Reliable AI delivery
AI quality evaluation and release gates
Turn business tasks, edge cases, and policy requirements into repeatable evaluations, traces, thresholds, and release decisions.
Operating problem
A polished demo does not reveal retrieval misses, incorrect tool arguments, unsafe escalation, or regressions after a model change. Teams need task-level evidence.
Useful first outcomes
- Representative evaluation sets
- Stage-specific quality metrics
- Traceable failure taxonomy
- Evidence-based release decisions
Delivery pattern
- 1Define the task contract and failure costs
- 2Create reviewed examples and a protected holdout
- 3Score retrieval, generation, tools, safety, and operations
- 4Gate releases and convert incidents into regression tests
Controls built into scope
- Human-reviewed expected outcomes
- Separate quality, safety, cost, and latency
- Versioned prompts, models, tools, and data
- No single aggregate score as the release decision
Services that support this use case
AI Evaluation & Observability
Measure groundedness, task completion, tool use, safety, latency, and cost before release, then trace and monitor quality in production.
Explore serviceManaged AI & Automation Operations
Operate AI agents and automations after launch with monitoring, incident response, quality reviews, cost controls, change management, and continuous improvement.
Explore service