RecommendationIndex rankings are based on the frequency and consistency with which companies appear in AI-generated recommendations.
Our rankings analyze responses generated by major AI systems including:
- ChatGPT
- Claude
- Gemini
- Perplexity
Each ranking is based on a standardized evaluation process designed to identify which companies are most frequently recommended within a specific category.
Before reviewing the methodology, learn what RecommendationIndex is and why AI Recommendation Rankings matter.
Ranking Factors
Recommendation Frequency (40%)
Measures how often a company appears across a large set of category-specific recommendation prompts.
Organizations recommended more frequently receive higher scores.
Cross-Model Presence (25%)
Measures how consistently a company is recommended across multiple AI systems.
Organizations appearing across several models generally receive stronger scores than companies appearing in only one model.
Ranking Position (20%)
Measures where a company appears within recommendation lists.
Companies appearing near the top of recommendations receive additional weighting.
Category Relevance (15%)
Measures how consistently a company appears for highly relevant category-specific questions.
Organizations with stronger category alignment generally score higher.
Ranking Philosophy
RecommendationIndex rankings are designed to measure AI recommendation visibility rather than product quality.
A higher ranking indicates stronger recommendation presence across AI systems.
Rankings should be viewed as a benchmark of AI recommendation behavior rather than a guarantee of product superiority.
AI Visibility Assessment Methodology
The AI Recommendation Score™ framework evaluates the underlying factors that influence whether AI systems can understand, trust, and recommend an organization.
Continuous Improvement
The AI landscape evolves rapidly.
RecommendationIndex periodically reviews and updates its methodology to reflect changes in AI systems, user behavior, recommendation patterns, and industry best practices.
As AI-driven discovery continues to grow, the framework will continue evolving alongside the technologies it evaluates.
Related Resources
- AI Recommendation Score™
- Industry Rankings
- Company Score Directory
- AI Visibility Research Reports
- Run an AI Recommendation Assessment
Discover how AI systems may perceive your company and identify opportunities to improve visibility, authority, and recommendation potential.
Frequently Asked Questions
What is the AI Recommendation Score Methodology?
The AI Recommendation Score Methodology evaluates the factors that influence how AI systems understand, trust, and recommend companies.
What factors are included
in the methodology?
Category Clarity, Authority Signals, Entity Recognition, Content Coverage, and Recommendation Presence.
Does the methodology
measure Google rankings?
No. The methodology focuses on AI visibility rather than traditional search rankings.
Which AI systems does RecommendationIndex analyze?
The framework is designed around visibility signals relevant to ChatGPT, Claude, Gemini, and Perplexity.
Can companies improve their AI Recommendation Score?
Yes. Improvements in positioning, authority, entity recognition, content coverage, and recommendation visibility can strengthen overall performance.