Human-in-the-Loop AI
AI systems designed so a person reviews the decisions that actually need a person.
Fully autonomous AI sounds appealing until it's wrong in a way nobody catches. Every AI system we build — from DMNexa's order extraction to validation pipelines — is designed around a specific question first: which decisions should the AI make on its own, and which should it propose for a person to confirm?
What this covers
- Decision-boundary design — what the system decides versus what it flags for review
- Confidence scoring and escalation thresholds calibrated to your actual error tolerance
- Review interfaces built for the person doing the reviewing, not just an admin table
- Audit trails covering both the AI's proposal and the human's decision
Where people stay in the loop
- Decisions with financial, legal or safety consequences — the AI prepares, a person decides.
- Low-confidence cases — anything below an agreed confidence threshold goes to review.
- New situations — inputs the system has not seen before are flagged rather than guessed.
- Spot checks — a sample of confident decisions is reviewed to keep the system honest.
How it improves over time
Every review is recorded: what the AI proposed, what the person decided, and why. Those records show where the AI is reliable and where it is not, which lets thresholds be adjusted with evidence instead of instinct. The goal is not to remove people — it is to spend their time on the cases that need them.
Questions we hear
Does review slow everything down? Only for the cases that need it; routine cases flow through, and reviewers see the evidence already gathered.
Related
Design Your Review Workflow
Tell us what decision you want AI to help with and who should have the final say.