A thousand applications, read in the order they arrived, and the shortlist closed at forty. Send every applicant the same take-home or AI screening interview instead. We grade each attempt on our servers with integrity signals attached, and by morning your list is ranked by who can actually do the job.
Screen on evidence, not arrival order
the problem you actually have
You post one role and hundreds — sometimes thousands — of applications land in a week. Somebody opens them top-down, five minutes each, and runs out of attention around number forty. The shortlist closes there. The strongest engineer in that pile might be number 612, and nobody will ever find out, because the queue decided before the skill did.
Attention runs out long before the pile does. Each square is an applicant.
■ read■ never opened■ your best hire
Same pile, scored in parallel, best-first by morning.
■ interview these■ scored, kept warm■ surfaced at rank 1
the pipeline
From a thousand applicants down to the signed offer — no tool-hopping, no lost context between stages, and one record per candidate you can point at afterwards.
Assessments in minutes, not sprints
The whole pile, scored in parallel
The live room, nothing to install
Evidence, not vibes
live interview room
A shared editor with live cursors, real execution and an AI co-pilot suggesting follow-ups, in the browser with nothing to install. Tab switches, pastes and timing are disclosed to the candidate and land on the replay for a person to read: the system flags, the panel decides.
Senior Backend Candidate · Java
the product
Challenge authoring over MCP, the multiplayer room, grading and rubrics. Pick one: each demo below is running on this page.
Point the workspace at your own LLMs and grading pipelines over the open JSON-RPC standard, then discover, invoke and chain those tools like any built-in one.
same candidate, two stories
Candidate.pdf
2 pages · zero verifiable claims
debounce-from-scratch · full session
24:16 · every keystroke kept
04:12 Writes failing test first — unprompted.
11:47 Catches own stale-closure bug, laughs, fixes it.
19:03 Explains trade-offs out loud. Panel nods.
Integrity PASS — hire with confidence.
Only one of them is evidence
the record
A hiring decision is easy to make and hard to defend three months later. Every attempt closes into one record your team can reopen, and hand to the person who asks why.
The candidate keeps the work. You keep the reasoning.
Records stay in the workspace under its retention policy, are written to an append-only audit log, and travel to Greenhouse, Lever or Ashby with the verdict attached.
built for trust
Hiring data is sensitive. None of these are roadmap promises — each one names a mechanism that is running today.
Candidate code never runs on the app server — it executes in a network-disabled sandbox with CPU, memory, and output limits.
Hidden tests run on our infrastructure, not in the candidate's browser. Submitted scores can't be forged client-side.
Attempt timelines and integrity signals are captured per attempt (with the candidate's knowledge) and surfaced on the scorecard.
TOTP-based 2FA with single-use backup codes protects recruiter and admin accounts.
ATS keys and integration tokens are AES-256-GCM encrypted and never returned to the browser after saving.
Workspace actions, security events, and AI tool calls are written to append-only audit logs.
pricing
Seats cover the workspace and everyone in it. Each paid seat adds 10 AI screening credits a month, and packs from $1.79 a credit top up the pool. Credits are charged only when a candidate starts.
Starts with 14 days of Growth and 10 AI credits
For trying Intervutree and for small teams that run a few screenings
Billed yearly, 20% less. Includes 10 AI credits per seat each month
For hiring teams that screen every week
For larger teams with procurement and security reviews
Every paid seat adds 10 AI screening credits a month to a shared pool, tracked live. Top up with packs when you need more, and watch spend as it happens.
your next opening, minus the pile