Hiring and background verification / NeuroVerify
For employers, staffing firms and background-check providers that hire and onboard people remotely. NeuroVerify checks the candidate selfie, interview video and voice for deepfakes and manipulation, so the person you screen is the person who starts work.
It fits the selfie or video step your hiring and screening flow already runs: one API call, no new step for the candidate, one unified risk score with signal-level evidence.
NeuroVerify does not run criminal, employment or education checks and does not analyse identity documents. It adds authenticity evidence about the face, video and audio; your hiring policy makes the decision.
The gap
Remote hiring separates the person from the process. Candidates apply online, interview on video, submit documents and take a selfie for identity verification. A background-check vendor then confirms records against the name and documents provided.
Those checks verify records. They do not confirm that the face on the interview and the face in the identity selfie belong to the real candidate. Hiring fraud now includes stand-in interviewers, synthetic personas and face or voice swaps on live calls. See synthetic employee hiring fraud and detecting deepfake job interviews.
A hire gets access: to systems, payroll, customer data and source code. If the person behind the application was not who they claimed, every later control is working for the wrong identity. Selfie liveness and face match confirm presence and similarity, not authenticity.
Application → Video interview → Identity selfie and document → Background screening → Offer and onboarding → Access granted
Each stage can be passed with synthetic or swapped media. NeuroVerify adds an authenticity read on the selfie and on interview video or voice.
Related: deepfake controls for remote hiring.
Threats
These are attack techniques, not claims about any employer or screening vendor.
A real-time swap shows a different face from the person actually answering, so a qualified-looking candidate is presented by someone else.
A cloned voice masks accent or identity during phone screens and technical interviews.
An AI-generated face and a fabricated resume are used to pass identity checks and collect an offer.
A different person takes the verification selfie, then a third person works the job.
A virtual camera feeds recorded or generated video to the selfie capture, bypassing the sensor.
After hire, an attacker impersonates the new employee to redirect pay or reset access. See payroll and employee data impersonation.
Hiring teams in regulated sectors face extra scrutiny of who has access to sensitive systems. The same applies to contractors and outsourced staff.
Candidate data is personal data. See GDPR, biometrics and authenticity analysis for the privacy context in many regions.
Where NeuroVerify fits
NeuroVerify sits at the identity selfie and the interview media, before an offer or system access. Your ATS, interview tool or screening platform sends the media it already captures. NeuroVerify runs six signals in one request and returns a unified risk score with signal-level evidence.
Your hiring policy decides next steps: continue, ask for an in-person or supervised re-check, route to a recruiter or security reviewer, or stop the process.
Pass the verification selfie or a captured frame from your ATS, interview tool or screening workflow.
Get one score with evidence from the six signals.
Continue, require a supervised re-check, route to review or stop, using thresholds you set.
Run the same check again at first login or credential issue to confirm continuity.
What you get
Catch swapped or synthetic faces before an offer or access is granted.
Background-check providers can add authenticity evidence behind the identity selfie.
Candidates take the same selfie or interview. The check runs behind it.
Signal-level detail supports a review conversation instead of a gut feeling.
Use it at application, interview, identity check and first login.
Questions
No. NeuroVerify fits the selfie or video step your journey already runs. You send the media you already capture in one API call. There is no new step for the end user.
Integration uses native mobile SDKs (Android/Kotlin, iOS/Swift, Flutter), backend SDKs (Python, TypeScript/Node.js, Go, Java) or REST. Two lines of code is the product claim; live in under 24 hours is the target.
Not as a replacement. Liveness asks whether a live person was in front of the camera. NeuroVerify asks whether the media itself is authentic, and it includes a Live Presence Verification signal alongside five others, so it checks beyond liveness. See deepfake detection vs liveness detection.
You can keep your current liveness vendor and send the same selfie or video to NeuroVerify as well. Hiring fraud often involves live swaps during interviews, which liveness at a single selfie step does not see.
No. It does not run criminal, employment or education checks and it does not analyse documents. It checks face images, video and audio, and complements your screening vendor.
NeuroVerify accepts video and audio as well as face images. You can send captured frames or clips from an interview for analysis. Real-time versus uploaded analysis involves design choices; see real-time vs uploaded media analysis.
NeuroVerify returns in under 200 ms p95 on eligible image requests under defined test conditions. The test methodology is available on request, so you can compare it with your own traffic and network path.
Image checks run inside the capture step, so the end user does not see a separate wait. Video and audio take longer because they carry more media; ask us for numbers that match your clip lengths.
NeuroVerify runs as a managed cloud API, in a private cloud or your own VPC, as a secure container, on-premises, or air-gapped. The right option depends on your data-residency and security rules.
The same six signals and the same unified risk score are available in each model. Neural Defend holds ISO/IEC 27001, ISO/IEC 27701, ISO/IEC 42001 and SOC 2 Type II; see the Trust Center for details.
NeuroVerify returns one unified risk score plus signal-level evidence, so a reviewer can see which of the six signals raised concern. It does not make the final decision. Your own policy decides whether to approve, step up, route to manual review or decline.
Because thresholds are yours, you can tune them to your own tolerance for false positives and false negatives. See deepfake detection errors.
Yes. We recommend evaluating on your own media: your real capture conditions, devices and attack samples. Start with a small test set, compare the unified score and signal evidence against your current checks, then set thresholds with your own policy team.
NeuroVerify ranked #1 of 36 systems on VendorBench-100 (detection score MCC 0.876 versus 0.646 for the next-best vendor), but a public benchmark is a starting point. Your own media is the real test. Contact us to set one up.
Evaluate NeuroVerify
Share selfies and interview samples from your hiring or screening flow. We will show the unified score and evidence so you can judge fit before you change a process.
