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Use cases· Last updated

Lead scoring production rollout 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 production rollout slice of the lead scoring pack. Intent: apply the Jev (TypeSafe System One) decision model to lead scoring production rollout. Primary search language: Lead scoring Jev production rollout. 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

Production rollout for lead scoring is Operate-pillar work: pin, shadow, canary, abort. It is not a launch-checklist clone of a rival “build with System One” guide — we only talk about this pack’s blast radius (paging an AE or starting outreach).

Hub: Composite scoring. Compare, when the other tool is the real job: classic lead scoring.

Production Rollout inputs

Ship the contracted payload, the pinned id, and a documented safe default when the API is unavailable:

{
  "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 }
}

Decision signals and actions

Stage What changes Lead scoring rule
Shadow Nothing customer-visible Write Jev blend to a shadow CRM field.
Canary One low-blast slice auto-acts Auto-MQL one segment (e.g. inbound demo form).
Abort Auto-act off ICP rewrite → freeze AE pages.

Keep the workflow code you already designed:

def composite(ans, weights=(0.45, 0.55)):
    if ans["disqualify"].noul >= T_DQ:
        return "recycle"
    fit = ans["icp_fit"].score
    intent = ans["buying_intent"].score
    blended = weights[0] * fit + weights[1] * intent  # your arithmetic
    if ans["icp_fit"].confidence < FLOOR or ans["buying_intent"].confidence < FLOOR:
        return "sdr_review"
    return "mql" if blended >= BLEND_CUT else "nurture"

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

Safe default if System One errors or confidence is low: do not page an AE or start outreach. 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

Promotion gate = evaluation green on the pinned id + audit traces for the canary. 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 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 you are here

FAQ

Can I ship on jev-latest? 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. Shadow first.

What is the safe default if System One is down? Fail closed on paging an AE or starting outreach. Do not guess.

Where is the rest of the Lead scoring pack? Start with Lead scoring evaluation and Lead scoring confidence thresholds. 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

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.