video1.0<iframe src="https://www.loom.com/embed/981586b362204ba887ee1d7633d14482" frameborder="0" width="1254" height="940" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>9401254Loomhttps://www.loom.com9401254https://cdn.loom.com/sessions/thumbnails/981586b362204ba887ee1d7633d14482-6d3e243b2cb6fafb.gif61.108This Loom explains how the team catalogs messy supply data into evidence-based product decisions using an OpenAI model with structured labels and validated source citations. It preserves raw payloads and detects conflicts, like when the drinker cup description conflicts with the supplier note saying cup holder, triggering a human review request. With a model fix, a synthetic holdout improved by one correct field decision while unsupported assertions dropped from 2 to 1. The results are stored with RailDock integration for observability, and a database benchmark processed 50,000.