Kritika
Advanced AI-Powered Academic Writing, Proofreading, and Plagiarism Analysis Suite.
Advanced code plagiarism detection and logic-based similarity analysis.
Codequiry stands as a premier technical solution for source code plagiarism detection, utilizing a sophisticated multi-layered analysis engine that goes beyond simple text matching. By 2026, its architecture has evolved to include specific forensic markers for AI-generated code, distinguishing between human logic and LLM-synthesized patterns. The platform operates by performing three distinct types of checks: Peer-to-Peer (comparing submissions within a group), Database (checking against historical repositories), and Web-scale (crawling billions of lines of public code across GitHub, Bitbucket, and GitLab). Technically, it leverages Abstract Syntax Tree (AST) comparison and logic-based fingerprinting, allowing it to detect similarities even when variables are renamed, comments are altered, or code structure is slightly refactored. This makes it an essential tool for both global educational institutions ensuring academic honesty in Computer Science programs and enterprise-level software firms seeking to protect intellectual property or verify the originality of vendor-submitted code. Its 2026 market positioning emphasizes the 'Verified Human Logic' standard, providing granular percentage-based reports and side-by-side visual comparisons that are defensible in both academic hearings and legal IP disputes.
Analyzes the underlying structural logic of code rather than just character sequences.
Advanced AI-Powered Academic Writing, Proofreading, and Plagiarism Analysis Suite.
The Gold Standard for Scholarly Plagiarism and AI Writing Detection in High-Stakes Publishing.
A non-profit open-source detector for educational integrity and transparent AI verification.
Enterprise-Grade AI Content Forensics and Linguistic Integrity Verification
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Cross-references submissions against billions of public repositories and forums.
Uses statistical variance analysis to identify patterns typical of LLMs like GPT-4 or Claude.
Compares every file in a batch against every other file in that same batch.
Allows users to upload 'starter code' that the engine will ignore during analysis.
Native plugins for Canvas, Moodle, and Blackboard via LTI 1.3.
Visual representation of code blocks that match external sources.
Students sharing code or copying from public repositories.
Registry Updated:2/7/2026
Export evidence for academic integrity committee.
Ensuring a third-party contractor hasn't used unlicensed GPL code in a proprietary project.
Verifying that graduates' portfolios contain original work rather than shared solutions.