HR policy checks 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 policy checks slice of the HR screening operations pack. Intent: apply the Jev (TypeSafe System One) decision model to HR screening operations policy checks. Primary search language: HR Jev policy checks. 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
A policy check is a typed question whose instructions + criteria are your rules about the application or HR ticket text. Jev scores compliance; the screening worker enforces. This is not a certification, and it is not a photocopy of a rival “policy engine” page — we keep rules atomic and ANDed in code.
Hub: Use cases. Compare, when the other tool is the real job: HR screening.
Policy Checks inputs
Put policy text and the artifact in structured state (never hope the model memorized last quarter’s PDF):
{
"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" }
}Name app.text, role.must.
Decision signals and actions
| Id | Rule | Enforce |
|---|---|---|
no_protected |
Lint questions/state for protected-class cues | wrapper + review |
adverse_human |
Reject / terminate paths require a human | code |
notice |
Where law requires notice or explanation, your ATS/legal stack owns it | ATS / counsel |
Typical primitives on the same request:
| 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? |
violations = [name for name, ans in policy_nouls.items() if ans.noul >= T_VIOLATION]
if violations:
return review(violations)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
Policy-in-state can be attacked (“ignore the policy”). High-risk auto-rejecting a candidate or taking employment action still needs deterministic checks. 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.
Evaluation and rollout notes
Gold labels are policy-versioned. A criteria edit without replay is how silent false-allows ship. 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 | decision workflow |
What may enter state |
input contracts |
| What to gather first | evidence collection |
| Atomic rules | you are here |
| 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
One Score for “compliant”? No. Atomic Nouls per rule, AND/OR in code. Money and dates: extract in code first (jaggedness).
If a regex can enforce it, should I still call Jev? Skip Jev. Official “how to build” guidance: keep deterministic rules in code when you can.
Where is the rest of the HR pack? Start with HR decision workflow and HR human handoff. 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
- Not an ATS, not employment-law advice, not a fairness certificate.
- No quality-of-hire numbers.
- Not official TypeSafe.
- Official TypeSafe status, or that jev.pro issues API keys.
- That a schema-constrained answer is automatically factually correct.
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.