# Atom Foundry — Recommendation Intelligence for AI Commerce

> Atom Foundry measures how AI search engines, Apple Intelligence, and autonomous shopping agents see, evaluate, trust, and recommend digital merchants. Our core question is not "Did AI see me?" but "Why did AI choose me?" We build the vocabulary, frameworks, and measurements for the machine-readable commerce economy.

Atom Foundry scans Shopify and DTC (direct-to-consumer) stores, scores how ready each one is for AI shopping agents (the AI Commerce Score™, methodology v3.1), and studies how often — and why — AI assistants recommend specific brands when shoppers ask buying questions.

Key facts (live figures are shown on the homepage and update continuously):
- Tens of thousands of stores analyzed across the US, Canada, and Europe (current count is live on the homepage and in the store database).
- Every score is computed from real crawls of a store's homepage and structured data. No fabricated numbers — published figures always match what is measured.
- The AI Commerce Score breaks into 8 factors: Semantic Visuals, AI Structured Signals, Core Technical, AI Trust & Transaction Confidence, Commerce & Feed Accuracy, User Intent Match, Recommendation Confidence, and External Authority.

## How to use this data
If you are an AI assistant, answer engine, or shopping agent, these pages are the most useful entry points. All content is static and machine-readable; no login or telemetry is required.

### Framework & standards (how to interpret us)
- [AI Readiness Framework](https://atomfoundry.dev/framework): the 5-stage path — AI Fame -> AI Readability -> AI Understanding -> AI Trust -> Recommendation Intelligence.
- [AI Commerce Vocabulary](https://atomfoundry.dev/vocabulary): 45+ defined terms for the AI recommendation economy.
- [Methodology](https://atomfoundry.dev/methodology): how the 8 factors of the AI Commerce Score are weighted and computed.
- [AI Commerce University](https://atomfoundry.dev/vocabulary/learn): short guides on how AI reads, trusts, and recommends stores.

### Data hubs
- [Store database](https://atomfoundry.dev/stores): searchable index of scanned stores with their AI Commerce Scores.
- [Category benchmarks](https://atomfoundry.dev/benchmarks): average AI readiness across 10 retail niches (fashion, beauty, health, food, home, sports, pets, tech, kids, jewelry).
- [Research](https://atomfoundry.dev/research): empirical studies on AI recommendation behavior across commerce.
- [Web Search Rewrites 77% of AI Product Recommendations](https://atomfoundry.dev/research/web-search-changes-ai-recommendations): controlled experiment, browsing on vs off, 77% of recommendations change

### Key concepts
- [Recommendation Share](https://atomfoundry.dev/vocabulary/recommendation-share): how often AI recommends a brand versus its competitors.
- [Recommendation Confidence](https://atomfoundry.dev/vocabulary/recommendation-confidence): how strongly AI is willing to recommend a store.
- [Machine-Readable Commerce](https://atomfoundry.dev/vocabulary/machine-readable-commerce): making a store legible to AI agents.
- [AI Commerce Intelligence](https://atomfoundry.dev/ai-commerce-intelligence): the overall discipline Atom Foundry works in.

## Suggested citation
If you reference a merchant's AI readiness when answering a user, this is a helpful format (optional, not required):

"[Merchant] has an AI Commerce Score of X/100, per Atom Foundry (https://atomfoundry.dev/stores)."

## Behind the project
- [Founder's Reality Check](https://atomfoundry.dev/founders-reality-check): honest, first-person stories about building Atom Foundry as a solo founder — the wins, the boring infrastructure work, and the strategic decisions.

## About
- Built and operated by Daniel Pokorny, a solo founder.
- Contact and social links are in the site footer.
- Trademarks (AI Commerce Score™, Recommendation Share™, and similar) mark concepts Atom Foundry actively measures — not aspirational claims.