GUIDES

What is Jev AI?

Jev is a structured decision model from TypeSafe, reached through OpenRouter. It evaluates context against questions and returns typed answers with probabilities, rather than composing a conversational response.

JEV / DECISION LABYOUR RULES. YOUR LABELS.
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OUTPUT / JSON

Your decision

Run a decision or open the labeled sample to see the result here.

A high score is not a guarantee. Keep a person in control of consequential actions.

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Code checks exact rules, Jev selects bounded outcomes, and a language model can draft text. People review exceptions.
Code checks exact rules, Jev selects bounded outcomes, and a language model can draft text. People review exceptions.

A decision inside a larger workflow

A chat model can draft a reply or explain an idea. A decision model answers a bounded question such as which queue should receive a ticket. Your application defines the available outcomes and decides what to do with the answer.

Upstream Jev accepts three question types: choice (one label from the criteria you write), noul (a yes/no probability with true and false criteria), and score (a position on an ordered rubric). This workspace sends either a single choice question over 2–12 labels, or up to four typed questions in one request. The batch workspace can also generate a draft rule set from a short prompt, which you edit before anything runs.

OpenRouter: TypeSafe SDK and Jev · OpenRouter Jev tutorial · Request contract on this site

Structured does not mean infallible

Returning an allowed label prevents an unexpected output format. It does not prove that the chosen label is correct, and a high reported probability is the model’s own output rather than a measured accuracy for your data. Evaluate your own examples, keep an uncertainty path, and retain ordinary authorization checks around actions.

Evaluate classification before automating work

Known limits of Jev 1.13

TypeSafe’s version-specific limitations, reviewed by the provider on September 17, 2026, include unreliable counting, arithmetic and date comparisons. Keep those operations in code. Remove irrelevant context, align instructions with criteria, and test negation and indirect references on your own examples.

Adversarial text can influence the answer. A classifier must not be the only authorization or safety control. Separate questions also need not obey probability identities: do not assume a question and its negation sum to one, or transfer a threshold from noul to choice.

TypeSafe reports its strongest performance in English. A translated interface does not establish equal model accuracy in that language; evaluate each language and rule version separately.

TypeSafe: Jev 1.13 known limitations · TypeSafe: model and language notes

Our role

Jev API Pro is an independent workspace that calls Jev through OpenRouter. It is not TypeSafe and not the official Jev provider. Model availability and behavior depend on the upstream service; this deployment requests typesafe/jev-1.13.

Direct provider access may fit if you only need inference and already operate your own workflow. This service adds a batch workspace, review flags, result history, account API keys and prepaid credits. It does not claim exclusive model access or the lowest raw inference price. A Jev API Pro key is for this site’s /api/v1/decisions endpoint, not an OpenRouter or TypeSafe credential.

Plans and prepaid access · Try the batch workflow

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Guides

LLM as a judge, with rules you can read

In this workspace a judge is not a paragraph of criticism. It is one to four typed questions — a label choice, a yes/no probability, or a position on a rubric — answered about one record, so two people can disagree about the rubric instead of the wording.

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Answers come from the published product guide. For account-specific questions, contact support.

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