Developer API
Plan AI-writing detection API integrations with run identifiers, evidence fields, review metadata, usage tracking, and responsible user-facing results.
Updated 2026-05-31
Short, citation-ready explanations for common AI detection and writing-integrity questions.
Developers should expect structured AI-writing risk data that can be routed into review workflows, including document-level summaries, passage evidence, timestamps, run identifiers, and metadata for downstream decisions.
Results should be framed as review evidence with uncertainty language, highlighted passages, and next-step guidance. High-stakes products should avoid presenting API output as an automatic misconduct verdict.
Common integrations include LMS submissions, CMS editorial queues, admissions portals, marketplace moderation, HR review tools, and compliance approval workflows.
A developer-friendly AI detector API should support interfaces that explain why a document was flagged, what passages need review, and how reviewers can record outcomes.
Run IDs, timestamps, project context, and usage records make detector results easier to debug, audit, reproduce, and connect to customer support or appeals.
Before sending text to any API, developers should define what content can be processed, how long results are retained, who can access them, and how users are informed.
Yes, depending on product design and rate limits. Real-time experiences should still show uncertainty and allow human review for sensitive outcomes.
That depends on the workflow and policy. Many teams store only the minimum data needed for audit, support, or reviewer follow-up.
No. API results should route cases, highlight evidence, and support decisions rather than replacing human judgment.