Ensure code is human written

Detect AI Written Code and Plagiarism

Safeguard academic and professional code with advanced AI-written code detection, plagiarism scanning, and collusion analysis. Codequiry empowers you to ensure every submission is original, authentic, and human-made.

98%+ Detection Accuracy
Instant Analysis
Scan Billions of Sources
Detect Code Collusion
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AI Code Detection Dashboard
Highly-likely ChatGPT
82% AI Confidence
Very-Likely Human
16% AI Confidence
Trusted by 500+ institutions

Empowering academic integrity worldwide

99.8%

Accuracy

50+

Languages

500+

Institutions

10M+

Submissions

Core Capabilities

A Comprehensive Suite for Code Authenticity

Our platform delivers a multi-faceted analysis of code submissions, providing deep insights into originality, authorship, and potential misconduct.

Precision AI & Model Detection

Analyzes the digital "DNA" of code—evaluating complexity, structure, and style to distinguish human nuance from machine generation with unparalleled accuracy.

Comprehensive Integrity Scans

Detects traditional plagiarism against billions of web sources and GitHub repos, while also identifying sophisticated student-to-student collusion.

Seamless Workflow Integration

Integrates directly into your existing workflow with LMS plugins and provides clear, actionable reports, making integrity checks effortless.

Our Process

A Clear Path to Code Integrity

Our streamlined three-step process makes ensuring academic integrity effortless. From submission to report, get clear results in seconds.

1. Submit Code

Upload code files individually, in batches, or integrate with your existing LMS for seamless submissions.

2. Analyze & Compare

Our AI engine analyzes code for AI patterns and cross-references it against our massive global database.

3. Get Reports

Receive detailed, easy-to-understand reports with confidence scores and actionable insights in seconds.

Detection Analysis

Uncovering the Digital Fingerprints

Our engine looks beyond surface-level similarities to analyze the underlying characteristics of the code. See how we distinguish between human and AI authorship.

bubble_sort.py

def bubble_sort(arr: list[int]) -> list[int]:
    """
    Sorts a list of integers in ascending order 
    using the Bubble Sort algorithm.

    Args:
        arr (list[int]): The list of integers to sort.

    Returns:
        list[int]: The sorted list.
    """
    n = len(arr)
    for i in range(n):
        swapped = False
        for j in range(0, n - i - 1):
            if arr[j] > arr[j + 1]:
                # Swap elements
                arr[j], arr[j + 1] = arr[j + 1], arr[j]
                swapped = True
        if not swapped:
            break
    return arr
                    
Verdict: Highly Likely AI

Our model is 95% confident this code was AI-generated.

95%
Key Detection Signals
Perfect Styling & Docstrings

Follows PEP 8 style and includes detailed docstrings, typical of AI perfection.

Textbook Optimization

Includes the standard `swapped` flag optimization found in most computer science textbooks.

Verbose Variable Names

Uses self-documenting names like `arr`, `n`, and `swapped` where a human might use shortcuts.

sort.js

// bubble sort... seriously?
function bSort(a){
  for(var i=0; i < a.length; i++){
    for(var j=0; j < (a.length-i-1); j++){
      if(a[j] > a[j+1]){
        var temp = a[j]
        a[j] = a[j+1]
        a[j+1] = temp
      }
    }
  }
  return a
}
                    
Verdict: Very Likely Human

Our model is 12% confident this code was AI-generated.

12%
Key Human Indicators
Imperfect Styling

Inconsistent spacing and use of `var` shows a more natural, less machine-like style.

Cryptic Naming

`bSort`, `a`, `j`, and `temp` are common human shortcuts that AI models are trained to avoid.

Dismissive Comment

The single, informal comment reveals a human-like personality and context.

Pricing Plans

Transparent, Scalable Pricing

Find the ideal plan to uphold integrity at your institution. All plans are backed by our commitment to continuous improvement and dedicated support.

Free Trial
$0

A 3-day trial to experience the full power of our AI detection.

  • 5 Code Submissions
  • Standard AI Detection
  • Web Plagiarism Scan
  • LMS & IDE Integrations
Start Free Trial
Enterprise
Custom

For large-scale deployments requiring custom limits, security, and support.

  • Unlimited Submissions
  • Bleeding-Edge AI Models
  • Dedicated Account Manager
  • Custom Integrations & SSO
Contact Sales
HAVE QUESTIONS?

Frequently Asked Questions

Find answers to common questions about our AI code detection platform. If you don't find what you're looking for, our team is always here to help.

AI code detection is a technology that analyzes source code to determine if it was generated by an AI model like GPT-4 or GitHub Copilot. Our system uses advanced machine learning algorithms to detect patterns, structures, and signatures specific to AI-generated code, helping institutions uphold academic integrity and ensure originality.

Our platform boasts a 99.8% accuracy rate. We achieve this by using a sophisticated multi-layered approach that combines deep learning neural networks, syntax pattern analysis, and code style fingerprinting. This allows us to distinguish between human-written and AI-generated code with extremely high precision.

We support over 50 programming languages, covering the vast majority of computer science curricula. This includes popular languages like Python, Java, C++, and JavaScript, as well as web technologies (HTML, CSS), database languages (SQL), and many others. Our language support is continuously expanding.

Yes, absolutely. Beyond just AI detection, our platform is a comprehensive academic integrity solution. It performs extensive scans across billions of online sources, public code repositories like GitHub, and a private repository of past student submissions to identify instances of traditional copy-paste plagiarism.

Seamless integration is one of our core features. We offer turnkey plugins for all major LMS platforms, including Moodle, Canvas, and Blackboard. For custom or proprietary systems, our flexible and well-documented API allows for straightforward integration to fit your institution's specific workflow.

Uphold Academic Integrity with Confidence

Join over 500 educational institutions and ensure fairness and originality in every line of code. Get started with your free trial today.

Start Your Free Trial Now
Seamless Integration

Integrate with Your Existing Workflow

Our platform seamlessly connects with your favorite development tools and learning management systems, making code authenticity verification a natural part of your workflow.

IDE Extensions

Real-time code analysis directly in VS Code, IntelliJ, PyCharm, and Eclipse.

VS Code IntelliJ PyCharm

LMS Plugins

Automate submission checks and grading in Moodle, Canvas, and Blackboard.

Moodle Canvas Blackboard

Powerful API

Build custom workflows with our comprehensive REST API and webhooks.

REST API Webhooks OAuth 2.0
Our Mission

Codequiry aims to achieve an equally fair environment for fields relating to computer science by preventing the use of unoriginal and plagiarised code. The first step to preserving academic integrity and original source code starts with us.

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