SKU interpretation
Parsing supplier language into structured unit, pack, category, compatibility, and hazard fields.
Applied research
Large-model workflows for messy technical buying data.
Silver Pine Works studies how AI can read catalog descriptions, quote PDFs, supplier terms, and inventory notes without losing the evidence trail required by scientific and technical teams.
See products
Important applied findings
- Equivalence requires explanation. Procurement teams need to know why an alternate SKU is acceptable, not just that a model thinks it is similar.
- Catalog normalization is a language problem and a controls problem. Product names, units, pack sizes, and compatibility claims must be standardized with reviewable source evidence.
- Quote comparison is multi-factor. Price is only one variable; lead time, substitution risk, compliance notes, and shipping constraints shape the final recommendation.
Research tracks
Work is focused on making AI useful inside procurement controls, not on replacing sourcing teams or inventing unsupported recommendations.
Evidence comparison
Extracting vendor terms and ranking options with source-backed explanations.
Approval trails
Keeping RFQs, justifications, quote evidence, and budget notes attached to the final packet.