Use cases· Last updated 2026-09-21

Onboarding confidence thresholds with Jev

Checklists already track “watch the video / ship the laptop.” Jev reads “I’m stuck because…” and picks a helper queue (access, equipment, accommodation). It does not mark tasks done and it does not approve legal forms.

This unofficial page is the confidence thresholds slice of the onboarding exception routing pack. Intent: apply the Jev (TypeSafe System One) decision model to onboarding exception routing confidence thresholds. Primary search language: Onboarding Jev confidence thresholds. 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): LMS/HRIS own task state; Jev routes free-text blockers. Not a checklist-clone or HRIS IA photocopy — I-9 and legal holds never Jev-only. Treat floors as production gates and price the false-reject cost — official 0.5/0.9 sketches are illustrations.

Onboarding use-case context

Thresholds turn onboarding exception routing answers into act / review / abstain. They are product policy, not a hyperparameter TypeSafe ships. Official 0.5 / 0.9 sketches are illustrations. This slice also carries the false-reject discussion: over-gating onboarding exception routing hides calibration.

Hub: Use cases. Compare, when the other tool is the real job: onboarding checklists.

Confidence Thresholds inputs

You need (1) pinned answers on a frozen contract and (2) labels for blocker-class gold from coordinators, plus accommodation gold (always human). State shape:

{
  "note": { "id": "ONB-55", "text": "I finished payroll forms at my last employer. Also I cannot sign in to the IdP on a screen reader." },
  "checklist": { "idp": "blocked", "laptop": "shipped" },
  "policy": { "access": "IdP or assistive-tech blockers go to IT-access, not LMS nudges." }
}

Decision signals and actions

FLOORS = {
    "lms_nudge": 0.52,      # illustrations — replace
    "provision_access": 0.90,
}
NOUL_TAU = 0.65  # for needs_human

def allow(ans, action):
    return ans.confidence >= FLOORS[action]

Do not treat a Noul of 0.5 as a “medium” onboarding exception routing 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 onboarding exception routing, treat provision_access as the high bar (provisioning access or closing a legal form). Tune on labels — see offline evaluation.

Band around 0.5 on needs_human always reviews. Do not copy 0.65 onto Choice confidence.

Evaluation and rollout notes

• Missed accommodation language
• False IT-access pages
• Coordinator override rate

Fit loop: pin jev-1.13.0 → replay → plot error vs confidence → pick floors where auto-act error ≤ your SLA. 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

FAQ

Should provision_access use 0.9 everywhere? No. Over-gating hides calibration and dumps the queue on humans. Fit per action.

Can I reuse a Noul τ as Choice confidence? No. Jaggedness: they are not interchangeable. See confidence.

Where is the rest of the Onboarding pack? Start with Onboarding decision workflow and Onboarding human handoff. Cluster hub: Use cases.

Employee or customer onboarding? Same pattern: checklist owns tasks; Jev routes leftover language. Keep legal holds out of auto-act.

Can Jev fill the I-9? No. Never Jev-only on statutory forms.

What this page does not claim

• Not an HRIS, LMS, or immigration product.
• No time-to-productivity claims.
• 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.