On-Device Interview Integrity Detection for Hiring Teams
By round three, you've spent hours on a candidate who may not exist. CheckReality runs on the interviewer's device during live calls — flagging deepfake video feeds and gaze patterns from AI overlay tools before you make an offer.
Why current processes fail
Most hiring teams believe they can spot a deepfake — the lag, the lighting, the lip sync that's slightly off. They're wrong, and the fraud knows it. Deepfake candidates are clearing first-round screens, technical interviews, and culture fits before anyone notices. Crypto, blockchain, and remote tech roles are the hardest hit, with fraudsters offering fabricated personas to extract wallet access or company credentials post-hire. By the time the fraud surfaces, a recruiter has lost 5–10 hours across multiple rounds. And if a fraudulent hire makes it through: IBM's 2025 Cost of a Data Breach Report found malicious insider threats cost organizations an average of $4.92 million per incident — the highest breach cost of any attack vector, for the second consecutive year.
How CheckReality solves it
CheckReality runs on the interviewer's device during the live video call — no software on the candidate's side, no video uploaded, no data leaving the room. The on-device engine detects deepfake video artifacts in real time, identifies AI overlay tools (like GPT-based screen assistants) being used to feed answers, and tracks gaze direction patterns that indicate reading rather than thinking. The signal surfaces during the interview, not in a post-mortem report after an offer has gone out.
Three steps to interview integrity
Runs alongside any video call — no candidate-side install
CheckReality opens on the interviewer's machine next to Zoom, Meet, or Teams. Nothing is installed on the candidate's end. The analysis is entirely local.
Deepfake artifacts and gaze patterns detected live
The engine checks the incoming video feed for deepfake signatures, identifies AI overlay tools on the candidate's screen, and tracks gaze direction for sustained reading patterns that indicate AI-assisted responses.
Signal surfaces before the offer, not after
Anomalies appear as real-time indicators the interviewer can act on immediately — probe deeper, request a different format, or escalate to the security team before a single day of onboarding begins.