A retrieval-grounded support copilot with an evaluation harness
An illustrative scenario showing how a B2B SaaS company could ship an AI support assistant that agents trust.
Illustrative solution scenario, not a client engagement.
A meaningful share of routine tickets deflected
Fast, cited responses agents can verify
A repeatable way to measure answer quality over time
The challenge
What was breaking.
The first attempt sends raw prompts to a model with no retrieval, no evaluation and no way to tell whether a change made quality better or worse.
Support leadership will not expose anything to customers without measurable accuracy and a clear escalation path to a human.
Our approach
How we delivered it.
Retrieval pipeline
Documentation, tickets and release notes are chunked, embedded and re-ranked, with every answer citing its sources.
Evaluation harness
A graded question set runs in CI, so accuracy and hallucination rates are tracked per change rather than guessed at.
Human-in-the-loop
Low-confidence answers route to an agent, and agent corrections feed back into the evaluation set.
Cost and latency controls
Model routing and caching keep per-conversation cost predictable as volume grows.
Services applied
The team on this work.
- AI & Machine Learning
- SaaS Product Engineering
Technology
What it runs on.
- Python
- LLMs
- RAG
- pgvector
- TypeScript
- AWS
Explore more
Related work.
Tell us what needs to ship.
A 30-minute call with the engineers who would run the work — not an account manager.