The first multi-modal desktop application that detects advanced AI cheating in remote interviews using system-level access and detection methods beyond browser based tools
InterviewShield monitors screen activity, system processes, clipboard usage, eye movements, and voice patterns to provide conclusive evidence of cheating during online interviews.
Problem Statement
68% of technical hiring managers report suspecting candidates of cheating during remote interviews
Companies lose an average of $33,000 per bad hire resulting from interview fraud
Existing browser-based solutions miss sophisticated cheating methods that use hidden applications
Programs like InterviewCoder and Cluely hide from interviewers and leave no trace in browser-based monitoring
Candidates use secondary displays to access unauthorized resources
Candidates read from AI-generated scripts that sound natural but don't reflect actual knowledge
Current solutions rely solely on browser activity, missing system-level cheating
Limitations
Solution Overview
InterviewShield is a desktop application that runs with system permissions to detect even the most sophisticated cheating methods, providing conclusive evidence rather than AI guesswork.
Core Differentiators
Desktop-Level Access
Unlike browser extensions, we monitor the entire system including hidden applications
Multi-Modal Detection
Combines screen recording, process monitoring, clipboard tracking, eye movement analysis, and voice pattern recognition
Human-in-the-Loop
Reduces false positives by providing evidence for human review
Real-Time Alerts
Notifies interviewers of suspicious activity as it happens
Put Us to the Test: See Our Detection in Action
Tired of solutions that can't keep up with modern AI cheating? We invite you to witness InterviewShield's desktop-level detection capabilities firsthand. We've created a way for you to see how our system identifies and flags known cheating applications like InterviewCoder and Cluey.
How It Works
Competitive Advantage
| Feature | InterviewShield | Lightscreen AI | Shivon AI | NextByte | Serra | WeCP |
|---|---|---|---|---|---|---|
| System-level access | ||||||
| Hidden app detection | ||||||
| Multi-display monitoring | Limited | Limited | Limited | |||
| Clipboard tracking | ||||||
| Eye movement analysis | Limited | |||||
| Voice pattern analysis | ||||||
| Human verification | ||||||
| Real-time alerts | ||||||
| Evidence collection | Limited | Limited | Limited | Limited | Limited |
Key Technological Advantage
Our desktop application approach provides system-level permissions that browser-based solutions simply cannot access, allowing us to detect even the most sophisticated cheating methods that claim to be "undetectable."
Pricing
Test Drive InterviewShield - No Commitment.
Ideal for getting started with essential detection.
Additional: $4.99 each
Our most popular plan for growing teams.
Additional: $3.99 each
FAQ
InterviewShield only monitors activity during the scheduled interview time. All monitoring is disclosed to candidates before the interview begins, and they must provide explicit consent.
Yes, InterviewShield seamlessly integrates with all major video conferencing and coding assessment platforms. Beyond popular tools like Zoom, Google Meet, HackerRank, and CoderPad, it's designed to work with any interview software you use. It runs independently as a desktop application on both sides without interfering with other software.
Candidates receive a unique link and invitation code via email, which interviewers can send directly through InterviewShield's interface. Installation takes less than 2 minutes.
InterviewShield currently supports Windows 10/11 and macOS 10.15+. Linux support is coming soon.
Rather than relying on AI to make automated judgments that may produce false positives, InterviewShield collects concrete evidence of potential cheating during the interview. After the interview ends, interviewers receive a detailed analysis report showing exactly where and how potential violations occurred, allowing them to review the evidence and make informed decisions about candidate behavior.
When the system detects suspicious activity, it collects evidence including screenshots, process information, and behavioral data. This evidence is presented to the interviewer for review, rather than making automated judgments.