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TypeSafe AI

Machine-native intelligence infrastructure for automation

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TypeSafe AI develops System One Models, a class of AI model intended to make decisions within software rather than generate text for human readers. Its first public model, Jev, is designed for automation workflows: it accepts structured questions and returns typed decisions accompanied by confidence probabilities, allowing software to act on the output or route it for review. The company describes its approach as distinct from chat-oriented language models, using a new architecture, sampler, and training method called Reinforcement Learning for Calibrated Decisions (RLCD).

Jev's outputs are typed and type-safe, with each decision carrying a confidence estimate that applications can use to set thresholds for autonomous action versus escalation. Decisions can be combined in code to build larger workflows, giving developers control over how the model's intelligence is applied. The homepage presents Jev as fast and low-cost relative to language-model workflows, and notes that it is deterministic. Pricing is listed per billion input tokens, with no free plan, trial, or seat-based editions described.

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