The hard part of applied AI is not the first demo. It is knowing whether the system is right often enough to put in front of customers, and noticing when it stops being right.
Evaluation first
Every engagement starts by building an evaluation set from your own data: real questions, real products, real edge cases, with the answers your team would accept. Every prompt, model and retrieval change is measured against it before it ships.
Product-data enrichment
For catalogue teams, the most valuable use is often the least glamorous: filling missing attributes, normalising descriptions and translating at scale. We run enrichment through a review queue so a person approves changes before they reach the storefront.