns with clear Q&A structure, and proper JSON-LD schema markup (FAQPage, HowTo, Article). Fresh content also matters—76.4% of most-cited pages are less than 30 days old.
To improve your Extractability Score: 1) Break long prose into 200-500 word self-contained sections, 2) Convert statements to questions in H2 headers, 3) Add listicles and comparison tables where relevant, 4) Structure FAQs with clear question-answer patterns, 5) Add FAQPage JSON-LD schema to existing Q&A content, 6) Update content regularly to maintain freshness signals, 7) Put key facts in the first sentence of each section. VectorGap's analysis shows estimated point impact for each fix, so you can prioritize changes by ROI.
Why is VectorGap's Extractability Score unique?
Competitors track if you're mentioned. VectorGap tells you if your content can even be cited. Based on real RAG research from NVIDIA, Princeton, and industry practitioners.
Ready to find out if AI can cite your content?
30 seconds. One URL. Actionable results.