Evozn.ai builds the reliability layer that makes frontier AI models safe to run a real interview — voice, evaluation, writing, matching, and integrity, co-developed with RiseYou from day one and proven on real candidates, not a lab demo.
Evozn.ai and RiseYou were built together, not bought and sold to each other. Every engine on this page was shaped by a real product need inside RiseYou first, then hardened into something the rest of the platform depends on — the Voice Engine doesn't call a third-party API, it runs on infrastructure its own team helped design.
Each engine does exactly one job extremely well — together, they're the difference between a chatbot pretending to interview someone and an AI that actually can.
Ask a frontier real-time voice model to run a 20-question interview unsupervised, and it drifts — repeats a question, skips one, races through a closing script in one breath. The Voice Engine holds it to the script instead: exact question count, zero repeats across a dropped connection, a real pause held for a real answer.
Every answer is weighed against the competencies the position actually calls for — substance, structure, and relevance, the way a good hiring manager would judge it. The Evaluator shows its reasoning alongside the score, not just a number.
The same evaluator scores a five-minute screening call and a full technical exercise with real submitted code consistently — one standard, every format.
Fed a messy work history — or a spoken conversation with the Voice Engine — it produces a resume that's structured, ATS-clean, and tailored to the specific role being applied for. No generic template; the emphasis shifts with the job.
Spots the gaps, the vague job titles, the keywords a screening system will miss — then tells a candidate exactly what to fix and why, instead of a generic "improve your resume" score.
Question generation, live follow-ups, written feedback, summaries — context-aware, and fast enough that a live interview never feels like it's waiting on a model to think.
Used to read a candidate their feedback out loud, narrate a hiring report instead of a wall of text, or close an interview the way a person would — by actually saying goodbye.
The interview is one moment. These four engines run before it, during it, and long after it's over — matching the right people to the right roles, keeping the process honest, and turning a single interview into a portable track record.
Weighs skills, experience, category, and stated preferences against what a role actually requires, and holds a hard line on category fit — a candidate never sees, or wastes time on, a role they were never eligible for.
Tracks focus changes, connection drops, and network shifts through a live session and classifies the risk level in plain terms — so a hiring team can trust a result without watching every recording themselves.
Watches for strong-fit roles and submits applications automatically for candidates who opt in — capped weekly, clearly labeled as automated, and never applying somewhere the match score wouldn't clear for a human either.
Structures post-employment feedback — punctuality, work ethic, teamwork — into a portable history the next employer can actually see, instead of a reference call that never happens.
Voice interview AI is normally demoed on software engineers. We ran it deliberately on candidates it wasn't built around, to find where a resume-anchored, industry-general system actually breaks.
Voice comes in, reasoning decides what happens next, evaluation and writing run in parallel, and voice goes back out — matching, integrity, and ratings running underneath the whole way.
Want these engines running inside your own product? Tell us what you're building.