Lead scoring evaluation 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 evaluation slice of the lead scoring pack. Intent: apply the Jev (TypeSafe System One) decision model to lead scoring evaluation. Primary search language: Lead scoring Jev evaluation. 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
Evaluation for lead scoring is a frozen harness, not a vibe check and not an opinion-blog “Jev review.” Labels: MQL / nurture / recycle gold from SDRs, plus disqualify gold. We publish no unofficial accuracy.
Hub: Composite scoring. Compare, when the other tool is the real job: classic lead scoring.
Evaluation inputs
Replay the same contract you ship:
{
"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 }
}Freeze questions, criteria, and jev-1.13.0. Record the response model.
Decision signals and actions
Score these, not a blog-grade star rating:
- Precision of AE pages (false pages cost quota)
- Recall of true bake-off language
- Stability of blend after a weight change (replay)
Pair auto-act errors with handoff rate. If the revenue router never acts, you have not evaluated lead scoring — you have evaluated a human queue.
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
Promote a threshold only when the harness says auto-act error ≤ SLA and reviewers still catch the residual. 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
After any criteria edit, rerun before production. Cookbook lifts you see on TypeSafe pages are vendor claims — re-measure on your lead record + recent texts. 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 | you are here |
| Shadow → canary | production rollout |
FAQ
Will jev.pro publish a leaderboard for this use case? No. Measure on your labels. Vendor cookbook figures stay labeled as vendor claims.
What must stay frozen? Questions, criteria, and the pinned model id. Aliases can move.
Where is the rest of the Lead scoring pack? Start with Lead scoring failure modes and Lead scoring production rollout. 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.