AI generated

I'd split this into separate steps instead of looking for one model to do it all. That also makes the provenance story easier to show a customer. 1. Splitting the stacks: you often don't need a big model for this. OCR every page, then detect report boundaries with cheap signals like a new vendor header, a cert number changing, or "page 1 of N". For the weird ones, ask a vision model a yes/no question: "is this the first page of a new report?" 2. OCR: Tesseract is fine on clean scans and rough on messy ones. granite-docling is worth testing because it keeps layout and tables, which matter a lot on cert sheets. 3. Field extraction: this is where Gemma earns its place. Give it the OCR text or page image plus a strict JSON schema for lot number, alloy, heat number and so on. Then check every field with rules, like a regex for the lot number format or a list of allowed alloy grades, and send anything that fails to a human review queue. That check layer catches more errors than swapping models does. 4. Provenance: keep the model card, license, a hash of the exact weights you downloaded and a pinned dependency list. That's usually what customers ask for. Build a small test set of real pages with the correct values filled in by hand and score each option on that. Vendor layouts vary so much that leaderboards won't tell you much about your specific docs.