Use cases· Last updated

Churn audit trail 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 audit trail slice of the churn risk decisions pack. Intent: apply the Jev (TypeSafe System One) decision model to churn risk decisions audit trail. Primary search language: Churn 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): 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

An audit trail for churn risk decisions is a decision trace: replayable inputs, typed answers, floors, and the action the success router took. It is not a chat log and not a clone of a SIEM product page.

Hub: Use cases. Compare, when the other tool is the real job: churn models.

Audit Trail inputs

Persist the filtered payload (the contract), not whatever arrived at the edge:

{
  "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." }
}

Redact secrets before the object hits cold storage.

Decision signals and actions

Minimum fields:

Also store usage.input_tokens (vendor meter) and the full probabilities map — argmax-only logs cannot explain a close cancel_intent.

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

If you cannot explain exec outreach or a commercial concession 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 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

Traces are the eval warehouse. Replay against cancel-intent gold and whether the chosen save play was appropriate 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 Churn pack? Start with Churn human handoff and Churn evaluation. 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

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.