Public research, datasets and frameworks for understanding how AI systems discover, evaluate, trust and recommend businesses.
Our mission is simple.
Understand how AI makes commercial decisions before everyone else.
- 15 public research reports
- 20,000 AI recommendations analyzed
- 1,490 brands measured
- 5 commerce categories
- 13,000+ ecommerce stores scanned
- 100% real-world data
- No surveys
- No simulations
- No synthetic datasets
Every report published here is based on captured AI responses, real ecommerce stores and reproducible experiments.
Our primary research synthesizing everything we have learned about AI recommendation systems.
| Report | Description |
|---|---|
| The State of AI Recommendations Across Commerce 2026 | Cross-category analysis of 20,000 AI recommendations across five commerce industries. |
Research explaining why AI recommends what it recommends.
- Web Search Changes AI Recommendations
- The Fame Study
- AI Knows Your Website
- AI Confabulates Its Reasons
- Search Changes the Vocabulary
- Candidacy vs Selection
- The Model Predicts Itself
Repeated experiments performed independently across different industries.
- Beauty
- Supplements
- Coffee
- Pets
- Home & Living
Each report follows the same methodology, allowing direct comparison across categories.
Founder Lab is our public laboratory.
Instead of studying other ecommerce brands only, we also build and document our own AI-native brand in public.
Current publications:
- Founder Lab — Day Zero
- Founder Lab — Research Log
Atom Foundry researches how AI systems:
- discover businesses
- understand products
- build trust
- generate recommendations
- explain their decisions
- change behavior with web search
- influence commercial outcomes
Rather than focusing on SEO, we study the complete recommendation process used by modern AI systems.
Every report follows the same principles.
- Real AI responses
- Real ecommerce stores
- Public methodology
- Reproducible experiments
- No paid placements
- No sponsored conclusions
If we cannot measure it, we do not publish it.
Our research combines several independent datasets.
Including:
- AI recommendation experiments
- AI Commerce Score™ measurements
- Store architecture analysis
- AI readability evaluation
- AI trust analysis
- AI understanding analysis
- Cross-model comparison
- Controlled web search experiments
All reports are generated from recorded observations rather than assumptions.
This repository supports the development of the AI Commerce Intelligence™ Framework.
Core concepts include:
- AI Readability™
- AI Understanding™
- AI Trust™
- Recommendation Intelligence™
- Recommendation Share™
- Recommendation Confidence™
- AI Commerce Score™
More information:
https://atomfoundry.dev/framework
Research begins.
First ecommerce stores scanned.
AI Commerce Intelligence™ introduced.
Cross-category recommendation experiments launched.
20,000 AI recommendations collected.
15 public research reports published.
Research library launched.
reports/
flagship/
mechanisms/
categories/
founder-lab/
framework/
methodology/
datasets/
observations/
timeline/
Research Library
https://atomfoundry.dev/research
Framework
https://atomfoundry.dev/framework
If you reference this work in research, articles or presentations, please cite the original report together with the publication date.
This repository is released under the MIT License.
See LICENSE for details.