Kandevo.ai
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Trust & Compliance

AI in hiring should show its receipts.

Hiring is one of the highest-stakes things software can touch. These commitments are structural — built into how the product works, not promises bolted on after.

01
A human makes every decision.
AI gathers evidence and drafts a rationale from it. It never writes a score, never ranks or compares candidates against each other, and never advances or rejects anyone — on any plan, in any mode. In the code, `scores` has exactly one writer, it stamps the human who set it, and the decision field is only reachable from a person clicking a button. On Expert and above an interviewer can switch on an advisory recommendation about a single candidate; it is described below, and it is still a person who decides.
02
Recommendations are advisory, and off until you turn them on.
Available from the Expert plan up, off by default, and switched on per interview rather than per workspace — so turning them on for one candidate does not turn them on for everyone. When on, the model writes one short note about what the evidence supports and which moments are worth re-reading. It is instructed never to give a hire or no-hire verdict, never to compare the candidate to anyone else, and never to score them, because that output is what would make this an automated decision system rather than a document a person reads. The note is labelled advisory wherever it appears, the toggle writes an audit event naming who flipped it, and the report records whether one was present when it was drafted. Depending on where you hire, automated recommendations carry disclosure and audit duties — NYC Local Law 144 and the EU AI Act are the usual two — and that is your obligation as the employer, not ours.
03
Every score links to evidence.
One tap from any number to the moment, message or piece of work that produced it. A score cannot be saved without it: it's enforced in the application, by a NOT NULL column, and by a database constraint that also rejects a blank.
04
Integrity signals are questions, never verdicts.
Paste, tab-switch and cadence detection prompt a conversation. They never label anyone a cheat — a signal is a reason to ask, not a verdict. No exercise hands the candidate an AI to answer for them — except one, Working with AI on the paid plans, where the assistant is the task and the candidate is marked on how they direct it. If you're testing judgement, a ghostwriter defeats the point.
05
Candidates are told how it works before they start.
Every candidate page we serve says an AI is involved, and each simulation names the counterpart as AI on screen. What we cannot control is what you say in your own invitations and calls — the Terms make that your obligation, and the invitation we draft for you includes it. A human reviews everything the model produces here; nothing is judged by machine alone.
Built for a regulated space

Designed around the rules, not despite them.

NYC LOCAL LAW 144

New York regulates automated employment decision tools, with bias-audit and candidate-notice requirements when tools substantially assist decisions. Whether a given use falls inside that is a question for your counsel, and a human making the final call is not on its own the answer. What we give you is the evidence to answer it: an append-only trail of who scored what, on what evidence, and who decided. We do not keep a record of the notices you give candidates — that one stays with you.

EU AI ACT

European law treats recruitment AI as high-risk, and the obligations that follow are broad — risk management, data governance, technical documentation, logging, accuracy, human oversight. We have built for the oversight and logging parts and we are not yet a conformity-assessed provider. If you are hiring into the EU, treat that as work still to do, on both sides.

We design to support your obligations — we can’t discharge them for you, and this page isn’t legal advice.
Kandevo AI runs on Anthropic’s Claude models — named here, in the Privacy Policy and the DPA, and on every candidate’s screen. No mystery AI.
AUDIT TRAIL · EVERY SESSION EXPORTS ONE
14:02Score 4/5 · Judgment — linked: "Named the client's exec exposure before the fix" (chat)
14:07AI recommendation enabled by T. Chen · advisory
14:12Decision: Advance — made by T. Chen (human)
SCORE → EVIDENCE → HUMAN DECISION, EVERY TIME
The trail is append-only at the database level: updates and deletes are rejected, not just discouraged.
Cheat-resistant by construction

Generated, not library.

Question banks leak onto forums within weeks. Our content doesn’t exist until you paste the JD — scenarios, sims and mandates are generated for your role the moment you paste the JD, and several exercises are generated fresh per interview. There’s nothing to memorise and nothing to trade, and if a scenario feels rehearsed you can regenerate it before the interview starts.

The candidate’s side of the deal
TIME
Async sessions stay short.
Respectful work samples — not an unpaid project.
DISCLOSURE
AI is always disclosed.
Candidates know when they're talking to a sim and what's being observed.
REVIEW
A human reviews everything.
Nothing they produce is judged by a machine alone — ever.
What we haven’t done yet

We are not SOC 2 certified, and we have no psychometric validation study — we can show you evidence behind every score, but not yet a correlation between those scores and who succeeded in the role. The Data Processing Agreement is published, in force on every plan including Free, and needs no signature — it is not something Enterprise buys. Bias-audit support and a security review are Enterprise commitments, scheduled against real conversations rather than shipped in advance.

We’d rather you read that here than discover it in procurement.

See the evidence trail for yourself.

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