The Only Scoring System Built for Google AI Overviews and LLM Search Results

The AI Friendly Score uses a sophisticated multi-stage calculation process:

1. Page Type Classification: Our AI first identifies your page type through URL patterns, schema markup, and content structure analysis.
2. Contextual Weight Application: Based on page type, we apply appropriate weights to each metric. For example, author credentials are critical for health articles but not required for homepages.
3. Sub-Metric Evaluation: Each of the 200+ factors is evaluated and normalized to a 0-100 scale.
4. Pillar Aggregation: Sub-metrics are combined using our proprietary weighting system to create pillar scores.
5. Final Score Calculation: The four pillar scores are weighted and combined to produce your final AI Friendly Score.

Google's AI systems prioritize content that is clear, comprehensive, and directly answers user queries. Our Content Excellence pillar evaluates:

• Readability: Optimal Flesch-Kincaid scores (60-70 for general content)
• Structure: Proper heading hierarchy and information chunking
• Semantic Depth: Entity coverage and topical comprehensiveness
• Answer Optimization: Direct responses to likely queries in opening paragraphs
• Conversational Alignment: Natural language that mirrors how users ask questions

Experience, Expertise, Authoritativeness, and Trustworthiness signals help AI systems determine content reliability:

• Experience: First-hand accounts, case studies, "I tested" language
• Expertise: Author credentials, technical accuracy, industry terminology
• Authority: Quality backlinks, brand mentions, Knowledge Graph presence
• Trust: HTTPS, citations, fact-checking, update frequency

For YMYL (Your Money or Your Life) content, E-E-A-T weight increases to 40% of the total score.

AI systems need to access, crawl, and understand your content structure:

• Structured Data: Comprehensive Schema.org implementation with entity interlinking
• RAG-Friendliness: Content chunked into 200-800 token semantic segments
• Performance: LCP < 2.5s, INP < 200ms, CLS < 0.1
• AI Crawler Access: Proper robots.txt configuration for Googlebot and Google-Extended

Our proprietary weights are based on:

• Google's Official Guidance: Direct analysis of Search Central documentation
• Empirical Testing: Correlation analysis of 10,000+ pages and their AI Overview performance
• LLM Behavior Research: Understanding how Gemini 2.5 processes and synthesizes information
• Query Pattern Analysis: Studying the "query fan-out" technique and multi-faceted searches
• Industry Variations: YMYL content receives adjusted weights for trust signals