Who this is for
- Teams moving AI into production
- Products with costly or regulated errors
- Engineering teams changing models or prompts
Workflows
- Task-level quality evaluation
- Hallucination and retrieval testing
- Agent trace review
- Latency, cost and drift monitoring
What we need
- Representative tasks and expected outcomes
- Risk and severity definitions
- Production traces where available
- Release and escalation policies
What you receive
Versioned evaluation dataset
Automated regression suite
Quality and operations dashboard
Release gates and incident playbook
Acceptance and handover
Deliver a maintained test set, documented scoring rules and a repeatable release report. Define alert ownership and review intervals. Test data must be access-controlled and representative; passing a benchmark does not certify a system safe for every future input.
Integration and deployment
Connect versioned task fixtures, application traces and release tooling. Redact sensitive records and document components that cannot be observed.
Keep evaluation data within its permitted boundary. Separate offline release checks from production telemetry and specify retention and alert ownership.
Security and human review
Calibrate automated scorers against human judgements. Report errors by severity and give an accountable owner control over release exceptions.
Boundaries
A single aggregate score can hide critical failures
Evaluation sets need ongoing maintenance
Automated judges require calibration against human review