Evals and governance
AI Evals and Governance
Trust the workflow because it has been tested, not because the answer sounds good.
Operational AI needs more than prompt quality. It needs behavior definitions, test scenarios, risk gates, audit logs, escalation rules, and review loops. This service helps teams define what good looks like and measure whether the system actually behaves that way.
Best fit
Where this helps
- Teams using AI for recommendations, drafting, triage, retrieval, or agent workflows.
- Operators who need evidence before trusting AI in frontline or document-heavy work.
- Builders who need a practical eval plan rather than a theoretical governance memo.
How it runs
- Define the behaviors the system must pass and the failures it must avoid.
- Create representative test scenarios, including ambiguous and high-risk cases.
- Add deterministic gates for escalation, approval, and refusal where model judgment is insufficient.
- Log outcomes so failures turn into product changes instead of hidden surprises.
What you get
- Eval rubric for grounding, safety, escalation, completeness, and usefulness.
- Scenario set using synthetic or sanitized data.
- Risk-gate and human-approval design.
- Audit trail and review-loop recommendations.
Guardrails
- Do not use eval language as a guarantee.
- Failure modes stay visible and become design inputs.
- High-risk decisions remain reviewable and reversible where possible.
Proof bridges
Have a workflow that looks close to this?
Send the messy version. The first useful step is usually deciding what should be mapped, what should be tested, and what should stay human-owned.