Originality AI

Originality AI reports the probability that text was generated by major models including ChatGPT, Gemini, Claude, and Grok, and bundles plagiarism scanning, a fact checking aid, and readability scoring. The audit grade output is built for publishers and agencies that need documented checks across hundreds of articles.

Pricing starts at about 12.95 dollars per month billed yearly for the base AI detection plan, with pay as you go credits for scan volume and an enterprise tier near 136.58 dollars per month. Third party reviews cite slightly different ladder numbers, so confirm current plans on the official pricing page before subscribing.

The company publishes accuracy studies and an AI Allowance feature tuned to flag fully generated text while tolerating lightly assisted drafts, which reduces false positives on human edited work. That said, every detector is probabilistic: heavily edited AI text and unusual human writing can both be misjudged, so treat scores as signals with review, not verdicts.

There is no meaningful free tier for casual checks; the product is priced for teams that run scans at volume. Free detectors exist for one off checks, but they lack scan depth, team dashboards, scan history, and the plagiarism and fact checking bundle that makes Originality an editorial workflow rather than a toy.

Publishers enforcing AI content policies, agencies auditing freelance deliverables, and affiliate publishers protecting search rankings are the core buyers. The common thread is scale and accountability: dozens or hundreds of contributors where manual review is impossible and documented checks are required.

Yes. Each scan can include plagiarism checking against web wide sources alongside AI detection, and the fact checking aid flags claims that need verification. Combining the three checks in one pass is the main editorial efficiency: one tool, one dashboard, and one audit trail instead of three vendors.

There is no formal appeal system, which is why teams use the AI Allowance setting and human review rather than acting on scores alone. Best practice is to pair detection with version history or process evidence, and the scan history in team dashboards provides the documentation trail for those conversations.