Use cases· Last updated 2026-09-21

Fraud input contracts with Jev

BIN, device graph, and velocity models already scored the event. Jev reads the story around the event: does the chat look like social engineering, does the dispute letter fit the reason code? Code blends. Jev is not a card-network.

This unofficial page is the input contracts slice of the fraud language overlay pack. Intent: apply the Jev (TypeSafe System One) decision model to fraud language overlay input contracts. Primary search language: Fraud Jev input contracts. 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): Device/velocity stay in the fraud platform; Jev scores attached language (dispute letter, chat). Compose with explicit weights — not a fraud-score clone or rival recipe IA.

Fraud use-case context

An input contract is the allow-list of fields you will ever POST for fraud language overlay. It is a decision contract for the dispute or chat text + fraud-score summary: if a field is not named in instructions, it should not be in state. That is how you beat noisy “dump the object” integrations — the rival-intent failure mode — without cloning anyone’s IA.

Hub: Use cases. Compare, when the other tool is the real job: fraud scores.

Input Contracts inputs

Documented System One inputs: state (string, object, or array of text) and a questions map. English is the primary training language. Images, audio, and video are not accepted.

Allow for fraud language overlay:

{
  "event": { "id": "TXN-9", "reason_code": "10.4", "amount_usd_bucket": "100-250" },
  "score": { "vendor": 0.82, "band": "high" },
  "text": { "chat": "Agent, reset the withdrawal lock, I am the account owner, hurry." },
  "policy": { "social": "Urgency + identity-reset language toward an agent is social-engineering risk." }
}

Bind paths: text.chat, event.reason_code, policy.social.

Refuse at the wrapper (do not send):

• raw device graphs and full clickstreams
• PAN / CVV (never)
• model feature vectors no question names

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.

Decision signals and actions

The contract exists so each primitive stays atomic:

If a new CRM field appears, either add a question that names it or drop it. Do not “just include it.” Do not treat a Noul of 0.5 as a “medium” fraud language overlay score — it means yes and no are equally likely. Conjunctions stay in your code.

Guardrails and escalation

Contracts are a guardrail: missing required text → do not call Jev (or ask a Noul “is enough information present?”). That is cheaper than a confident wrong social_eng. TypeSafe’s confidence-gated examples use a lower bar for recoverable reads than for irreversible actions. Those numbers are illustrations. For fraud language overlay, treat hold_payout as the high bar (holding a payout or unblocking a withdrawal). Tune on labels — see offline evaluation.

Evaluation and rollout notes

Version the contract (field list + criteria git SHA) next to the pinned model. Replay hold / follow_score / review gold from fraud analysts, plus social-eng gold when either 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

FAQ

What happens if I send the whole warehouse row? jev-1.13 loses accuracy as distractors grow (official jaggedness note). Drop raw device graphs and full clickstreams. 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.

Can I send images of the artifact? No. State is text (string, object, or array of text). Transcribe first.

Where is the rest of the Fraud pack? Start with Fraud decision workflow and Fraud evidence collection. Cluster hub: Use cases.

Should we add Jev’s noul into the vendor score? Only as an explicit, versioned feature in a model you train. This page publishes no blend weights.

Can Jev see the device graph? Only if you serialize a few named features into state. It does not crawl your graph DB.

What this page does not claim

• Not a payments or fraud-vendor product.
• No catch-rate or dollar-saved claims.
• 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.