Lead scoring input contracts 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 input contracts slice of the lead scoring pack. Intent: apply the Jev (TypeSafe System One) decision model to lead scoring input contracts. Primary search language: Lead scoring Jev input contracts. 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
An input contract is the allow-list of fields you will ever POST for lead scoring. It is a decision contract for the lead record + recent text: if a field is not named in instructions, it should not be in state. That is how you beat noisy “dump the object” integrations — the rival-intent failure mode — without cloning anyone’s IA.
Hub: Composite scoring. Compare, when the other tool is the real job: classic lead scoring.
Input Contracts inputs
Documented System One inputs: state (string, object, or array of text) and a questions map. English is the primary training language. Images, audio, and video are not accepted.
Allow for lead scoring:
{
"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 }
}Bind paths: form.message, lead.title, firmographics.industry.
Refuse at the wrapper (do not send):
- raw MAU / ARR you should bucket in code first (
company_size_bucket) - the entire Marketo activity dump
- other open opportunities’ notes
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.
Decision signals and actions
The contract exists so each primitive stays atomic:
| 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? |
If a new CRM field appears, either add a question that names it or drop it. Do not “just include it.” 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
Contracts are a guardrail: missing required text → do not call Jev (or ask a Noul “is enough information present?”). That is cheaper than a confident wrong icp_fit. 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
Version the contract (field list + criteria git SHA) next to the pinned model. Replay MQL / nurture / recycle gold from SDRs, plus disqualify gold when either changes. 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 |
you are here |
| 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 | production rollout |
FAQ
What happens if I send the whole warehouse row?
jev-1.13 loses accuracy as distractors grow (official jaggedness note). Drop raw MAU / ARR you should bucket in code first (company_size_bucket). 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.
Can I send images of the artifact? No. State is text (string, object, or array of text). Transcribe first.
Where is the rest of the Lead scoring pack? Start with Lead scoring decision workflow and Lead scoring evidence collection. 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.