About TypeSafe AI
Overview
TypeSafe AI is an AI lab building machine-native intelligence infrastructure for automation. Its first public System One Model, Jev, makes decisions inside software rather than producing chat text.
Jev returns typed decisions with calibrated probabilities and a confidence estimate for each decision, so software can act when confidence is high and escalate for review when it is not. TypeSafe reports Jev costs $42 per billion input tokens and delivers 193.6x faster and 444.6x cheaper workflows than LLMs on System One tasks.
Key Benefits
- Decisions, not strings: typed outputs that software can act on directly
- Calibrated confidence on every decision
- Reliable, fast, and type-safe behavior that is more like code than chat
- Zero hallucinations: every decision ships with a confidence estimate
- Reinforcement Learning for Calibrated Decisions (RLCD) training algorithm
Use Cases
- Automation engineers send Jev structured questions and consume typed decisions in code
- Platform teams set confidence thresholds for autonomous action versus human review
- Developers chain decisions in code to build larger workflows
- Teams building software that must account for uncertainty when acting on model output