Churn failure modes with Jev
Usage curves belong to your warehouse. Jev reads why someone is unhappy (cancel language, effort, save-offer fit). Code blends the two.
This unofficial page is the failure modes slice of the churn risk decisions pack. Intent: apply the Jev (TypeSafe System One) decision model to churn risk decisions failure modes. Primary search language: Churn Jev failure modes. 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): Evidence-first risk bands from tickets and language, with arithmetic in code — not a black-box churn-model clone or rival recipe IA.
Churn use-case context
Churn risk decisions breaks in product-specific ways. This page lists those modes so you can write tests — not a generic “AI can be wrong” essay, and not a rival limitations-page clone.
Hub: Use cases. Compare, when the other tool is the real job: churn models.
Failure Modes inputs
Many failures start as contract violations (distractors, missing account language + pre-aggregated usage text). Canonical shape:
{
"account": { "id": "A-12", "plan": "pro", "seats": 40 },
"usage": { "wow_delta_bucket": "down_gt_30", "last_active_days_bucket": "21_plus" },
"tickets": { "latest": "We are moving to a competitor unless SSO ships." },
"nps": { "comment": "Setup took weeks." }
}Decision signals and actions
- Survival models on usage still win for numeric risk — Jev adds language (compare).
- Asking Jev for a 0–1 churn probability from seats and dates (jaggedness: arithmetic).
- Treating effort Score 2 as “will churn in 14 days.”
- Auto-issuing credits from a Noul.
- Invented “reduced logo churn 12%” claims.
HTTP vs application:
| You see | Class | Churn move |
|---|---|---|
| 401 / 422 / 429 / 529 | Documented HTTP | Fix key/body or back off — errors |
| 200 + flat confidence or Noul ≈ 0.5 | Low confidence | Hold; do not start exec outreach or grant a concession |
| Empty gather | Missing evidence | Skip Jev or ask “is enough information present?” |
Do not treat a Noul of 0.5 as a “medium” churn risk decisions score — it means yes and no are equally likely. Conjunctions stay in your code.
Guardrails and escalation
Fail closed: do not start exec outreach or grant a concession. Schema-safe answers are not factual correctness. TypeSafe’s confidence-gated examples use a lower bar for recoverable reads than for irreversible actions. Those numbers are illustrations. For churn risk decisions, treat exec_outreach_or_discount as the high bar (exec outreach or a commercial concession). Tune on labels — see offline evaluation.
Evaluation and rollout notes
Your canary set should include each bullet above.
- Precision of exec_save pages
- Missed competitor-threat language
- Do not attribute revenue save to Jev without a designed experiment
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 | audit trail |
| How it breaks | you are here |
| Labeled replay | evaluation |
| Shadow → canary | production rollout |
FAQ
If the API returns 200, is the decision good? 200 only means the call parsed. Low confidence, Noul ≈ 0.5, or a policy miss are application failures.
Where do official weaknesses live? TypeSafe’s jev-1.13 jaggedness note — distractors, arithmetic, adversarial content. We do not invent more.
Where is the rest of the Churn pack? Start with Churn evaluation and Churn decision workflow. Cluster hub: Use cases.
Can Jev replace our churn model? No. Keep warehouse risk; use Jev on unstructured complaints and save-offer fit.
TypeSafe mentions churn in the use-case map — is that a product? It is an example of more questions on ticket state, not a separate TypeSafe churn API.
What this page does not claim
- No causal save-rate numbers.
- Not a billing system.
- 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.