Most organizations interview inconsistently. The same candidate gets a rigorous hour-long conversation with one interviewer and a casual chat with another. Gut feel masquerades as judgment. Strong communicators coast on confidence; quiet candidates with deep substance get cut early. And the problem compounds at scale — the higher the volume, the worse the signal.
Existing tooling has focused almost exclusively on knowledge-work and senior professional roles — product managers, engineers, analysts. The other 70% of the global workforce, the nurses, store associates, delivery partners, call-center agents, cooks and technicians, has been almost entirely ignored. Their interviews are shorter, less documented, and even less consistent.
We founded MasterPrep AI on a simple conviction: a well-designed, video-based AI can hold a rigorous, structured interview for any role — and do it at a standard that most human first-round screens never reach. Not a form. Not a quiz. A real conversation with adaptive follow-ups, probing questions, and rubric-based evaluation that produces a scorecard you can stand behind.
Add built-in cheating detection — 10+ behavioral, content and linguistic signals that surface AI-assisted cheating without disrupting the candidate — and you have a hiring signal that's both fairer and harder to game than what most teams do today.
We don't interview to fill time. Every question is purposeful, every answer is evaluated against a rubric, and every score is grounded in evidence from the transcript — not first impressions.
The same rubric for every candidate, every time. The interview adapts intelligently to each candidate's background and answers — but the standard it's measured against never moves. Structural bias creeps in through inconsistent standards. We remove the inconsistency.
From product leaders to shift supervisors, from nurses to warehouse leads — we believe every role deserves a structured interview, and every candidate deserves a fair shot. Frontline is not an afterthought.
Each customer's data is strictly isolated — separate credentials, separate storage, separate configuration. We don't cross-contaminate tenants. Candidate data is never used to train models without consent.
AI-assisted cheating is invisible and instant. We built 10+ signal detection into the core product — not as a bolt-on — because a hiring signal that's easy to game isn't a signal at all.
We measure success by whether our customers make better hires, faster. Features come from real talent teams wrestling with real problems — not our roadmap assumptions.
These aren't policies — they're the convictions that shaped every design decision we made.
Whether you get 5 applicants or 5,000, the evaluation standard should be identical. Inconsistency at high volume isn't an operational problem — it's a fairness problem.
Candidates who use AI tools during live interviews are invisible to traditional screens. Cheating detection isn't optional anymore — it's the price of a valid signal.
Every score should link to a rubric dimension. Every rubric dimension should link to evidence from the transcript. "It felt right" is not a hiring decision — it's a liability.
You can screen 100% of applicants with a structured, high-bar interview — and have results in hours, not weeks. The bottleneck was always human bandwidth, not the bar itself.
If you're a talent leader, operator, or investor who believes structured interviews should be universal, we'd love to talk.
