Control, safety and observability
Make AI automation measurable, governed, and safe to operate
Perfectory AI adds controls, audit trails, observability, evaluation, and human approval to AI automation so teams can trust production workflows.
We add the controls production AI needs: permissions, audit trails, review queues, evaluations, monitoring, alerts, and rollback paths.
The question is not only whether AI can complete a task. The production question is whether the business can observe it, trust it, correct it, and prove it improved the workflow.
Problems this solves
- Teams cannot see why an AI workflow made a recommendation or failed.
- Approvers do not know which outputs are safe, risky, stale, or outside policy.
- AI systems ship without metrics, evaluation sets, rollback paths, or operational ownership.
What we deliver
- Human-in-the-loop review flows, permission boundaries, policy checks, and exception handling.
- Observability for prompts, model calls, tool usage, latency, cost, quality, and business outcomes.
- Evaluation sets, release checks, runbooks, alerts, and continuous improvement loops.
Direct answers
What is AI observability?
AI observability is the ability to inspect model inputs, outputs, tool calls, errors, cost, latency, quality, and business impact so teams can operate AI workflows with confidence.
Why does AI automation need human control?
Human control keeps sensitive decisions accountable. The strongest AI workflows let AI prepare, explain, and prioritize work while humans approve high-impact actions and correct exceptions.
FAQ
Can you add safety controls to an existing AI workflow?
Yes. We can review the current flow, identify missing controls, add audit logs and review gates, then define metrics and evaluation checks before the workflow scales.
Which metrics should production AI track?
Track task completion, exception rate, approval rate, correction rate, latency, cost, accuracy, user trust, and the business KPI the workflow was built to improve.