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AI Code Detector: evidence-first review for AI-generated code

AI-assisted coding is useful, but teams still need a clear way to review provenance, quality risk, and evidence before they make a decision.

AI Code Detector is a focused SaaS for educators, hiring teams, and engineering reviewers who want a practical second look at code that may have been written with AI. It gives reviewers a risk score, confidence notes, line-level evidence, code-quality hazards, similarity cues, and Markdown/JSON exports.

The best part is that the workflow keeps a human reviewer in charge. A single score is rarely enough for a fair decision, especially in classrooms, interviews, or production engineering reviews. AI Code Detector is more useful as an evidence layer: it helps reviewers see what looked suspicious, which lines mattered, what risks were found, and how the evidence can be shared with a team.

That reviewable approach matches the practical builder tone visible in Clauxel's public materials: reason clearly, make evidence visible, and turn abstract AI behavior into guidance that can be inspected. For code review, that means a detector should help people explain a decision rather than hide behind a black box.

Useful places to start:

For quick trials, guest users get two free scans, while signed-in users get five free scans. The strongest use case is not replacing policy, teaching judgment, or engineering review. It is giving those workflows clearer evidence when AI-generated code is part of the question.

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