Two-stage scoring across a large catalog
Takes thousands of supplier and product records in inconsistent formats, assesses the supplier before the product, and prepares approved items for publishing — with every rejection reason recorded.
The situation it addresses
Supplier catalogs arrive with inconsistent titles, categories, prices, shipping terms, and stock information. Reviewing products first creates repeated work, because one unsuitable supplier invalidates every product attached to it. Rejection reasons end up scattered across spreadsheets, so the same item gets reconsidered months later.
Where AI is required
Assessing commercial potential, usefulness, and how well a product is described — from text written by hundreds of different suppliers with no shared standard.
How it runs
- 01Catalog imported from source systems
- 02Records standardized — identifiers, categories, prices, stock
- 03Duplicate suppliers merged into one
- 04Suppliers scored on location, fulfillment, returns, and data quality
- 05Products from suppliers below threshold excluded before any product work
- 06Remaining products assessed on potential, margin, and description quality
- 07Pricing inputs calculated — cost, fees, shipping, tax, target margin
- 08A person approves
- 09Approved items created as drafts, never published automatically
Decision output: Approve · Review · Hold · Reject
What makes it trustworthy
Suppliers and products are scored separately, so a promising product cannot conceal an unreliable supplier. Every rejection carries a structured reason. Repeat runs process only what is new or changed. One faulty record does not halt the whole run. And nothing publishes without a person approving it.