One corpus, audited in Q2 2026 across roughly 140 deterministic checks per store. Every figure on every page below traces back to it. The per-vertical cuts, the index, and the methodology that defines each term.
Free to read, free to download, and licensed CC BY 4.0. Attribution lines are published at the foot of each page.
The headline cut of the corpus. 1,091 Shopify storefronts audited, 72.2% functionally invisible to AI shopping assistants, and zero at the full AI-ready bar. Published as a downloadable CSV under CC BY 4.0.
Read the research →205 apparel storefronts, the largest single vertical in the corpus. 67% ship no Product or Organization JSON-LD, and 70% fall in the AI-invisible tier. Apparel tracks just under the corpus average on both.
Read the research →171 beauty storefronts. 74% ship no Product or Organization JSON-LD, the highest schema-absent rate of any vertical clearing the 50-store floor. App density displaces the structured-data surface.
Read the research →146 pantry, snack, sauce, and beverage storefronts. 70% ship no schema, and the dietary and allergen filters food buyers actually run are exactly the queries structured data controls.
Read the research →122 home-goods storefronts, sitting almost exactly on the corpus average. 68% schema-absent, 72% AI-invisible. Not better, not worse, and the dominant gap is the same one.
Read the research →108 electronics storefronts, the lowest schema-absent rate of the verticals above the floor at 65%. Spec-comparison buying runs through AI assistants, which read the structured block rather than the page.
Read the research →How the corpus is built and what each term means: roughly 140 deterministic checks per store, 1,077 of 1,091 classified to a primary cluster, the n=50 statistical floor, and the definitions behind AI-shoppability, schema-absent, and the invisible tier.
Read the research →