Use cases· Last updated 2026-09-21 · Outline stub

TypeSafe Jev Finance: evidence collection

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This page was generated from the jev.pro inventory for TypeSafe Jev Finance: evidence collection. Expand it against public TypeSafe docs before treating every sentence as final.

Related search language: Finance automation, Jev finance, evidence collection, decision model.

Official facts that apply

• Evaluation API documented by TypeSafe: POST https://api.typesafe.ai/v1/systemone.
• List aliases: GET https://api.typesafe.ai/v1/models.
• Question types: Choice, Score, and Noul. Mix them in one questions map.
• Public list price for jev-1.13 (confirm on the models page): $0.042 per million input tokens; output tokens are free.
• Choice and Score return confidence. Noul returns only noul (0–1).

Finance use-case context

TypeSafe Jev Finance: evidence collection sits in the System One loop: you assemble a state, ask typed questions, and let your code act on probabilities and (for Choice/Score) confidence. The reader intent here is to apply the Jev model to finance evidence collection. Keep the judgment narrow. TypeSafe’s docs say a good question is a snap decision a knowledgeable person could make in a few seconds — not an open-ended analysis.

Evidence Collection inputs

Inputs are state (string, object, or array of text) and a questions map. English is the primary training language. Images, audio, and video are not accepted. Filter unrelated fields before you send them: jev-1.13 loses accuracy as distractors grow, and unused tokens still bill as input. Name fields in instructions with backtick paths when the state is structured.

Decision signals and actions

Pick the primitive that matches the answer your code can consume: Choice for an unordered set (max 255 options), Score for an ordered rubric (2–10 levels), Noul for P(yes). Finance Jev evidence collection is a product decision you encode in instructions and criteria, then threshold in code. Do not treat a Noul of 0.5 as a “medium” score — it means yes and no are equally likely.

Guardrails and escalation

Use confidence (Choice/Score) or distance from 0.5 (Noul) as a second axis. TypeSafe’s confidence-gated routing examples use higher bars for irreversible actions than for read-only ones. Those numbers are illustrations — tune on your labels. Low confidence or a policy miss should go to a human or a safe default, not a guessed action.

Evaluation and rollout notes

Pin jev-1.13.0 (or the current versioned ID) if you tune thresholds; jev-latest can move. Log the response model, usage.input_tokens, and the full probabilities map. Official Python and JavaScript SDKs read TYPESAFE_API_KEY and retry rate limits. This site does not issue keys.

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.