TypeSafe Jev
A decision-only model from the San Francisco lab TypeSafe that answers declared questions with typed choices, scores or yes/no probabilities, so harness code can threshold them instead of parsing generated text.
Description
TypeSafe AI came out of stealth on 15 September 2026 with Jev as its first public model, which it calls a System One model built to make fast decisions inside software rather than to chat. A caller sends a text state plus declared questions of three shapes: Choice (pick one of a fixed set, returned with a distribution and a confidence), Score (a position on caller-defined levels) and Noul (a yes/no probability). Answers cannot fall outside the declared schema, questions over the same state are evaluated independently, and the model is trained for calibration so that its probabilities can be thresholded in code. Most calls complete in about 100 milliseconds. It does not write text, code or explanations, and it is weak at arithmetic and at questions that require cross-step reasoning. SDKs exist for Python and JavaScript, and a Vercel AI SDK provider package exposes it to TypeScript harnesses.
Solution
No agent loop of its own. The harness calls Jev at a branch point with the current state and a set of declared questions, receives typed answers with probabilities and confidence for each, and applies thresholds in code to pick the next step, send an uncertain case to a human, or fall back. Responses name the versioned model that answered so thresholds can be tuned per model version.
Primary use cases
- branch conditions inside agent harnesses
- intent and ticket routing and triage
- tool and skill selection from a closed set
- safety gates such as classifying shell commands by risk
- checking model outputs or claims against source material
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