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.

The Real Cost of a Bad Engineering Hire (And How to Avoid It)

"We spent six months trying to make it work. In the end, we had to let him go. The project was three months behind, the team was burned out, and we still had to start the search over from zero."
— VP of Engineering, Series B startup.

I hear some version of this story every week. A company thinks they've found their unicorn engineer. Six months later, they're back at square one—except now they're $200,000 poorer and their team morale is shot.

The traditional stat you'll see thrown around is "a bad hire costs 30% of their first-year salary." That number is laughably low for engineering roles. When we actually tracked the full impact, the real cost was 2-4x the annual salary.

The Hidden Costs Nobody Talks About

Direct Costs: $75K - $150K

Recruiting fees: 20-25% of salary = $30K-$50K
Salary during tenure: $40K-$80K (6-12 months)
Severance package: $10K-$30K
Benefits, equipment, office space: $5K-$15K

Onboarding & Training: $25K - $60K

Senior engineers typically spend 10-15 hours per week mentoring new hires during the first 3 months. That's 120-180 hours of senior engineer time worth $100-$150/hour. Add in the formal training, documentation review, and codebase walkthroughs, and you're looking at a significant investment that walks out the door.

Productivity Loss: $50K - $200K

This is where it really hurts. A bad hire doesn't just fail to contribute—they actively slow down everyone around them:

Code review burden: Other engineers spend 2-3x longer reviewing their PRs
Bug fixing: Technical debt that takes months to unwind
Project delays: Features slip, launches get pushed, revenue is delayed
Team context switching: Constant interruptions to help or fix issues

Morale & Attrition Risk: $0 - $300K+

This is the wildcard that can make a bad hire catastrophically expensive. Good engineers have options. When they see leadership make questionable hiring decisions and then ignore the problem for months, they start interviewing elsewhere. Losing even one strong performer because of a bad hire can double or triple your total cost.

The Total Damage

$150K

Best Case

$280K

Typical Case

$500K+

Worst Case

Based on senior engineer salary of $180K. Costs scale proportionally for different levels.

Why Traditional Interviews Fail

The average technical interview process is basically a coin flip dressed up as a rigorous evaluation. Here's why:

1.Interviewers are inconsistent. Without standardized scoring, two interviewers can have completely different takeaways from the same candidate. One person's "shows promise" is another's "not ready."
2.Charisma beats competence. Confident candidates who interview well often beat technically stronger candidates who are more reserved. This is especially problematic for diverse hiring.
3.Fraud is easier than ever. With remote interviews, candidates can use AI assistance, have someone else take the interview, or even use deepfake technology. We've seen all of these in the wild.
4.Pressure to fill roles. When a critical position has been open for months, hiring managers lower their bar just to end the pain. This almost always backfires.

A Better Approach

After watching companies make the same mistakes over and over, we've identified what actually works:

Verify identity and integrity from day one

Before you invest hours interviewing someone, make sure they are who they claim to be. This sounds obvious, but proxy interviewing is more common than most companies realize.

Use multi-signal assessment

A single interview or coding test is a snapshot. You need multiple data points: code quality, system design thinking, communication patterns, problem-solving approach, and behavioral signals.

Standardize and calibrate

Every candidate should be evaluated on the same criteria by calibrated interviewers. This reduces bias and makes it easier to compare candidates fairly.

Trust the process, not the gut

"I just have a good feeling about this candidate" has caused more hiring disasters than any other phrase. Data beats intuition every time.