Validate
Score the product data an AI shopping surface would need before it can understand a catalogue.
AI shopping readiness
Turn product listings into AI-commerce-ready data.
AI shopping systems can only recommend and act on product data they can read and trust. KoreLens validates identifiers, variants, prices, stock, images, policies, freshness, and checkout paths, then generates the missing data layer for OpenAI-style feeds, Merchant Center workflows, and future agent integrations.
Readiness and proof, not guaranteed placement. Platform approval and ranking decisions stay with each platform.
Illustration with fictional products — not a live model answer. It shows the mechanism: assistants can only present products whose data they can read and verify. Listing and in-chat checkout are approval-dependent and decided by each platform.
Product identifiers, variants, images, stock, and price checks
Returns, shipping, support, and trust policy checks
Approval-dependent language on every platform claim
SDK and webhook ingestion when listings change
Operating layer
From visibility gap to governed proof.
Create
Normalize source-shaped listing data into governed product facts suitable for feed preparation.
Govern
Watch for drift between saved product facts, storefront pages, and platform-shaped updates.
Why it compounds
The commerce moat is product-level authority history: every SKU, variant, policy, price, source update, and readiness delta over time.