Use cases· Last updated

HR decision workflow with Jev

Inbound applications and tickets mix eligibility, policy questions, and messy free text. The ATS, payroll, and your employment counsel still own outcomes. Jev can score job-related criteria you wrote. It is not an ATS and must not become a proxy for protected-class decisions.

This unofficial page is the decision workflow slice of the HR screening operations pack. Intent: apply the Jev (TypeSafe System One) decision model to HR screening operations decision workflow. Primary search language: HR Jev decision workflow. Confirm patterns on docs.typesafe.ai. This site does not sell, issue, or proxy TypeSafe keys. Use a credential you already have from the console or a documented gateway.

Independent angle (cover ≠ clone): Job-criteria Nouls with an explicit do-not-encode-protected-class lint — not a clone of an ATS or HR-screening product page.

HR use-case context

HR screening operations is a workflow, not a chat. Assemble a narrow state, ask the primitives below, and let the screening worker branch. TypeSafe’s docs say a good question is a snap decision a knowledgeable person could make in a few seconds — not an open-ended analysis of the application or HR ticket text.

Hub: Use cases. Compare, when the other tool is the real job: HR screening.

Decision Workflow inputs

Keep only fields the questions name:

{
  "app": { "id": "c-190", "text": "5 years Python; no work auth mentioned; relocating July." },
  "role": { "must": "Python in production; authorized to work without sponsorship this cycle." },
  "ticket": { "type": "inbound_app" }
}

Point instructions at app.text, role.must. Drop photos, CVs as images, or video interviews; demographic / protected-class fields — drop in the wrapper.

Decision signals and actions

Id Type Job
screen Choice advance / hold_docs / reject_role_mismatch / other
must_have_fit Score none of the musts → all musts evidenced
work_auth_clear Noul Text clearly meets role.must work-auth clause?

All of these share state and run in parallel. Code owns the graph:

def hr_screen(ans):
    s = ans["screen"]
    if s.confidence < FLOOR or s.choice == "other":
        return "recruiter_review"
    if ans["work_auth_clear"].noul < T_AUTH:
        return "hold_docs"
    if s.choice == "reject_role_mismatch" and ans["must_have_fit"].score <= 1:
        return "recruiter_confirm"  # adverse action stays human
    return s.choice

Do not treat a Noul of 0.5 as a “medium” HR screening operations score — it means yes and no are equally likely. Conjunctions stay in your code.

Guardrails and escalation

TypeSafe’s confidence-gated examples use a lower bar for recoverable reads than for irreversible actions. Those numbers are illustrations. For HR screening operations, treat auto_reject as the high bar (auto-rejecting a candidate or taking employment action). Tune on labels — see offline evaluation.

Low confidence, screen == other, or a policy miss → human or safe default; do not auto-reject or take employment action.

Evaluation and rollout notes

Shadow: ATS stage unchanged; log Jev screen only.

Canary: Auto hold_docs only; every reject stays human.

Pin jev-1.13.0 (the versioned id) after you fit thresholds. jev-latest and the marketing line jev-1.13 can move. Log the response model. TypeSafe’s published list price for jev-1.13 is $0.042 per million input tokens (vendor claim — confirm on the models page); output tokens are free on that same page. Unused distractors still bill as input.

Official Python and JavaScript SDKs read TYPESAFE_API_KEY and retry documented 429/529. This site does not sell, issue, or proxy TypeSafe keys. Use a credential you already have from the console or a documented gateway.

Pack map

Slice Page
Graph and primitives you are here
What may enter state input contracts
What to gather first evidence collection
Atomic rules policy checks
Act / review / abstain confidence thresholds
Reviewer payload human handoff
What to persist audit trail
How it breaks failure modes
Labeled replay evaluation
Shadow → canary production rollout

FAQ

Does Jev execute the screening worker action? No. It returns typed answers. Your screening worker code calls queues, models, or humans.

Why several questions in one request? TypeSafe’s fan-out pattern: extra questions are cheap versus another HTTP call. Screen + must_have_fit + work_auth_clear in one call. Do not add a personality or “culture fit” Score — that is how proxies sneak in.

Where is the rest of the HR pack? Start with HR input contracts and HR confidence thresholds. Cluster hub: Use cases.

Is this an ATS or hiring decision system of record? No. It is an unofficial typed-screening pattern. Employment law stays with you and counsel.

Can we score “culture fit”? We recommend you do not. Keep criteria job-related and lint for proxies.

What this page does not claim

Disclaimer

This is an independent unofficial site and is not affiliated with TypeSafe AI; official documentation is available at https://docs.typesafe.ai.

Primary documentation: https://docs.typesafe.ai. Hub: Use cases.

Sources

Public TypeSafe or adjacent documentation only. No private claims.