Research and white papers on AI catalog readiness.
Longer-form research on how AI shopping systems read, trust, and recommend product catalogs, and what commerce teams can do about it. Practical field notes live on the Blog.
The white papers
White Paper
The AI Shelf
How agentic shopping is rewriting product discovery, and what commerce leaders must do now.
For CMOs, CDOs & Heads of eCommerce · June 2026
Read the white paper →White Paper
Recommendation Eligibility
A commerce leader’s guide to being found, understood, trusted, and recommended by AI.
For CDOs & Heads of eCommerce · June 2026
Read the white paper →The questions behind the Commerce Eligibility Index™.
Our research focuses on the catalog data layer that decides whether AI shopping agents can find, understand, trust, and recommend products. These are the themes we publish on.
How AI shopping systems assemble a product picture, and why some catalogs get surfaced for realistic shopping queries while others stay invisible.
Why machine-checkable, internally consistent product claims matter for recommendation, and how to make catalog quality inspectable rather than asserted.
How commerce teams keep catalog quality from drifting backward as products, feeds, and descriptions change over time.
Want the white papers as they publish?
Tell us about your catalog and we will share relevant research and a readiness view for your own products.
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