Product-page-level AI-readiness corpus for e-commerce catalogues

DATAÏADSbuyer room · privateManage buyer room

Product-page-level AI-readiness corpus for e-commerce catalogues: for each scanned public product URL, a Readiness Score decomposed into a 2x3 matrix — two parallel readings (agent-agnostic and major-AI-specific) across three sub-scores: (1) Reach & Read (fetch success, robots.txt, sitemap, raw vs rendered HTML divergence, plus confirmation that Gemini/OpenAI actually read the URL); (2) Product Data (crawler-side Schema.org Product/Offer presence, and strict-JSON facts extracted independently on the Gemini and OpenAI sides with multi-provider confidence); (3) Semantic Decisioning (use cases, target audience, differentiation, FAQ, specs, reviews extracted from the content the agents actually observed). Underlying raw material is a paired capture per URL — the site's raw HTML vs its rendered DOM, the crawler-visible markup, and the JSON that each AI provider independently derives from it. Published as the Agentic Commerce Index plus a Baromètre E-commerce France 2026 sector benchmark, from a Google-certified Generative AI for Marketing partner with a Media Readiness Program and paid product-feed/Shopping expertise.