Sales policy checks with Jev
Opportunity stage and quote PDFs live in the CRM. Jev can set a picklist from unstructured notes: is this a next-step, a risk, a closed-won hint, a discount promise that violates policy? Workflows fire after the field write. Jev does not host the Opportunity.
This unofficial page is the policy checks slice of the sales note labeling pack. Intent: apply the Jev (TypeSafe System One) decision model to sales note labeling policy checks. Primary search language: Sales Jev policy checks. 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): CRM owns objects and SLAs; Jev labels messy call/email notes (next step, risk, discount-policy). Weighted scores stay in code — apply the composite-scoring win (not a Salesforce-IA or lead-score recipe clone).
Sales use-case context
A policy check is a typed question whose instructions + criteria are your rules about the call or email note + policy excerpt. Jev scores compliance; the CRM field writer enforces. This is not a certification, and it is not a photocopy of a rival “policy engine” page — we keep rules atomic and ANDed in code.
Hub: Use cases. Compare, when the other tool is the real job: CRM workflows.
Policy Checks inputs
Put policy text and the artifact in structured state (never hope the model memorized last quarter’s PDF):
{
"note": { "id": "N-88", "text": "CFO asked for 30% off if we sign this week. Legal still redlining DPA." },
"opp": { "stage": "negotiation", "list_discount_cap_pct": 15 },
"policy": { "discount": "Promises above list_discount_cap_pct need deal-desk, not AE verbal." }
}Name note.text, policy.discount, opp.stage.
Decision signals and actions
| Id | Rule | Enforce |
|---|---|---|
cap |
Promised % vs list cap | code when you extracted a number |
verbal_discount |
Language of an off-policy promise | Jev Noul |
crm_sla |
Owner / stage SLAs | CRM workflow |
Typical primitives on the same request:
| Id | Type | Job |
|---|---|---|
label |
Choice | next_step / risk / closed_hint / discount_promise / other |
next_step_clarity |
Score | How concrete is the stated next step? |
discount_violation |
Noul | Does the note promise a discount above the cap language (compare the number in code)? |
violations = [name for name, ans in policy_nouls.items() if ans.noul >= T_VIOLATION]
if violations:
return review(violations)Do not treat a Noul of 0.5 as a “medium” sales note labeling score — it means yes and no are equally likely. Conjunctions stay in your code.
Guardrails and escalation
Policy-in-state can be attacked (“ignore the policy”). High-risk paging deal-desk or sending a customer quote change still needs deterministic checks. TypeSafe’s confidence-gated examples use a lower bar for recoverable reads than for irreversible actions. Those numbers are illustrations. For sales note labeling, treat page_deal_desk as the high bar (paging deal-desk or sending a customer quote change). Tune on labels — see offline evaluation.
Evaluation and rollout notes
Gold labels are policy-versioned. A criteria edit without replay is how silent false-allows ship. 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 | you are here |
| 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
One Score for “compliant”? No. Atomic Nouls per rule, AND/OR in code. Money and dates: extract in code first (jaggedness).
If a regex can enforce it, should I still call Jev? Skip Jev. Official “how to build” guidance: keep deterministic rules in code when you can.
Where is the rest of the Sales pack? Start with Sales decision workflow and Sales human handoff. Cluster hub: Use cases.
Is this the same as lead scoring? No. Lead scoring composites ICP/intent Scores. This pack labels notes on an existing Opp. Link them; do not merge slugs.
Can Jev update Amount? Not from language alone. Extract numbers in code; CRM math stays in the CRM.
What this page does not claim
- Not a CRM or CPQ product.
- No invented pipeline lift.
- 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.