AI Operations
Keep production AI measurable after launch.
Models change, user behavior changes, integrations fail, costs drift, and edge cases accumulate. AI operations provides the evaluation, monitoring, review, and improvement process needed to keep the workflow useful.
Operational coverage
- · Evaluation regression runs
- · Failure taxonomy maintenance
- · Prompt and model change review
- · Tool-call and integration monitoring
- · Cost and latency tracking
- · Exception and escalation review
- · User feedback analysis
- · Incident response
- · Security and permission review
- · Monthly improvement planning
Monthly operating review
- · Volume and completion rate
- · Evaluation results
- · Low-confidence and exception rate
- · Human override and rejection patterns
- · Cost per completed workflow
- · Latency by stage
- · Integration failures
- · User feedback
- · Recommended changes
Start with one bounded workflow.
We will identify the inputs, decisions, actions, controls, and evidence required for a production pilot.