Copyleaks

Copyleaks is a "content authenticity and compliance" platform founded in 2015 that bundles AI-generated content recognition, plagiarism detection, code license verification, and text moderation into one multimodal detection suite. It is delivered in five forms — the web app, browser extensions, LMS integrations, and the API — addressing content trustworthiness for universities, enterprises, and publishers.

Core capabilities

• AI text detection: Recognizes text generated by ChatGPT, Gemini, Claude, DeepSeek and others, covering 30+ languages, with sentence-level highlighting and confidence scores
• Plagiarism and paraphrase detection: Plagiarism checking across 100+ languages, including paraphrase, cross-language translated plagiarism, and text matching from image OCR, drawing on 60,000,000,000,000 web pages and open-access journal libraries
• Codeleaks code detection: Token-level identification of AI-generated code, code plagiarism, and open-source license risks
• Multimodal detection: Covers AI images and AI video/deepfakes, forming unified image/video/audio/text detection
• Text moderation and AI Logic: Contextual AI moderation of 10+ harmful categories, and it explains "why this was judged as AI"
• Integration in every form: Browser/Google Docs/Word extensions, 7 LMS platforms, and open-source SDKs and API in 6 languages

Use cases Best suited to universities and educational institutions (academic integrity + LMS integration), mid-size and large enterprises (GenAI governance, IP/copyright and code license compliance), publishers and media (verifying manuscript authenticity), UGC platforms (trust and safety), and developers who need to embed detection capabilities through an API.

Unique strengths Where Turnitin serves institutions only and GPTZero focuses on free individual use, Copyleaks builds an institution-grade compliance moat with a full-stack suite of AI detection + plagiarism + code + image/video + moderation, 100+ languages, 7 LMS integrations, plus SOC 2/3, GDPR, and private cloud/on-premises deployment, all built on its own models.

Editorial review It is one of the oldest and most full-stack players in the content detection field, with no obvious weak spots for institution-grade end-to-end deployment. One caveat: the official 99% accuracy figure comes from internal English testing, independent evaluations are generally lower, and performance drops sharply on humanized text; false positives, credit-based billing, and refund reputation are the main deductions. Treat detection results as a "signal" rather than a "verdict".