AI Era Technical Hiring Platform
Assessment integrity candidates respect
CoderPad pairs AI-aware, project-based assessments with layered detection and fair monitoring—so you can trust the signal without treating candidates like suspects.
Trusted by 4,000+ customers worldwide
Why CoderPad?
Key Outcomes for Fraud & Cheating Detection
Harder to Game, Better Signal
Open-ended, multi-file projects are intentionally difficult to one-shot with AI and expose the reasoning you actually hire for.
Real-Time Detection at Scale
Catch suspicious behavior across high-volume campaigns with automated alerts (IDE exit, external paste), code playback, and workflows to flag or auto-reject.
Respectful, Transparent Monitoring
Balance integrity with experience: evaluate real-world skills (including how candidates use AI) rather than relying on heavy-handed surveillance.
Fewer False Positives/Negatives
Real projects add depth and complexity that reveal true capability—reducing “perfect test, poor onsite” outcomes.
Why does this matter now?
Traditional MCQ/Leetcode tasks are easily handled by AI; single-file, single-answer challenges are widely compromised. Integrity has to be designed into both content and controls.
Solve your Top Challenges in Fraud & Cheating Detection
| Cheating & Integrity Challenges | CoderPad Solutions |
|---|---|
| Cheating & Integrity Challenges AI tools trivialize MCQ/Leetcode; content leaks | CoderPad Solutions Multi-file, job-relevant projects that require human reasoning and explanation. |
| Cheating & Integrity Challenges “Silent paste” / help from others is hard to spot | CoderPad Solutions Code similarity checks, code playback, IP tracking, and IDE exit tracking highlight anomalous behavior. |
| Cheating & Integrity Challenges Large university or early-talent drives make manual review impossible | CoderPad Solutions Robust cheat mitigation & detection across hundreds of candidates with scalable workflows. |
| Cheating & Integrity Challenges Heavy proctoring hurts candidate experience | CoderPad Solutions “Surveillance vs. Reality” approach—evaluate real skills (including AI collaboration) while applying appropriate monitoring. |
| Cheating & Integrity Challenges Need evidence, not suspicion | CoderPad Solutions Optional webcam proctoring with AI image analysis, plus audit trails via playback and pad summaries. |
The Platform
Core Features for Fraud & Cheating Detection
- Integrity by Design (Projects) Open-ended, multi-file projects that are cheat-resistant by design and assess how candidates <em>use</em> AI safely and effectively.
- Multi-Layered Detection Code similarity detection, playback to spot abnormal copy/paste, IP tracking, and IDE exit tracking for MCQ and coding tasks.
- Proctoring Options Automated webcam screenshots analyzed by AI to flag suspicious behavior when required.
- Activity Insights & Auditability Real-time suspicious-activity alerts (navigate away, external paste), searchable playback, and pad summaries for fast review.
- Secure Test Mechanics Anti-copy statements, per-question timers, and test randomization reduce answer-sharing and lookup value.
+96%of tests started are completed
4,000+customers across 165 countries
Unified screen to interview workflow
FAQs
No. We evaluate how candidates use AI (prompting, tool choice, verification) within realistic projects—skills that matter on the job.
Code similarity, code playback, IP tracking, IDE exit tracking, and optional webcam proctoring with AI image analysis.
Yes, robust cheat mitigation & detection and workflows scale to hundreds or thousands of candidates.
Our “Surveillance vs. Reality” approach balances integrity with realism; Meta’s candidate satisfaction remains high using these practices.