Adaptive AI interviews and hands-on labs — returning an auditable shortlist your engineering managers can trust.
Meet Rio
Rio runs every first-round interview — and generates each follow-up question from the candidate's last answer.
Three layers between a claim and a hire.
01 · Hands-on labs
Every candidate gets a live environment and a real brief: route traffic, wire services, survive failures. The score comes from what runs, not from what they say ran.
02 · Evidence trails
Eight dimensions are scored per interview, and every number links to the moment it was earned. Open any score and read the exact transcript line behind it.
“I'd rule out the load balancer first — its health checks pass, so the 502s originate downstream.”
03 · Adaptive depth
Each follow-up is generated from the last answer, one layer deeper every time. Rehearsed candidates run out of script; real ones keep going.
Your checkout service intermittently returns 502s behind the load balancer. Where do you start?
Target-group health checks pass, and the app logs are clean around each spike.
If both pass, the 502s start below the gateway. What in the pod lifecycle would produce them under load?
Import a CSV or sync your ATS.
A conversation that digs into what they've actually run.
Walk me through your last sev-1 — what failed first?
A regional cache fleet. We failed reads over before writes…
A live environment, scored on what gets built.
A ranked shortlist with proof behind every score.
Recio assumes every candidate has AI open in another tab — and verifies anyway.
Browser-based liveness detection runs quietly during the interview. No plugins, no lockdown client.
Claimed experience is verified against how the candidate actually performs before the shortlist is built.
An actively maintained detection posture against AI answer-overlay tools, updated as they evolve.