Human-in-the-loop AI

Human-in-the-loop AI keeps people responsible for sensitive decisions while AI handles intake, classification, drafting, and evidence gathering.

Human review by default

For sensitive AI workflows, the system should show source evidence, confidence, suggested actions, and an approval path before anything important is sent or changed.

Source citations
Confidence and fallback handling
Approval history
Logging for AI actions

Production-minded delivery

A useful AI sprint is not a prompt demo. It needs data boundaries, user roles, retries, monitoring, security assumptions, and a clear path to integration.

Data-source definition
Role-based access assumptions
API and model-provider assumptions
Deployment notes