calhire
Features

A complete, resume-free hiring platform

From proving skills to a consented hire — every step is verified, anonymous-first, fair, and auditable.

The platform

Everything hiring needs, rebuilt around proof

Eight capabilities work together to make hiring verified, anonymous-first, fair, and fast.

  • Verified skills profile

    A cheat-resistant assessment replaces the résumé with a portable, verified composite score.

  • Anonymous-first

    Candidates are evaluated on skills only; identity is revealed on mutual consent.

  • Blind ranking

    Composite-score ranking on ability — no name, photo, school, or employer in the mix.

  • Human-in-the-loop

    AI recommends and explains; a person makes — and is recorded for — every decision.

  • Zero-ghosting

    Every applicant gets a growth-framed report card. The word “rejected” never appears.

  • Integrity layer

    Identity assurance plus originality and behavior signals route risk to a human, never an auto-fail.

  • Compliance-native

    Audit-ready for the EU AI Act, NYC LL 144, EEOC, and UAE Emiratization by design.

  • Open & integrated

    REST API, webhooks, SSO, and ATS sync — with privacy boundaries that hold across integrations.

Prove it once

A verified skills profile, not a résumé

Candidates take one AI-generated, cheat-resistant assessment — a skills test plus a text interview under the integrity layer — and earn a composite score from test, interview, and role-fit. It is explainable, time-stamped, valid for 90 days, and portable across every application.

  • Default weighting Test 40 / Interview 35 / Role-fit 25, adjustable per role
  • Freshness badge and a 30-day retake cooldown
  • No “résumé match” component — ever

Verified skills score

Valid 90 days · portable

Top 8% this week
ReactTypeScriptSystem Design
Evaluate blind

Anonymous until you both decide

Every candidate is born anonymous. Employers see verified scores and skills — never a name, photo, age, school, or past employer. Identity and right-to-work details unlock only when an employer advances a candidate and the candidate consents, for that one employer, logged immutably.

  • Opaque candidate handles, never derived from PII
  • Consented, per-employer reveal at allowed stages
  • Every reveal recorded in an immutable ledger
Anonymous pipeline Verified
  • 94Candidate A7F3
  • 91Candidate C2K9
  • 88Candidate B4M1
Decide fairly

Ranked by ability, decided by a human

Verified candidates are ranked on a composite score you can shape. Below-threshold applicants are flagged for human review — never auto-rejected. AI recommends and explains; a person makes the call and is recorded against it, satisfying human-oversight requirements.

  • No code path can auto-reject a candidate
  • Custom composite-score weights per role
  • Explainable recommendations tied to the deciding human
Report cardGrowth-framed
  • Strong: React, TypeScript
  • Grow: system-design depth
  • Next: a tailored practice set
Protect identity

No PII ever reaches a model

Anonymity is a hard architectural boundary, not a setting. Personal information is stripped before any AI sees a candidate, across scoring, the text interview, and ranking. The integrity layer measures originality and behavior signals — never invasive webcam proctoring.

  • PII stripped at a hard boundary before any model
  • Text-only AI interview; video is human-only
  • Config-driven, swappable AI providers per task
Anonymization boundary
Name, photo, school Stripped
Verified skills & score To model
Platform & security

Enterprise-grade under the hood

The controls and infrastructure that make verified, anonymous-first hiring production-ready.

  • Multi-region & residency

    Pin data to the EU, India, or the UAE to meet GDPR, DPDP, and PDPL.

  • SAML SSO & scoped roles

    Single sign-on, SCIM, IP allow-listing, and RBAC + ABAC enforced server-side.

  • API, webhooks & ATS

    Automate hiring from your stack; identity syncs only after a consented reveal.

  • Audit & compliance console

    Immutable logs, bias-audit exports, and Emiratization quota reporting.

  • Fast & accessible

    Pre-rendered, Core-Web-Vitals-tuned pages with AA accessibility throughout.

  • Notifications & email

    Reliable, templated transactional email with consented marketing only.

Feature questions, answered

How the verified, anonymous-first model works in practice.

How is the verified skills score calculated?
It is a composite of a cheat-resistant skills test, a text interview, and role-fit (how verified skills match the role). The default weighting is Test 40 / Interview 35 / Role-fit 25, and employers can adjust it per role. There is no résumé-match component.
How do you keep assessments cheat-resistant without invasive proctoring?
The integrity layer combines identity assurance, unique-per-session generation, and signals like response latency, typing cadence, and originality. These produce a risk score that routes to a human reviewer rather than an automatic verdict — and we explicitly avoid biased webcam-snapshot proctoring.
Can the platform reject candidates automatically?
No. No code path can auto-reject anyone. Below-threshold candidates are flagged for human review, and a person makes — and is recorded for — every person-affecting decision.
When is a candidate’s identity revealed?
Only when an employer advances the candidate to an allowed stage and the candidate consents. Identity and right-to-work details then unlock for that one employer, recorded in an immutable ledger.
Which integrations are available?
A REST API and webhooks (Business and Enterprise), SAML SSO and SCIM (Enterprise), and ATS integration (Enterprise). Identity and right-to-work data sync to your ATS only after a consented reveal.

Why the platform works this way

The reasoning behind verified skills, blind evaluation and assessment integrity.

Hiring Playbook8 min read

What skills-based hiring actually is (and what it is not)

Skills-based hiring decides on demonstrated ability, not proxies like degrees or job titles. What it means in practice and where it fails.

Read
Fairness9 min read

Blind hiring: what the evidence supports, and what it does not

Blind hiring has one of the cleanest natural experiments in labour economics behind it, and real limits. What it fixes, and what it cannot.

Read
Integrity8 min read

Candidates are using AI in your assessments. Now what?

AI-text detectors are unreliable and disproportionately flag non-native speakers. What to do instead: assessment design, signals and human review.

Read
Hiring Playbook8 min read

Does your assessment predict anything? Validity for non-specialists

An assessment that feels rigorous can predict nothing. What validity and reliability mean, how to check yours, and the four ways hiring tests quietly break.

Read

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