Jev replaces generated text with typed parallel decisions

TypeSafe AI’s first System One model takes unstructured state plus predefined Choice, Score, or Noul questions and returns typed probabilities and confidence. It evaluates independent questions in parallel instead of generating a response token by token; the company reports 70–500 ms latency, $0.042 per million input tokens, and unmetered outputs for this narrower decision interface. Why it matters: Jev tests a different automation boundary: keep generation in an LLM, but move repetitive classification, routing, scoring, extraction, and verification into a model whose possible outputs are fixed before the call. That could make high-volume decision steps faster, cheaper, and easier to contain—without making the underlying judgments automatically correct.

Try this: Compare Jev with your current LLM on one routing workflow. Record an eval trace with latency, cost, calibration, escalation, and schema-error rates; keep the LLM for generation and test Jev only where the allowed outputs can be defined in advance.