IQualify catches biased language in your job posts before they publish, turns every candidate decision into a defensible, evidence-cited record, and keeps a tamper-evident audit trail — so you can prove your hiring is fair the moment anyone asks.
One of the most-litigated age proxies in modern hiring — the EEOC has specifically called out the phrase as discriminatory.
IQualify drops into your existing Greenhouse workflow. Two webhooks, one shared review queue, and a record of everything. Always advisory, never auto-rejecting.
The moment a req publishes, IQualify reads the JD and flags biased language at the source — each flag citing the governing law, the risky phrase, and a compliant rewrite for a human to approve.
Every applicant gets a PII-stripped gap analysis against the approved requirements — qualitative tiers with cited evidence, and separate recruiter and candidate language. No scores to game.
Every machine suggestion and human decision lands in a tamper-evident, hash-chained log — the spine of your annual NYC Local Law 144 bias audit, replayable years later.
The hard, defensible parts are built in from day one — the parts a legal review will actually interrogate.
25 bias rules across 9 protected classes, each with a legal citation, a documented false-positive guard, and a safe rewrite. Reviewed and approved by employment counsel.
The analysis store has no name, email, or phone fields — candidates are referenced only by a one-way hash. Privacy is enforced by the schema, not a policy promise.
An append-only, hash-chained event log, designed to anchor daily roots to an external timestamp service. Even dismissed flags are recorded — that's what makes it defensible.
Every analysis records its model, prompt version, and seed. If a rejection is disputed two years later, replay the exact decision bit-for-bit.
Webhook-driven, no rip-and-replace. It shows up inside the Greenhouse flow your team already uses — one shared review queue.
An annual bias-audit report generated per employer — methodology transparent enough for an outside auditor to replicate, with an honest limitations section.
Not just the obvious phrases. Age proxies, coded gender language, credential and socioeconomic gatekeeping, citizenship and culture-fit traps — across every protected class.
NYC requires annual bias audits of automated hiring tools. The EEOC is actively enforcing. Colorado's SB 205 and the EU AI Act classify hiring AI as high-risk. Employers are being told to prove their hiring is defensible — and most have no way to do it. IQualify is that proof, built on a foundation that's hard to copy:
Click through a real requisition queue, watch the engine catch bias across six roles, review a candidate pipeline, and walk a defensible rejection from draft to sent — all on synthetic data.
Open the interactive demo →