Lead scoring human handoff 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 human handoff slice of the lead scoring pack. Intent: apply the Jev (TypeSafe System One) decision model to lead scoring human handoff. Primary search language: Lead scoring Jev human handoff. 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
Handoff is a first-class outcome for lead scoring, not a failure of Jev. When the revenue router cannot auto-act, a human sees a packed lead record + recent text — not a chat transcript. We do not ask Jev to write the reviewer essay.
Hub: Composite scoring. Compare, when the other tool is the real job: classic lead scoring.
Human Handoff inputs
Humans should see what the model saw (filtered), not the warehouse dump you correctly refused to POST:
{
"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
| Signal | Typical reason enum (you name it) |
|---|---|
| Either Score confidence below floor | thin_text |
| Blend near the cut | borderline_mql |
| disqualify mid-band | maybe_competitor |
| Enterprise logo list match in CRM (code) + low Jev intent | named-account_review |
These are application outcomes next to HTTP 200, not invented TypeSafe status codes.
Send the reviewer:
- lead.id + form.message
- atomic scores + confidences + disqualify
- weights + blended number you computed
- destination (recycle / nurture / MQL / AE)
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
Do not page humans on a single Score unless your conjunction says so. Pair with confidence thresholds. 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
Write the gold label back into the offline set. That is how floors move. 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 | you are here |
| What to persist | audit trail |
| How it breaks | failure modes |
| Labeled replay | evaluation |
| Shadow → canary | production rollout |
FAQ
Is handoff a Jev failure? No. It is a first-class outcome. Abstention is “no auto action”; handoff is the queue you send that case to (glossary).
Should Jev draft the reviewer note? No. Send structured answers. Generation is the wrong job.
Where is the rest of the Lead scoring pack? Start with Lead scoring confidence thresholds and Lead scoring audit trail. 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.