Eval Pipelines for AI Systems

We build evaluation pipelines that turn AI quality from a feeling into a measurable engineering practice. Our work starts by defining what good looks like for each AI feature, then building test sets that reflect both common usage and known edge cases. We add scoring methods that match the task, including exact match for structured output, LLM as judge for open ended responses, rubric based human review, and behavioral checks for agents. The pipeline runs automatically on every prompt change, model swap, and code update, and it blocks releases when scores drop below agreed thresholds. We also set up drift detection so changes in user behavior or model performance are caught over time. The result is an AI system where every change is measured, every regression is visible, and the team can ship with the same confidence they would for any other piece of production software.