Beauty, supplements, coffee, pets, and home & living: five categories, run through the identical method. 20 high-intent shopping prompts, 20 times each, 4,000 recommendations mapped to real AI Commerce Scores. Every single time, how often AI recommends a brand has little to no measurable relationship to how AI-ready its store is.
Each report holds the model, the prompt structure, and the scoring method constant, and changes only the category. The leaderboard and the frequency-vs-score chart look a little different every time. The relationship between them never does.
4,000 recommendations, 238 brands. Frequency vs readiness r = +0.17.
4,000 recommendations, 371 brands. Frequency vs readiness r = −0.015.
4,000 recommendations, 228 brands. Frequency vs readiness r = +0.019.
4,000 recommendations, 405 brands. Frequency vs readiness r = −0.366.
4,000 recommendations, 271 brands. Frequency vs readiness r = +0.108.
Across beauty, supplements, coffee, pets, and now home and living, we have captured more than 20,000 recommendations and measured the same thing every time: the relationship between how often AI recommends a brand and how AI-ready its store is.
| Category | Recs | Brands | On-Index | Marketplace | r-value | Relationship |
|---|---|---|---|---|---|---|
| Beauty | 4,000 | 238 | 57.6% | 0.4% | +0.17 | None |
| Supplements | 4,000 | 371 | 33.5% | 4.9% | −0.015 | None |
| Coffee | 4,000 | 228 | 49.8% | 1.8% | +0.019 | None |
| Pets | 4,000 | 405 | 19.8% | 7.1% | −0.366 | Weak negative |
| Home & Living | 4,000 | 271 | ~60% | ~21% | +0.108 | None |
Find out exactly what AI agents see when they evaluate your store.
Illustrative example · single-site signal for atomfoundry.dev.
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