AI Detection API Implementation Guide

Start with the review decision

An API implementation should begin with the decision workflow, not the endpoint. Define what happens when a document is low risk, high risk, or inconclusive before storing any result.

Use AI detection API resources, developer API guidance, and enterprise AI detection workflows to plan the integration.

Implementation pattern

Assign a document ID. Send text for detection. Store the risk band and confidence with a timestamp. Route high-risk or low-confidence documents to a reviewer queue. Add reviewer notes, decision status, and retention rules. Avoid exposing raw text to users who do not need it.

Operational checklist

Test invalid inputs, long documents, retries, and duplicate submissions. Monitor latency and failure rate. Keep policy labels separate from detector scores so teams can update governance without changing the model integration.

FAQ

What should developers log?

Log the document identifier, request time, risk band, confidence, reviewer status, and policy outcome. Avoid logging more raw text than the workflow truly needs.

How should failures be handled?

Use retries for transient failures, preserve idempotency for duplicate submissions, and route unresolved cases to manual review instead of silently approving them.