What is an AI Interview Platform?

An AI interview co-pilot assists your hiring team by handling technical screening while you focus on culture fit. TalentLyt combines Maya AI with 13-signal fraud detection, Job Success Prediction, and the Interview Genome—keeping you in control of every hiring decision. Beta: 6,800+ anomalies detected across 82 interviews.

How does TalentLyt ensure integrity in AI interviews?

TalentLyt uses a Multi-Agent AI system featuring Maya (Conversational Lead) and a Forensic Engine to detect anomalies, proxy actors, and LLM assistance in real-time. Our Vision-Sync and Cognitive-Audit systems provide sub-millisecond consensus verification.

Can AI interviews reduce hiring bias?

Yes, TalentLyt provides consistent AI-assisted scoring that complements your team's judgment, helping reduce evaluation variance by up to 80%. Your hiring team stays in control while getting data-driven insights.

How is TalentLyt different from HireVue or Codility?

TalentLyt is an AI interview platform with Multi-Agent consensus verification. While other platforms focus on assessment, TalentLyt combines adaptive AI interviews with forensic integrity verification to detect cheating, proxy actors, and AI-assisted responses in real-time.

What is interview integrity verification?

Interview integrity verification is the process of ensuring that a candidate is who they claim to be and is completing the assessment authentically. TalentLyt provides forensic-grade verification through behavioral analysis, gaze tracking, and cognitive pattern detection.

Is TalentLyt suitable for enterprise technical hiring?

Yes, TalentLyt is designed for enterprise-scale technical hiring. We offer SOC2 compliance, legally-defensible audit trails, and can handle high-volume recruitment with consistent quality and integrity verification.

How does TalentLyt detect deepfakes in interviews?

TalentLyt uses 13-signal verification including lip-sync analysis, voice biometrics, facial recognition consistency, and video feed analysis to detect deepfakes in real-time. Our Governor agent identifies visual anomalies and voice irregularities that indicate AI-generated content.

What is the difference between TalentLyt and Talently.ai?

TalentLyt and Talently.ai are different products. TalentLyt provides 13-signal fraud detection, deepfake identification, and proxy candidate detection that Talently.ai lacks. TalentLyt also offers Interview Genome (portable verified profiles) and 85% accurate job success prediction.

Can TalentLyt detect proxy candidates?

Yes. TalentLyt detects proxy candidates through continuous identity verification, voice biometrics, behavioral analysis, and cognitive pattern detection. If someone other than the verified candidate attempts to answer questions, TalentLyt flags the anomaly immediately.

Does TalentLyt detect AI-assisted cheating like ChatGPT?

Yes. TalentLyt detects AI-assisted cheating through keystroke dynamics, response timing patterns, tab switching detection, clipboard monitoring, and cognitive load indicators. The system identifies when candidates use ChatGPT, Claude, or other AI tools during interviews.

How is TalentLyt different from InterviewGuard or Talview?

TalentLyt combines AI interviewing (Maya AI) with fraud detection in one platform. InterviewGuard and Talview focus on proctoring existing interviews. TalentLyt conducts the entire interview autonomously while simultaneously verifying integrity with 13 signals.

ChatGPT Cheating in Technical Interviews: Detection & Prevention Guide 2026

The numbers are alarming.

1 in 5 employees admits to using AI during job interviews
Gartner predicts 1 in 4 candidate profiles will be fake by 2028
Leading cheating tools claim 93% pass rates in real coding interviews
Google's CEO has suggested returning to in-person interviews

This isn't a theoretical problem. In February 2025, a Columbia University student publicly demonstrated how he used AI to game Google's virtual interview platform and received multiple internship offers. His story went viral, spawning an entire ecosystem of interview cheating tools.

Even more concerning: cybersecurity firm KnowBe4 discovered they had inadvertently hired a North Korean software engineer who used AI to alter a stock photo, combined with a stolen U.S. identity, and passed through four video interviews and a background check. He was only discovered after the company detected suspicious activity from his account.

The Modern Cheating Arsenal

Understanding the tools candidates are using is the first step to detecting them. Here's what we're seeing in the wild:

Real-Time Coding Assistants

Tools like Interview Coder and Leetcode Wizard run invisibly alongside video calls, parsing coding questions via screen capture and generating solutions in real-time. They're designed specifically to be undetectable by standard proctoring software.

Detection challenge: These tools don't trigger tab-switching alerts because they run in separate windows or on different devices.

Deepfake Video Overlays

Bad actors use real-time face-swapping technology to have a proxy take interviews while appearing to be the actual candidate. The technology has improved enough that standard webcam quality makes detection extremely difficult.

Detection challenge: Modern deepfakes only break down at the pixel level, requiring specialized forensic analysis.

Voice-to-Text Answer Generators

Audio from the interviewer is transcribed in real-time, fed to ChatGPT or Claude, and the answer is displayed on a second screen or teleprompter. The candidate just reads the response.

Detection challenge: Latency has dropped to under 2 seconds, making pauses seem natural.

Async Interview Automation

For recorded video interviews, candidates have unlimited time to generate polished responses. Some services even offer complete interview completion as a paid service—a proxy records answers for multiple candidates.

Detection challenge: Pre-recorded responses can be rehearsed to perfection.

Behavioral Detection Signals

While the tools are getting better, human behavior under AI assistance still leaves detectable patterns. Here's what to look for:

Signal	Natural Behavior	AI-Assisted Behavior
Eye Contact	Looks at camera, occasionally away while thinking	Eyes track horizontally (reading), fixed gaze off-camera
Speech Patterns	Filler words, self-corrections, natural pauses	Unnaturally fluent, robotic pacing, no stumbling
Typing Speed	Consistent with thinking pauses	Burst typing (pasting), sudden speed increases
Code Approach	Iterative, makes mistakes, refactors	Perfect first draft, rarely backtracks
Response Latency	Variable based on question difficulty	Consistent 2-5 second delay (AI processing time)
Follow-up Depth	Can explain reasoning, discuss alternatives	Struggles with "why" questions about their own answer

Technical Detection Methods

Audio-Visual Sync Analysis

Real speech creates precise lip-to-audio timing. Deepfakes and video overlays introduce 150-300ms lag that's invisible to humans but measurable with proper tooling. This is one of the most reliable fraud indicators.

Keystroke Dynamics

Everyone types differently—speed, rhythm, error patterns. When a candidate suddenly shifts from typing 40 WPM with frequent corrections to pasting 200 characters instantly, that's a clear signal. Advanced systems can detect copy-paste even without clipboard access.

Cognitive Load Mapping

Complex questions should create observable cognitive load: longer pauses, micro-expressions of concentration, slower speech. If a candidate answers a hard algorithmic question with the same ease as stating their name, something is off.

Cross-Session Identity Verification

The person who aces the technical screen should be the same person in the behavioral interview. Comparing voice patterns, facial micro-expressions, and communication styles across sessions catches proxy swaps.

Prevention Strategies That Actually Work

Abandon verbatim questions

Standard Leetcode-style questions are instantly recognizable to AI. Design questions that require understanding your specific codebase, system constraints, or hypothetical scenarios. ChatGPT can't optimize code it's never seen.

Require thinking out loud

Force candidates to verbalize their thought process as they code. AI-assisted candidates struggle to explain reasoning they didn't generate. "Walk me through why you chose that approach" is devastating to cheaters.

Build in surprise follow-ups

After a candidate answers, ask them to modify their solution for a new constraint they couldn't have anticipated. Authentic engineers adapt; AI-dependent candidates scramble.

Implement continuous verification

Don't just verify identity at the start. Monitor for behavioral consistency throughout. If someone's communication style shifts dramatically between your phone screen and onsite, investigate.

Use multi-modal assessment

Combine live coding with system design discussion, code review, and behavioral questions. It's hard to cheat across all formats simultaneously. Inconsistencies between modes reveal fraud.

The Uncomfortable Truth

Here's what most detection guides won't tell you: you can't manually detect sophisticated AI cheating reliably. The tools have gotten too good. Human interviewers catch obvious cases, but the candidates using premium cheating tools often sail through.

The only sustainable solution is automated, real-time integrity analysis that examines signals humans can't perceive: sub-frame video artifacts, keystroke timing patterns, audio-visual desynchronization, and cross-session behavioral consistency.

"We thought we had a good process. Then we implemented automated integrity checks and discovered that 12% of our recent technical hires had shown significant fraud indicators during interviews. Twelve percent. That's not a rounding error—that's a systemic failure."
— VP Engineering, Series C startup

ChatGPT Cheating in Technical Interviews: Detection & Prevention Guide