AI Detection API Review Workflows

Why API workflows need more than a score

An AI detection API is most useful when it fits an existing review process. Teams need submission intake, risk routing, reviewer notes, retention rules, and audit history. A raw score without workflow context is hard to act on consistently.

Start with the AI content detector API, then review developer-oriented details at AI detector API for developers. Teams with strict data requirements should also review secure AI detection platform.

Recommended review pattern

Send each document for detection, store the result with a document ID, and route only high-risk or low-confidence cases to human review. Add reviewer notes, decision status, and any student, author, or employee response. This turns detection into an auditable integrity workflow.

Privacy and governance

Limit stored text where possible, control access by role, and define retention windows. For schools, publishers, and enterprises, privacy controls can matter as much as model quality because review workflows often handle sensitive writing.

FAQ

What should an API response include?

At minimum, include a document identifier, score or risk band, confidence, reviewed text range, timestamp, and enough metadata to reproduce the review path. Teams may also need reviewer fields and policy status.

Should every result go to a human?

No. Route high-risk, low-confidence, or policy-sensitive cases to review. Clear low-risk cases can be logged without slowing the workflow.

How should teams manage retention?

Define what text is stored, who can access it, and when it expires. Shorter retention and role-based access usually reduce privacy risk while preserving audit needs.