Risk score
Scores code from 0 to 100, labels Low, Review Recommended, or High Risk, and explains confidence based on snippet length, signal density, and how many independent clues appear together.
Developer-first code review
AI Code Detector for code reviews that matter.
Spot AI-written risk, quality hazards, and similarity cues before grading, hiring, or shipping. Guests get 2 free scans; signed-in reviewers get 5.
review --deep submission.js
Risk signals ready
Authorship, quality, similarity
Open the console free. Upgrade when the free scans are used.
AI Code Detector / Deep Scan
StaticNo runtime execution
ExportMarkdown + JSON
Paid gatePolar checkout
function calculateFactorial(n) {
// Step 1: check the base case
if (n === 0) return 1;
// Step 2: recursively calculate result
return n * calculateFactorial(n - 1);
}
try {
processData(resultData);
} catch (error) {
console.log(error);
}Live evidence board
73
High AI-written risk
Authorship Line 2: tutorial narration in comments
Quality Line 8: generic naming and weak exception handling
Similarity Factorial teaching sample pattern
Product journey
See the full scan flow before you open the dashboard.
The console is built around one primary path: paste code or drop a file, run analysis, inspect the evidence, then export a reviewer-ready report.
- 1Paste or drop codeUse the code box or drag a file into the upload area.
- 2Run Analyze codeChoose Quick Scan or Deep Scan, then start review.
- 3Read the resultThe page moves to score, evidence, and quality risks.
- 4Export the reportKeep Markdown, JSON, and local history for follow-up.
What it is
What is an AI code detector?
An AI code detector helps reviewers inspect whether code looks machine-assisted, copied from common samples, or too risky to accept without a deeper conversation.
The AI Code Detector review console is built for schools, hiring teams, assessment vendors, and engineering groups that need a faster way to handle suspicious submissions. The tool reads pasted code or uploaded files, scores the chance that a snippet contains AI-written patterns, and separates the result into signals a reviewer can inspect. It does not run the code, does not decide guilt, and does not replace human judgment.
The main job is to make high-stakes review less noisy. A teacher can use the scanner to decide which assignment needs a short explanation interview. A hiring manager can use the report to route a take-home exercise into a deeper technical discussion. An engineering lead can use the console to catch generated-code risks before a pull request turns into production maintenance debt.
Best used when the decision matters
The strongest use cases are grading disputes, hiring screens, vendor code review, code-quality audits, and internal review queues where reviewers need documented reasons to ask better follow-up questions.
Review workflow
How the scanner checks a submission
The AI Code Detector review console combines authorship-risk clues with practical code-quality checks so the output reads like a review brief, not a black-box accusation.
Line-level review signals
Highlights tutorial narration, generic naming, placeholder imports, unfinished branches, repeated structure, broad exception handling, and comments that sound like generated instructions.
Generated-code failure modes
Flags hard-coded secret risk, unsafe dynamic execution, empty implementations, debug residue, fabricated dependencies, shallow error handling, and unbounded loops before code reaches review.
1Paste code or upload a file into the paid console.
2Review the score, confidence, language, and detected signal groups.
3Inspect highlighted lines and quality risks before deciding the next human step.
4Export a Markdown or JSON report for a case note, interview packet, or review queue.
Use cases
Review workflows for schools, hiring, and engineering teams
Each workflow keeps the same boundary: use the report to open a better review, not to make an automatic final decision.
For education teams
Use the report as a review trigger, then ask students to explain drafts, decisions, tests, and edge cases. The useful output is not just a number; it is a set of concrete lines and patterns that make the follow-up fairer and more specific.
For hiring teams
Route suspicious take-home assignments into a deeper interview instead of losing signal to generic code, copied tutorial patterns, and polished answers that do not match the candidate's live reasoning.
For engineering teams
Catch generated-code risks that matter in production: fabricated imports, broad catches, placeholder branches, missing tests, vague ownership, and code that looks complete while hiding fragile assumptions.
Report output
What the report includes in every review
The AI Code Detector review console turns a messy code sample into a structured report that can be read by teachers, recruiters, reviewers, and technical leads.
Score and confidence
The report shows the overall risk band, numerical score, confidence note, language, and the strongest factors behind the result so reviewers can separate strong evidence from weak signals.
Signal groups
Signals are grouped into authorship patterns, source-similarity cues, security hazards, maintainability risks, and incomplete-code smells. That makes the result easier to discuss with a human.
Downloadable evidence
Teams can export Markdown for review notes or JSON for workflow systems. The report is built to support a careful follow-up, a second interview, or a documented code-quality decision.
Decision fit
When the scanner is strongest
The best results come from enough code context and a reviewer who already knows what decision the scan should support.
The scan is strongest on complete functions, small projects, take-home assignments, lab submissions, utility scripts, and pull-request excerpts where there is enough structure to compare naming, comments, branching, error handling, and dependency choices. Longer code gives the detector more independent signals, which usually makes the confidence note more useful for a reviewer.
The dashboard is also useful when a team needs consistency. Instead of every reviewer describing suspicion in a different way, the exported report gives a shared vocabulary: score, risk band, signal groups, highlighted lines, quality hazards, and recommended follow-up.
The scan is weakest on tiny snippets, boilerplate, textbook exercises, heavily edited AI output, generated tests, and code copied from common documentation. In those cases, the report should lower the decision pressure, not raise it. A low-confidence scan can still be helpful because it tells the reviewer to ask for version history, design notes, live explanation, or a second code sample.
That boundary matters for trust. The product is built to help humans ask better questions before grading, hiring, or shipping, while keeping the final judgment anchored in context the software cannot know.
Trust boundary
The score is a lead, not a verdict.
No detector can prove authorship from code alone, especially on short snippets, edited AI output, or common programming exercises. The console gives reviewers a faster, safer way to decide what deserves human follow-up.
What never happens by default
- No code execution.
- No raw-code storage.
- No training use.
- No automatic misconduct verdict.
Questions
Reviewer FAQ
Use these answers to set the right expectation before a school, hiring team, or engineering group relies on the dashboard.
Can the tool prove that code was generated by AI?
No. Code authorship is a probabilistic review problem, especially for short snippets, common assignments, edited AI output, and repeated tutorial examples. The product provides evidence for follow-up, not an automatic verdict.
What code signals does the scanner look for?
It looks for patterns such as tutorial-style comments, generic variable naming, unusually uniform structure, shallow error handling, placeholder branches, fabricated imports, unsafe dynamic execution, hard-coded secrets, and source-similarity cues.
Who should use this product?
The product is built for people who need to make careful decisions about submitted code: instructors, bootcamps, technical recruiters, assessment vendors, code-review teams, and engineering managers reviewing generated-code risk.
What happens after payment?
The dashboard opens immediately with 2 guest scans. Signing in with email raises the free limit to 5 scans, and payment keeps the console open for repeated review work.