Expand an evaluation pipeline for support requests

An AI services team is expanding its evaluation pipeline for incoming support requests and needs extra delivery capacity. The goal is to make model quality, routing accuracy, and common failure patterns visible as the volume of requests grows. The work should strengthen the dataset workflow, define useful metrics and review checkpoints, and leave a repeatable process for the internal team. Payment will follow the agreed delivery milestone; the important outcome is a dependable evaluation loop that supports future scaling.