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AI can write the take-home now, so we stopped grading the artifact. Candidates work in live sandboxes on genuinely broken systems — copilots allowed — and what gets measured is the judgment around the code: what they fixed, what they distrusted, and how they verified what a model wrote for them.
From $399/month · Invoiced, not charged · Live in a working day
No webcams · No lockdown browser · No keystroke logging — ever
async function transfer(from, to, amount) { const balance = await db.balanceOf(from); if (balance < amount) throw new InsufficientFunds(); await db.debit(from, amount); await db.credit(to, amount);}The catalogue
10 calibrated tracks, 26 ready-to-dispatch challenges — every one proven against a deliberately flawed baseline and a reference solution before any candidate sees it. No guesswork, no “contact us to find out”: if it is not on this list, we do not sell it as ready.
junior → senior · calibrated
junior · calibrated
junior → senior · calibrated
junior → senior · calibrated
mid · calibrated
mid · calibrated
junior → senior · calibrated
junior → senior · calibrated
mid → senior · calibrated
senior · calibrated
In the pipeline — labelled honestly
Scale tier · engineered for you
The public library tells you someone can engineer. Only your own domain tells you they can engineer here. A Custom Calibrated Track is a genuinely broken system that looks like yours — your framework versions, your architecture’s sharp edges, the incident your seniors still talk about — rebuilt by our engineers as a timed, gradable challenge. And it goes through the same harness as every public track: a flawed baseline that predictably fails, a reference solution that scores 100%, proven on live cloud sandboxes before your first candidate sees it.
Exclusive to your workspace
Enforced by a database policy, not a promise. No other customer can see it, dispatch it, or know it exists.
You give us an hour
One scoping call with a tech lead; a sealed, calibrated track in your dashboard in about two weeks. We author, you inform.
Candidates meet your real work
The honest job preview generic quizzes cannot give — and a signal AI assistance does not wash out, because the failure is contextual.
Ruby, Rust, C++, or something stranger — the harness that calibrated the ten public tracks points wherever you hire.
The case
A first-round technical screen costs an hour of someone who could be shipping, plus the context switch on either side of it. Most of that hour confirms what a well-built exercise would have told you before anyone joined a call.
So the funnel filters progressively. Everyone gets a ten-minute fundamentals task, auto-graded, no human in the loop. Only the people who pass reach a deep dive, and only those reach your tech lead — who opens one view holding the code, the grader report and the recruiter’s notes.
Worked example
Engineering hours returned
~30/month
26 of 40 filtered before a tech lead opens anything; 14 deep dives × 75 min ≈ 32 hours returned. Substitute your own numbers — the shape is the point, not our arithmetic. At $399 a month, break-even is under three of those hours at a loaded senior rate; the other twenty-nine are the product.
The pipeline
Every track in the catalogue runs the same shape of funnel with different tasks in it. The point is not the tasks — it is that nobody expensive looks at anybody until something cheaper has already said they are worth it.
Everything starts from a position. Your recruiter opens one, adds candidates into it, reviews fit with the CV and notes attached, and dispatches the assessment from there. They never see code — that boundary is a database policy, not a hidden button.
No engineering time
Routing reads the CV and the role: stack, seniority, domain. Every underlying challenge was proven against a flawed baseline and a reference solution before any candidate saw it — the wording adapts to the person, the bar never moves.
Stack · profile · seniority
A ten-minute screen filters the field; survivors get a 45–55 minute deep dive in a genuinely broken repo. A hidden suite scores correctness; the weighted rubric scores the craft — what they fixed, and how.
Correctness + craftsmanship
After submitting, the candidate can write a short debrief — next steps, doubts, where AI helped and how they checked it. A model reads the diff and those words, and drafts questions for your tech lead, anchored to file and line. Never a score, never a recommendation.
From the real diff
The diff, the grader report, the guide and the recruiter’s notes in one view. Your lead reads, asks, and makes the call — no candidate is ever subject to a decision made solely by software.
GDPR Art. 22 · human decision
The moat
Not a code box that shells out to an evaluator. A container, a database with its own isolation boundary, and a grader that reports structured evidence rather than whatever landed on stdout — because judgment can only be measured against systems real enough to have judgment about.
Execution
Every attempt boots a genuine sandbox with a filesystem, a package manager and a network. Candidates run the suite, read the failure, and iterate the way they would on a Monday — because a challenge that cannot be run is a quiz about code, not a test of engineering.
Isolation
Every tenant’s data is separated by Postgres policies, forced on every table, with no application role able to bypass them.
alter table assessments
force row level security;
create policy tenant_scope
on assessments
using (tenant_id
= app.current_tenant_id());The Debrief
When they submit, the pressure ends and the signal starts: what would you do next, what do you trust least, where did AI help and how did you check it. Voluntary, five minutes, verbatim to your reviewer — the asynchronous version of the best moment of pair programming. Their own words become the opening questions of the interview, which is why bluffing them is pointless.
Our stance
No webcams, no lockdown browsers, no keystroke logging — surveillance selects for candidates with no alternatives, which is the opposite of the pool you are paying to reach. Copilots are permitted, disclosed, and discussed: we evaluate how an engineer verifies what a model writes, because that is the job now.
After grading
Hidden suites and a weighted rubric decide the score. A model then reads the diff the candidate wrote — and their own post-submit debrief — and drafts three or four questions for your tech lead, each anchored to a file and line. Questions — never a score, never a recommendation.
Routing
A model extracts a skill vector. Arithmetic — not the model — selects from your verified library, so the same seniority always draws comparable work.
Integrity
Paste bursts and focus loss are recorded and shown as context. They never move a score on their own, because a signal that silently fails someone is one you cannot defend in an appeal.
Three views
A hiring decision is shared by three people who have always been shown one interface built for none of them. Every Arbiter seat opens onto a view built for exactly its reader — and deliberately no fourth step: your tech lead decides, your executive is briefed, and nobody waits on a second sign-off.
Recruiters
Open a position, add candidates, dispatch, track the funnel. Source code is invisible to this seat — enforced by database policy, so calling the API directly returns exactly what the screen shows.
Foolproof by construction
Tech leads
The rubric, the diff, the candidate’s own debrief, then AI-drafted questions anchored to file and line. Human evidence always outranks generated prose — the machine asks, it never concludes.
Fifteen minutes to a confident interview
Executives
Band, score, criteria passed, time used, and the tech lead’s read — every sentence assembled from structured facts. No model writes your executive summary, so nothing in it can be hallucinated.
Informed, never bottlenecked
The difference
Algorithmic screens are not useless — they measure a real skill. They simply measure a different job than the one you are hiring for. The work you are actually buying is reading unfamiliar code, distrusting a green checkmark, and finding the failure mode nobody wrote a test for.
Every criterion in a rubric is authored as either correctness — does it observably work — or craft — how it is built: the cleanup function, the cache key that can hit, the structure a senior flags in PR review after CI is green. Two subtotals from the same graded criteria, pure arithmetic, no model opinion anywhere.
Because “the tests pass and the code is a liability” and “one test fails and the architecture is exactly right” are opposite hiring conversations — and a single score cannot tell you which one you are about to have.
Sound approach, flawed execution
craft ✓ · correctness ✗
Correct, and built to last
both axes clear
Neither axis cleared
the rubric says why
Works, but brittle
correctness ✓ · craft ✗
The obvious question
You can. It costs nothing, takes a minute, and returns a confident paragraph. Here is what you would actually be running — four failures, any one of which is enough:
Their copilot co-wrote the artifact — pasting it into a chat measures frontier model capability with a human’s name attached. Signal now lives in what AI assistance does not erase: behaviour in a live broken system, the verification story, the debrief in their own words.
Ask twice, get a 6 and an 8 — against no baseline, predicting nothing. LLM judges carry documented biases: sycophancy, verbosity preference, and self-preference — they rate model-written code more highly, which is exactly what candidates now submit. Every Arbiter score comes from a challenge proven to fail a flawed baseline and pass a reference solution.
An AI system that evaluates candidates is an AEDT under NYC Local Law 144 — bias audit, notice — and high-risk under the EU AI Act’s employment annex; a solely automated rejection violates GDPR Article 22. The chat-window version has none of that, and the candidate’s code and CV now sit in a consumer tool’s history with no data processing agreement.
A rejected candidate, an auditor, or a court asks why. Our answer is a signed grader report, a calibrated rubric, a process record, and a named human’s decision. The shortcut’s answer is a chat transcript.
And the part that surprises people
We run frontier models all through Arbiter — reading the CV to route a challenge, personalising the task to a candidate’s background, drafting the interview questions from their real diff. For judgment, never. That is not a capability we lack; it is the product. A chat window is not our competitor — it is a component, and a component does not come with a calibration harness, a disclosure architecture, or someone to hold liable.
Access control
So they cannot. Enforced by row-level policies in the database, not by hiding buttons — a recruiter who calls the API directly gets exactly what the interface gives them.
Manage billing, workspace settings and members. Full access to candidate work.
Read submitted code, grader reports and rubric detail. Can overrule a grade.
Send and revoke assessment links, and see pass or fail. No access to code.
Pricing
One flat rate a tech lead can approve alone — priced for adopting this quarter, not for a procurement cycle. The rigour is not discounted with it: every workspace still begins with a challenge calibrated against your stack, proven against a flawed baseline and a reference solution until it separates them. That hour of our time is in the price, not behind a quote.
One hiring team, calibrated challenges, invoiced.
$399/month, invoiced
or $3,990/year with two months free
Up to 25 seats
Several openings in flight, and a stack of your own.
$699/month, invoiced
or $6,990/year with two months free
Up to 60 seats
For regulated hiring and central procurement.
Talk to us
Unlimited seats
Asked for, not yet built
Listed because you will ask, and because a feature list that quietly includes them would be one you could hold us to. Tell us which of these you need and we will tell you when — or that we will not.
Tell us what you are hiring for. We will have a calibrated challenge and a working workspace in front of you inside a week.
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