Jev decision model demo: browser agent picks a flight in 7.1 seconds
Jev evaluates predefined options in parallel and returns typed values with probability distributions; in the demo a browser agent uses it to find a Zürich to London flight in 7.1 seconds.
Jev is what happens when you stop forcing AI to talk. It’s the first public “System One” model from TypeSafe AI, built for fast decisions inside software—not conversations. You give it: • Unstructured state: text, logs, tickets, documents • A predefined schema: the questions and allowed answers Instead of generating tokens one by one, Jev evaluates the choices in parallel and returns typed values with probability distributions: billing: 92% technical: 6% sales: 2% urgent: 87% TypeSafe trains it with RLCD: Reinforcement Learning for Calibrated Decisions. The goal isn’t better prose, but decisions whose confidence reflects how likely they are to be correct. No free-form text means no malformed JSON, invented fields, or parsing retries. In the attached demo, a browser agent uses Jev to find a Zürich → London flight in 7.1 seconds by choosing each operation and target in real time. LLMs generate the plan. Jev could become the decision engine running inside every software loop.