Assistants built for open-ended conversation learn from whatever the public web happens to hold. Our shortlisting models learn from the signals inside your own pipeline — who advances, who stalls, and who accepts an offer.

That history, read alongside the structured scoring layer we maintain, is what sharpens ranking a general model would otherwise guess at.

We use customer records for one purpose: raising the precision of models we build and operate ourselves.

Applicant records sit in the regional store you pick at setup and never leave it for training. Retention windows, export, and deletion are yours to set per role or per workspace.

Every ranked list carries a plain-language note on the inputs that moved it, and each model card is published with its evaluation window, refresh date, and known limits.

Our policy team tracks hiring-technology rules across the regions we serve, and product changes ship ahead of each enforcement date rather than on it. GDPR and SOC 2 obligations are covered in the trust pack.

Demographic fields are withheld from every ranking model, and outcome spreads are measured each quarter against the stage-by-stage baseline. Anything drifting past the agreed band is pulled and retrained.

Structured scorecards, blind first-pass review, and sourcing reports broken out by channel give your team the evidence to set targets and show progress against them.