ROUTEAgents & automation
Jev: Typed Probabilistic Decisions
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Jev is a model that provides typed probabilistic decisions instead of generating text, designed for routing, scoring, and real-time agents.
Pattern↑ Jev AI decision models
TypeSafe AI just shipped Jev: a model that refuses to chat.
Instead of generating text, it returns typed probabilistic decisions software can call like a function. Unstructured state in, structured answers out. They call this class System One models, trained with Reinforcement Learning for Calibrated Decisions (RLCD).
Why it matters: most production automation fails when an LLM invents a field, slows a loop to seconds, or cannot say how sure it is. Jev bets the opposite stack wins for routing, scoring, and real-time agents.
• Latency: claims 70ms to 500ms end to end, roughly 40x to 200x faster than frontier chat models on System One shaped tasks.
• Cost: about $0.042 per million input tokens; output tokens billed as free (too cheap to meter, their words).
• Guarantees: schema-matched outputs, no type errors by design, calibrated confidence with every answer.
• Fit: smart if-statements inside workflows, map-reduce over large corpora, judges and guardrails for other models, even a Doom bot driven by structured game state.
• Caveat: early access, and it gives up free-form string generation. Chat and open-ended coding stay with LLMs.
If your product needs decisions inside code, not essays in a chat box, this is the split to watch.