Human-in-the-Loop Systems
Not every AI output should reach the user automatically. We build the review queues, confidence thresholds, and escalation paths that keep humans in control.
When full automation is the wrong answer
Some AI outputs are low-risk and high-confidence — those can ship directly. Others carry regulatory weight, financial consequence, or reputational risk. For those, the right architecture is AI that drafts, recommends, or flags — with a human reviewing, approving, or overriding before the action takes effect.
Confidence-based routing
Route AI outputs based on confidence scores. High-confidence results proceed automatically. Low-confidence or ambiguous results enter a review queue. The threshold is configurable and measurable.
Review queues & approval workflows
Purpose-built interfaces for human reviewers: see what the AI produced, see why, approve or correct, and move on. Designed for throughput — not busywork.
Escalation paths
When the AI can't handle a case and the first reviewer can't either, the system needs a clear path to a senior reviewer, a subject matter expert, or a manual fallback. We design these paths explicitly.
Feedback loops
Human corrections become training signal. Override patterns reveal systematic model weaknesses. We build the data capture and analysis that turns human oversight into system improvement.
Common human-in-the-loop patterns
- AI drafts a response, human approves or edits before sending
- AI classifies and routes, human verifies edge cases that fall below confidence threshold
- AI extracts data from documents, human spot-checks a sample for accuracy
- AI flags risk or anomalies, human investigates and decides on action
- AI generates recommendations, human selects from ranked options
Need to add human oversight to an AI feature?
We can design the review workflow, build the approval interface, and implement the confidence routing that keeps humans in control without creating bottlenecks.