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What is Jev AI?

Jev is a System One model from TypeSafe AI that returns structured, typed decisions instead of free-form text. It handles routing, gating, and scoring — the bounded decisions that sit between your code and your LLM.

Jev in one sentence

Jev is a model that answers typed questions about a state and returns typed decisions. Instead of generating paragraphs of text, it returns a value your code can branch on directly: one option from a set, a yes/no answer, or a level on a scale.

Jev is not a language model

An LLM generates text. It predicts the next token, one at a time, and produces open-ended output — an essay, a summary, a conversation. That is its strength, and also its weakness when you need a deterministic answer.

Jev does something different. You give it a question with a bounded set of possible answers, and it returns one of those answers. No tokens to parse, no JSON to validate, no prompt engineering to coerce the output into the right shape.

This does not make Jev better than an LLM. It makes it different. Most AI workflows need both: an LLM for generation, Jev for decisions, and ordinary code for everything deterministic.

JevTyped decisions, bounded outcomes
LLMOpen-ended text generation
CodeDeterministic computation

How Jev works

You define a decision: a question, a set of inputs, and a fixed set of possible outcomes. Jev evaluates the inputs and returns one outcome. The outcome is typed — your code receives a value it can switch on immediately, without parsing prose or extracting JSON from a text response.

For example, if you need to route a support ticket to one of five teams, you define a Jev Choice with five options. Jev reads the ticket and returns one team name. Your code routes the ticket. No prompt engineering, no output validation, no retry loop.

Jev is made by TypeSafe AI and is currently in Early Access. The API may change — always validate before production use.

The three Jev primitives

What Jev is good at — and not good at

Good at

  • Routing and classification with known options
  • Binary gates: should this escalate, approve, or flag?
  • Scoring against a rubric with defined levels
  • Decisions that need to be fast, cheap, and repeatable

Not good at

  • Generating text, summaries, or explanations
  • Translation or language tasks
  • Arithmetic, lookups, or deterministic computation
  • Open-ended questions with unbounded answers

Jev and LLMs work together

The most common pattern is not Jev or an LLM — it is Jev and an LLM in the same workflow. The LLM generates text; Jev makes decisions about that text.

For example: an LLM drafts a reply to a customer. Jev Noul decides whether the reply needs human review before sending. Jev Score rates the reply quality. Code sends the email if it passes. Three tools, three jobs, one workflow.

See when to use Jev →Compare Jev and LLMs
Jev is in Early Access

Jev is a product of TypeSafe AI and is currently in Early Access. The API may change between versions. Jev Fit + Builder is an independent tool that designs your architecture and generates specs — it does not call Jev for you. To run any generated implementation, you need your own access from TypeSafe AI.

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FAQ

Frequently asked questions

No. Jev is a System One model that returns typed decisions. It does not generate free-form text the way an LLM does.

Still have questions?

Contact support@jevmodel.co →