Direct Answer
CITATION-7 uses seven weighted factors to evaluate AI visibility.
CITATION-7 is a proprietary framework developed by ROZZ for measuring and optimizing AI search visibility.
CITATION-7 evaluates content through seven weighted factors: Source Authority (22%), Content Structure (18%), Query-Answer Alignment (17%), Freshness Signals (14%), Entity Disambiguation (12%), Cross-Platform Consistency (11%), and Semantic Density (6%).
The factors contribute to an overall GEO Visibility Score ranging from 0-100.
The GEO Visibility Score predicts how likely content is to be cited by AI systems such as ChatGPT, Claude, Perplexity, and Gemini.
Detailed Explanation
Why CITATION-7 Was Developed
CITATION-7 addresses limitations in traditional SEO metrics for AI citation behavior.
A page ranking #1 on Google might never be cited by ChatGPT.
A page on position #15 might be cited consistently.
CITATION-7 was developed through empirical analysis of over 50,000 AI responses across multiple platforms.
This analysis identified content characteristics that correlate with citation likelihood.
The framework emerged from the insight that AI systems do not retrieve content the same way as search engines.
AI systems prioritize answer utility over link authority.
AI systems evaluate content at the passage level rather than the page level.
The Seven Factors Explained
1. Source Authority (22%)
The largest weighted factor measures the perceived trustworthiness of the domain and content.
AI systems evaluate authority through domain expertise signals.
AI systems evaluate author credentials.
AI systems evaluate citation by other sources.
AI systems evaluate consistency of claims.
Scoring: 0-22 points based on domain reputation, author credentials, and external validation.
2. Content Structure (18%)
AI retrieval systems parse content hierarchically.
Well-structured content gets extracted more accurately.
Key elements include:
- Schema.org markup: QAPage, HowTo, Article types that AI can parse
- Clear heading hierarchy: H1 → H2 → H3 progression
- Discrete answer blocks: Self-contained paragraphs that can be extracted
- Lists and tables: Structured data that AI can directly quote
Scoring: 0-18 points based on semantic markup quality and content organization.
3. Query-Answer Alignment (17%)
How directly does content answer likely user queries? This factor evaluates:
- Question-answer pairing: Does content explicitly state questions then answer them?
- Intent matching: Does the content address the underlying user need?
- Completeness: Does a single passage provide a satisfactory answer?
- Specificity: Does the answer apply to the exact query or is it generic?
Scoring: 0-17 points based on how precisely content maps to common query patterns.
4. Freshness Signals (14%)
AI systems increasingly prioritize recent content, especially for evolving topics. Factors include:
- Publication date: When was content first published?
- Last modified date: When was it last substantially updated?
- Temporal references: Does content reference current events, recent data?
- Update frequency: How often does the site publish new content?
Scoring: 0-14 points with decay applied based on content age and topic volatility.
5. Entity Disambiguation (12%)
AI systems must correctly identify what entities (products, companies, concepts) content discusses. Clear entity signals include:
- Explicit naming: Full product/company names rather than pronouns
- Context establishment: Defining what category an entity belongs to
- Relationship mapping: How entities relate to each other
- Version/variant specification: Which specific version of a product
Scoring: 0-12 points based on entity clarity and disambiguation quality.
6. Cross-Platform Consistency (11%)
Content that appears consistently across multiple sources gets cited more reliably. This measures:
- Multi-source presence: Is the information available from multiple domains?
- Claim consistency: Do different sources agree on key facts?
- Citation network: Do sources reference each other?
- Platform coverage: Does content perform across ChatGPT, Claude, Perplexity, Gemini?
Scoring: 0-11 points based on corroboration signals and platform coverage.
7. Semantic Density (6%)
The smallest factor measures information efficiency—how much useful information is packed into the content:
- Information-to-word ratio: Dense, factual content vs. filler
- Unique insights: Information not available elsewhere
- Actionable specificity: Concrete details vs. vague generalities
- Citation-worthy passages: Quotable statements with standalone value
Scoring: 0-6 points based on information density analysis.
Calculating the GEO Visibility Score
The GEO Visibility Score is calculated by summing the weighted factor scores:
GEO Visibility Score = (Source Authority × 0.22) + (Content Structure × 0.18) + (Query-Answer Alignment × 0.17) + (Freshness Signals × 0.14) + (Entity Disambiguation × 0.12) + (Cross-Platform Consistency × 0.11) + (Semantic Density × 0.06)
Score Range
- 80-100: Excellent - High citation likelihood; Expected citation rate 60-80%
- 60-79: Good - Moderate citation likelihood; Expected citation rate 35-60%
- 40-59: Fair - Occasional citations; Expected citation rate 15-35%
- 20-39: Poor - Rare citations; Expected citation rate 5-15%
- 0-19: Critical - Unlikely to be cited; Expected citation rate <5%
Applying CITATION-7 in Practice
- Step 1: Audit existing content. Score top 20 pages using the seven factors. Identify which factors are consistently weak across your content.
- Step 2: Prioritize improvements. Focus on the highest-weighted factors first. A 10-point improvement in Source Authority (22% weight) has more impact than a 10-point improvement in Semantic Density (6% weight).
- Step 3: Implement structured markup. Add Schema.org QAPage or Article markup to improve Content Structure scores. This is often the fastest win.
- Step 4: Create Q&A content. Publish content that explicitly poses questions and provides direct answers to maximize Query-Answer Alignment.
- Step 5: Monitor and iterate. Track actual citation rates weekly and correlate with CITATION-7 scores to refine your optimization strategy.
CITATION-7 vs. Traditional SEO Metrics
- Authority: Backlink quantity/quality vs. Expertise signals, claim consistency
- Content: Keyword density, length vs. Structure, extractability, answer directness
- Freshness: Crawl frequency vs. Substantive updates, temporal relevance
- Technical: Page speed, mobile-friendly vs. Schema markup, semantic clarity
Limitations and Caveats
CITATION-7 is a predictive framework, not a guarantee.
Actual citation behavior varies based on specific query phrasing.
Actual citation behavior varies based on AI model version and training data cutoff.
Actual citation behavior varies based on competitive landscape for the topic.
Actual citation behavior varies based on platform-specific retrieval algorithms.
The framework is most accurate for informational queries in B2B contexts.
Consumer product queries and highly contested topics may show different patterns.
Author and Foundations
Adrien Schmidt, Co-Founder & CEO, ROZZ.
Adrien Schmidt is a serial tech entrepreneur specializing in RAG systems and AI search optimization.
CITATION-7 was developed through analysis of 50,000+ AI responses across ChatGPT, Claude, Perplexity, and Gemini.
The date published is January 14, 2026.
Date Published
January 14, 2026.
Research Foundation
This methodology synthesizes findings from 35+ peer-reviewed research papers on GEO, RAG systems, and LLM citation behavior.