A decision model for your software
Jev is an AI model from TypeSafe AI for making structured judgments inside software. Give it information and a set of well-defined questions; your application receives answers it can use to choose the next step. TypeSafe calls this model family System One. Official introduction
Imagine an inbox containing hundreds of customer messages. Before anyone writes a reply, several decisions need to happen: which team owns the issue, how urgent it seems, and whether a person should review it. That is the kind of work Jev is designed to help with.
Your application still decides what to do. A model’s recommendation to send a ticket to engineering does not itself create a ticket, contact a customer, or authorize a refund.
Explore the technical details
Three ways to ask a question
| Primitive | Use it when you need… | An example question |
|---|---|---|
| Choice | One answer from options you define | Which queue should receive this message? |
| Score | A rating against ordered descriptions | How strongly does this article match our topic? |
| Noul | The probability that a statement is true | Does the customer explicitly request a callback? |
You can ask several questions about the same input in one request. Jev evaluates them independently; your code combines the answers. Question types
The distinction matters: “How urgent is this?” needs a defined scale. “Does this mention a deadline?” asks a narrower yes-or-no question. Choosing the right question is part of designing the application.
What Jev does—and what you supply
Jev currently works with text, including text represented in JSON objects or arrays. It does not directly accept images, audio, or video, and it does not generate an email, essay, explanation, or code. System One documentation
You supply the relevant context, possible choices, scoring criteria, and rules for handling the result. For a document workflow, that could mean extracting text first, asking Jev to judge relevance, and using a separate tool to summarize the selected documents.
Start with practical use cases, or read Jev vs. LLMs to decide which parts of your workflow belong with which tool.
Explore the technical details
Why the probabilities matter
TypeSafe describes its training approach as Reinforcement Learning for Calibrated Decisions, or RLCD. The aim is for reported probabilities to match outcomes across many predictions. For example, among comparable predictions assigned a 70% probability, the event should occur about 70% of the time in a well-calibrated system. That is a statistical property, not a promise about one answer. TypeSafe’s AI primer
For a developer, this opens a useful design choice: act on clear cases, collect more context for ambiguous ones, and send difficult cases to a person. The thresholds still need evaluation on the task you actually care about.
Where to go next
- Developers: start with one semantic check, then keep execution and permissions in code.
- Support teams: separate queue selection, urgency, and the decision to involve a person.
- Search and RAG teams: find relevant evidence before asking another model to write an answer.
- Security teams: explore review signals while retaining established controls and analyst judgment.
The use-case guide follows these roles and includes a complete official document-search walkthrough. The limitations guide explains where the interface, evidence, and operational assumptions stop.
Common questions
Is Jev a chatbot?
No. Its output is constrained to the decisions you define. It can help a chat application choose a route or evaluate a response, while a separate model or template provides the actual wording.
Does structured output mean the answer is correct?
No. A response can fit the expected type and still choose the wrong category. Treat format guarantees and decision accuracy as separate properties. Our comparison guide explains the distinction.
Can I try it right now?
Start with our Playground: load an example or ask your own question. Live checks depend on the site’s availability and usage limits. For your own API integration or the official Playground, follow the getting-started guide and the official TypeSafe website; a TypeSafe account and the required product access are separate from this website.
What does it cost?
See the pricing guide and calculator for the published input-token rate, a practical estimate, and what the estimate leaves out.
Does this website’s language selector describe Jev’s language support?
No. The website’s 12 reading languages describe this guide’s translations. They are not a model capability list, an accuracy claim, or evidence that the same question works equally well in each language. Evaluate the language and terminology of your own workflow.
Is this the official Jev website?
No. What is Jev is an independent educational guide, unaffiliated with TypeSafe AI. We link to primary sources and label examples by their evidence. Read our editorial approach.
Sources & further reading
Official documentation and linked community reports support this guide. Community observations are attributed to their authors.
How we check our sources