A public research library exploring how AI systems discover, understand, trust, recommend, and influence commerce.
Built by Atom Foundry.
Foundational concepts for understanding AI-native commerce.
- AI Readability™
- AI Understanding™
- AI Trust™
- Recommendation Intelligence™
- AI Commerce Graph™
- AI Native Commerce™
Empirical research based on real recommendation data.
- State of AI Recommendations Across Commerce 2026
- AI Recommends by Fame, Not Store Quality 2026
Current Recommendation Intelligence™ dataset:
- 20,000 AI recommendations
- 1,490 distinct brands
- 5 ecommerce categories
- 100 shopping intents
Categories analyzed:
- Beauty
- Supplements
- Coffee
- Pets
- Home & Living
The future of commerce will not be won by visibility alone.
As AI systems increasingly influence discovery, evaluation, recommendation, and purchasing decisions, the key question becomes:
Why are some businesses consistently recommended while others remain invisible?
Our research suggests that visibility is only the first layer.
Understanding, trust, recommendation behavior, decision confidence, and entity relationships increasingly determine which businesses AI systems choose to surface.
These whitepapers explore the emerging infrastructure, signals, entities, and ranking factors behind AI-driven commerce.
Current areas of investigation:
- Recommendation Frequency™
- Recommendation Stability™
- AI Trust Signals™
- Brand Fame Effects
- Recommendation Confidence™
- AI Commerce Graph™ relationships
- AI-Native Commerce behaviors
Build the world's most comprehensive public research library on AI-driven commerce.
Our objective is to understand how AI systems:
- Discover businesses
- Interpret information
- Build trust
- Select recommendations
- Influence purchasing decisions
- Route revenue across the internet
Atom Foundry
Building the AI Commerce Intelligence Layer™
AI Commerce Intelligence™ Recommendation Intelligence™ AI Commerce Graph™