ShelfReady: Trust Layer for Product Data

Online shops copy product data from supplier emails: a spec sheet PDF, some photos, a care label, a dimension drawing. These often contradict each other. A wrong width, colour or washing instruction on a product page turns into a return: 60.4% of German online shoppers have returned items because the description or photos did not match (KPMG/EHI 2025). Since December 2024 the EU General Product Safety Regulation also requires manufacturer details, an EU responsible person and safety warnings in the shopper's language on every offer. ShelfReady is the trust layer between supplier data and the shop. Gemma 4 31B, running on AMD, extracts the spec and reads every photo, label and drawing, drawing boxes on what it sees. Seventeen checks in plain Python then compare the supplier's claims with its own evidence and with EU listing rules. What the evidence settles (a width the supplier's own drawing shows) collapses into one line; everything else goes to a person with the spec sheet and the photo side by side and the contradiction highlighted. Open facts are held back instead of guessed. The listing, safety warnings and alt text for every photo are written from verified facts only, translated into seven languages and checked in code (numbers, units, names, glossary). A publish gate blocks anything with an open legal item. Evolus agents, on the same Gemma 4 31B, write the emails that ask suppliers the open questions and answer shoppers on the shop page from the verified facts only. Measured, not claimed: on 25 supplier submissions with 59 planted defects, ShelfReady found 57 (recall 0.966, precision 0.983) with no false alarm on clean products. On 250 real Amazon Berkeley Objects listings it audited 900 photos; a person confirmed 29 of 30 photo findings. Video narration voice: ElevenLabs.