MEET JEV · SYSTEM ONE MODEL

Meet Jev.
Built for decisions.

Intelligence. Into action.

Turn information into decisions. Put software in motion.
Meet Jev, a fast decision model built for automation.

See Jev in action Explore System One

Type safetyParallel decisionsCalibration

THE DECISION ENGINEJEV / 01

Unstructured inputStructured decisions

70–500ms

End-to-end latency^*

1 call

One call. Parallel answers.

Type-safe

Typed values, ready for code

P(decision)

A probability for every decision

* Latency source: TypeSafe AI announcement. Actual performance varies by task and operating conditions.

01 / A DIFFERENT KIND OF INTELLIGENCE

What is Jev?

Jev is TypeSafe AI’s first public System One model, built for automated decisions inside software.

Provide a state and predefined questions. Jev returns typed answers and their probabilities so your software can choose the next step using business rules.

Try the decision flow below

02 / FROM STATE TO DECISION

How does Jev work?

Provide context, define question types, then read structured decisions. The preset examples below show support routing, content review, and lead scoring.

01 / STATEInput state

Customer messageTEXT

Hi, I subscribed to Pro last week, but I noticed a duplicate charge today. Could you refund the extra payment? Thanks!

TYPED QUESTIONSPredefined questions

intentenum

needs_humanboolean

urgencyscore 1–5

JEV

02 / DECISIONStructured output

Example result

{
  "intent": {
    "value": "refund",
    "probability": 0.96
  },
  "needs_human": {
    "value": true,
    "probability": 0.92
  },
  "urgency": {
    "value": 4,
    "confidence": 0.89
  }
}

Example next step: route to a billing specialist for priority review of the duplicate charge.

Provide context

Messages, logs, or business events,
just as they are.

Define the answer's shape

Categories, booleans, or scores:
you choose the output type.

Let software act on decisions

Read the results and probabilities,
then apply business thresholds to choose the next step.

03 / A NEW PRIMITIVE

How does Jev differ from LLMs?

Jev returns typed decisions and probabilities for predefined questions. LLMs focus on general generation and reasoning, and can also constrain JSON through structured outputs.

Type safety ≠ a correct decision.
Critical workflows still need thresholds, validation, and human oversight.

Compare Jev and LLMs

Four dimensions for comparing Jev and language models.
Dimension	Language models	Jev · System One
Output	Text or constrained JSON	Values of predefined types
Process	Token-by-token generation	Multiple decisions in parallel
Integration	Parsing or Schema constraints	Read structured results
Probability	Depends on the model and API	Returns calibrated probabilities

Based on Jev's published positioning. Actual capabilities depend on the API. TypeSafe AI · Jev official website

04 / INTELLIGENCE, EVERYWHERE

What can you use Jev for?

Jev can support workflow routing, real-time interaction, batch feature extraction, and model safety assessment.

01 / ROUTING

Every task, in the right place.

Identify intent, assess priority, and route messages and tickets to the right handler.

Workflow routing

02 / REAL-TIME

Feedback that keeps up.

Assess the current state quickly to inform decisions in latency-sensitive interactive systems.

Real-time interaction

03 / EXTRACTION

Find signals in the noise.

Turn unstructured content into categories and scores that software can process.

Batch feature extraction

04 / GUARDRAILS

One more check before acting.

Assess risks in prompts and model outputs to help systems decide whether to allow, review, or block them.

Model safety assessment

LESS WAITING. MORE DOING.

Make intelligence part of the action.

Start with a question. Explore the next decision Jev could make for your system.

Get early API accessRequest access at typesafe.ai

Meet Jev. Built for decisions.