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About us

Compliance wasn't the feature. It was the premise.

Most hiring AI was built to go fast, then asked to be fair afterwards. We think that order is backwards — and it's why we started HireBeep.

Why we exist

Hiring is the highest-stakes repeated decision most companies make, and for decades it has been made badly: inconsistently, from memory, on gut feel, with the reasoning reconstructed afterwards if anyone asks. The first wave of hiring AI didn't fix that. It automated it — same decisions, same blind spots, now made faster and at greater volume, with a model in the loop that nobody could interrogate.

Then the bill arrived. Regulators started asking how these systems reach their conclusions. Candidates started asking too, and increasingly they have a legal right to an answer. Teams that had bought a black box discovered that "the algorithm decided" is not a defence — not to a works council, not to a data protection authority, and not in court.

We built HireBeep for the world after that reckoning rather than the one before it. Not fairness as a marketing badge, and not a compliance module you buy as an add-on — a platform where the explanation, the bias check and the audit trail are produced by the same act that produces the grade, because they are not separable from it.

What we believe

An unexplainable decision isn't a decision. It's a guess with a number on it.

If a system can't say why it reached a grade in language a hiring manager can read and a candidate can contest, it hasn't assessed anyone. It has produced a score. Scores feel like rigour and aren't. Every grade HireBeep produces carries a written rationale — not because a regulation demands one, though several do, but because a grade without a reason was never worth acting on.

Fairness is a measurement problem, not a values statement.

Every company says it hires fairly. Almost none can show it. The difference between the two is instrumentation: identical criteria applied to every candidate, outcomes monitored for adverse impact, and the evidence kept. A commitment you cannot measure is a hope. How adverse impact is actually measured is here.

Compliance bolted on at the end is compliance that fails at the end.

You can add a consent checkbox to anything. You cannot add explainability to a model that was never built to produce reasons, and you cannot reconstruct an audit trail for decisions you didn't log at the time. Those properties are architectural. They are decided early, cheaply — or late, expensively, or never. We decided early.

The candidate is a party to this, not the subject of it.

Every assessment has two sides, and only one of them bought the software. That asymmetry is exactly why transparency, accommodations and the right to contest a result can't be left to whoever is paying. A process a candidate would call unfair if they saw how it worked is a process we shouldn't be shipping.

The choices that follow

Beliefs are cheap. These are the places where ours cost us something — which is the only real test of whether we hold them.

We price per outcome, not per token
Metered pricing punishes you for screening one more candidate. That's a direct incentive to assess fewer people less carefully — the opposite of what a fair process needs. One flat price means thoroughness never shows up as a line item. See pricing.
We built for the EU first
Not a US product with a translation layer and a GDPR page. Full German localization, and the AI Act's transparency, oversight and record-keeping duties designed in rather than retrofitted. What the AI Act requires.
One grade, on a scale people understand
One holistic grade on an F→A+ scale with the reasoning attached — not a dashboard of sub-scores that looks precise, resists challenge, and quietly hides which factor actually moved the result.
Every decision is logged as evidence
Timestamped, exportable, complete — written at the moment of the decision, because a trail assembled afterwards is worth very little precisely when you need it most. What we log.

See whether we actually mean it

The fastest way to test a claim about explainability is to make us explain a grade in front of you.