Problem
What needed to change
The team needed to ship an AI assistant quickly, but lacked a retrieval architecture, eval harness, and reliability guardrails.
Approach
Architecture + execution
- Designed a retrieval layer with chunking strategy, hybrid search, and relevance scoring.
- Installed an evaluation harness with golden questions, regression suites, and failure-mode tracking.
- Integrated the workflow into CI so retrieval changes required evidence and passed checks.
Results
Outcomes that held up
- Reduced AI feature iteration cycles from weeks to days.
- Improved answer consistency through retrieval tuning and test-driven evaluation.
- Enabled safe rollout by pairing agents with engineering guardrails and review discipline.