Jev versus lead scoring: a practical comparison
Classic lead scoring adds points: title +10, employees +5, email open +1. Jev can score unstructured traces (notes, emails, call summaries) as atomic rubrics you then weight in code — TypeSafe’s composite-scoring pattern.
Unofficial. Not a Marketo/HubSpot replacement guide. Confirm primitives on docs.typesafe.ai. We do not sell keys.
Comparison scope
If the score is already a clean table of firmographics, keep the points model. If SDRs paste paragraphs, add Jev Scores — do not hide five judgments in “rate this lead 1–10.”
Independent angle (cover, do not clone)
Win delta (recipe-composite-scoring): weighted Score composition in application code, an offline eval harness, and a vertical compare — not a clone of a single “composite lead scoring” recipe URL.
Criteria that decide the architecture
| Axis | Jev (System One) | Lead scoring systems |
|---|---|---|
| Features | Language judgments you named | Structured CRM fields |
| Weights | Your coefficients | Admin UI points |
| Fit vs intent vs timing | Separate Scores | Often one blob |
| Retrain | Edit criteria + pin model | Refit the points or the ML model |
Decision quality and control
Do not interpolate a dollar amount from Score probabilities (jaggedness). Use levels as bands, combine with weights you can explain to sales ops. Official note: Score levels are weak for reconstructing a continuous magnitude.
Integration trade-offs
Ask fit, intent, timing as three Scores (2–10 levels you define). composite = w·s in code. Gate auto-MQL on composite and min confidence on each Score.
TypeSafe’s public models page lists jev-1.13 at $0.042 per million input tokens with output tokens free — a vendor claim, not a jev.pro measurement. Confirm on the models page before you bid.
When each approach fits
Prefer Jev when
- Notes/emails dominate what SDRs actually use
Prefer Lead scoring systems when
- You already have a stable points model on trusted fields
CRM points + Jev language scores + human override. Eval the action (MQL/not), not argmax alone.
Weighted composition (illustrative weights)
# Levels are yours. Weights are yours. Not TypeSafe defaults.
w = {"fit": 0.5, "intent": 0.3, "timing": 0.2}
# ans.answers[k].score is the chosen level id — map to a number in code.
numeric = {k: level_to_float[ans.answers[k].score] for k in w}
composite = sum(w[k] * numeric[k] for k in w)
auto = composite >= 0.72 and all(ans.answers[k].confidence >= 0.55 for k in w)Replay a gold JSONL with frozen questions and a pinned jev-1.13.0 — see offline evaluation.
What this page does not claim
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No lift vs Marketo published here.
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Not sales advice.
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Schema-safe output is not the same as factual correctness.
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No independent bake-off numbers live here.
FAQ
One Score instead of three? Official guidance: decompose independent factors. Change a weight without rewriting a mega-prompt.
Can Jev write the outreach email? No. Draft with an LLM after the score, if you must.
Disclaimer
This is an independent unofficial site and is not affiliated with TypeSafe AI; official documentation is available at https://docs.typesafe.ai. Never treat jev.pro as TypeSafe official documentation. We do not sell, issue, or proxy API keys.
Hub: Comparisons. Siblings: composite scoring, usecase triage confidence thresholds, offline evaluation. Canonical: https://docs.typesafe.ai.
Sources
Public TypeSafe or adjacent documentation only. No private claims.