Lead scoring policy checks with Jev
A fuzzy “how hot is this lead 1–10?” prompt hides dimensions. Ask atomic Scores (ICP fit, buying intent, timing), then weight them in code you can change without a new prompt.
This unofficial page is the policy checks slice of the lead scoring pack. Intent: apply the Jev (TypeSafe System One) decision model to lead scoring policy checks. Primary search language: Lead scoring 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): Weighted Score composition in app code + eval harness + vertical compare — beats a single composite-lead-scoring recipe clone.
Lead scoring use-case context
A policy check is a typed question whose instructions + criteria are your rules about the lead record + recent text. Jev scores compliance; the revenue router 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: Composite scoring. Compare, when the other tool is the real job: classic lead scoring.
Policy Checks inputs
Put policy text and the artifact in structured state (never hope the model memorized last quarter’s PDF):
{
"lead": { "id": "L-77", "title": "VP Engineering", "company_size_bucket": "201-500" },
"form": { "message": "Need SOC2 review before Q4 bake-off." },
"firmographics": { "industry": "fintech", "in_icp_list": true }
}Name form.message, lead.title, firmographics.industry.
Decision signals and actions
| Id | Rule | Enforce |
|---|---|---|
gdpr_contact |
Do not auto-email if consent flags in your CRM are missing | code |
competitor |
disqualify Noul high → recycle, no AE page | Noul |
weights_owned |
Blend lives in git, not in instructions | code |
Typical primitives on the same request:
| Id | Type | Job |
|---|---|---|
icp_fit |
Score | role + industry fit to your ICP rubric |
buying_intent |
Score | language of evaluation vs casual browse |
disqualify |
Noul | Student / competitor / obviously out of market? |
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” lead scoring 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 paging an AE or starting outreach 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 lead scoring, treat page_ae as the high bar (paging an AE or starting outreach). 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 Lead scoring pack? Start with Lead scoring decision workflow and Lead scoring human handoff. Cluster hub: Use cases.
Why not one Score for “lead quality”? It secretly mixes ICP, intent, and timing. Atomic Scores stay inspectable; weights change in code. See composite scoring.
Can Jev compute a 0–100 predictive score like our vendor? Do not treat a 2–10 rubric as a probability of close. Keep predictive math in your model; use Jev for language judgments.
What this page does not claim
- No invented lift vs Marketo/HubSpot scores.
- Not a CRM.
- 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.