AI detector for Publishers

1. 1

   Submit incoming manuscripts or articles in batch via API

2. 2

   Receive AI probability scores per submission

3. 3

   Flag scores above editorial threshold for human review

4. 4

   Use sentence-level breakdown to identify specific concerns

5. 5

   Apply publication policy (acceptance, revision, rejection)

The publishers use case, in detail.

Publishers operating at volume need AI detection integrated into editorial pipelines rather than as a manual one-by-one check. AI Checker's API tier supports up to 10,000 submissions per day with batch endpoints, sentence-level breakdown in the response payload, and configurable confidence thresholds. Publishers we work with typically set a 70% AI probability threshold for automatic flagging, with human review on anything above 50%. The API also supports webhook delivery so detection results can land directly in editorial Slack or email pipelines. For publishers concerned about archival authenticity (e.g. wire services, academic journals), our enterprise tier provides retroactive batch scoring against historical content libraries to identify when AI-assisted content first appeared in a publication's catalog.

The publishers workflow, in depth.

Publishing operations at scale face a different AI detection problem from individual editors. The volume of submissions makes manual one-by-one screening impractical, the diversity of contributor styles makes single-threshold policies brittle, and the legal exposure of publishing AI-generated work without disclosure has grown significantly in 2025-2026. AI Checker's publisher tier is built for this scale. The API supports up to 10,000 submissions per day per account with parallelizable batch endpoints, sentence-level breakdown in every response payload, and configurable confidence thresholds per content type (op-eds, news, reviews, etc.). Publishers we work with typically configure two thresholds: an automatic-flag threshold around 70% AI probability that routes content to a designated editor for review, and an inconclusive-zone between 50-70% that lands in a separate review queue with longer review windows. Below 50% content typically goes through standard editorial flow. For wire services and academic journals concerned about archival integrity, AI Checker's enterprise tier offers retroactive batch scoring against historical content libraries — useful for identifying when AI-assisted content first appeared in a publication's catalog and for documenting institutional response to AI policy changes. The reporting layer surfaces contributor-level trends (writers whose work has drifted higher-AI over time), content-type trends (which sections are seeing more AI-assisted submissions), and topic trends (which beats are most AI-vulnerable). For publishers that want to shape rather than detect AI use, AI Checker provides aggregated-but-anonymized industry benchmarks so editorial leadership can see how their AI exposure compares to peer publications. Privacy commitment: submission text and contributor identity remain inside your account; we do not aggregate submission content across publishers.

Built for publishers who need actionable detection.

• Sentence-level breakdown. Every result includes per-sentence scoring with the most likely source model identified — so the output is actionable evidence, not just a single percentage.
• Free tier with no daily traps. Up to 10,000 characters per check, unlimited checks per day, no signup required. Paid tiers exist for volume and team features, not as a tax on the free experience.
• API access on every plan. Integrate detection into your existing workflow with a clean REST API. Documentation includes example clients in TypeScript, Python, Go, and Rust.
• Privacy-first by default. Submissions are processed in memory and not used to train models. No third-party advertising trackers. Read our security & privacy policy for the long version.
• Multi-model coverage. We detect ChatGPT, Claude, Gemini, Llama, Mistral, Microsoft Copilot, and emerging open-source variants — not just GPT.

Detection by model, comparison, and language.