Lead scoring audit trail 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 audit trail slice of the lead scoring pack. Intent: apply the Jev (TypeSafe System One) decision model to lead scoring audit trail. Primary search language: Lead scoring Jev audit trail. 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 audit trail for lead scoring is a decision trace: replayable inputs, typed answers, floors, and the action the revenue router took. It is not a chat log and not a clone of a SIEM product page.
Hub: Composite scoring. Compare, when the other tool is the real job: classic lead scoring.
Audit Trail inputs
Persist the filtered payload (the contract), not whatever arrived at the edge:
{
"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 }
}Redact secrets before the object hits cold storage.
Decision signals and actions
Minimum fields:
- lead.id
- atomic answers
- weight vector version
- blended score
- CRM write-back
Also store usage.input_tokens (vendor meter) and the full probabilities map — argmax-only logs cannot explain a close icp_fit.
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
If you cannot explain paging an AE or starting outreach from the trace, you are not ready to auto-act. 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
Traces are the eval warehouse. Replay against MQL / nurture / recycle gold from SDRs, plus disqualify gold after criteria or alias 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 |
input contracts |
| What to gather first | evidence collection |
| Atomic rules | policy checks |
| Act / review / abstain | confidence thresholds |
| Reviewer payload | human handoff |
| What to persist | you are here |
| How it breaks | failure modes |
| Labeled replay | evaluation |
| Shadow → canary | production rollout |
FAQ
Is the HTTP log enough? No. Persist the filtered state, full probabilities, floors, and downstream action as a decision trace.
May I log raw secrets? Redact in code. Jev will not be your DLP layer.
Where is the rest of the Lead scoring pack? Start with Lead scoring human handoff and Lead scoring evaluation. 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.