As the first System One model launched by @typesafeai, Jev is designed to enable software to make fast, structured decisions directly. Traditionally, AI is used to generate text, followed by additional code to parse the AI’s output. Jev takes a different approach: → Send your application state → Ask a typed question → Receive a typed decision → Along with the probability and confidence score for the result No JSON prompts. No cumbersome output parsing. Just structured decisions your application can use immediately. Jev can process three types of questions in parallel: 🔹 Choice — Select among predefined options 🔹 Score — Evaluate based on a specified scoring framework 🔹 Noul — Handle structured decision problems designed specifically for application logic Speed is also a core part of Jev’s design. Jev responds in approximately 70–500ms, making it ideal for scenarios requiring real-time AI decisions within software workflows—rather than waiting for lengthy text generation. Meanwhile, its pricing is optimized for high-frequency inference: 💰 Just $0.042 per million input tokens 🆓 Free output tokens This opens up compelling use cases for software infrastructure: 🎫 Ticket routing Automatically determine which team or workflow a new support request should be assigned to. 🛡️ Content moderation Make structured moderation decisions based on application state and predefined questions. 📊 Risk scoring Assess transactions, users, or events and return structured scores with confidence levels. 🤖 Agent branching decisions Let AI agents decide which tool to call, which workflow to execute, or what the next step should be. The core idea is simple: Traditional LLM workflows follow: Prompt → Text → Parse → Validate → Application logic Jev follows a more direct path: Application state → Typed question → Typed decision When the AI’s role isn’t to generate content for humans, but to rapidly execute a small, time-sensitive decision required by software, this difference becomes critical. Jev is now available via API at https://t.co/toijCbney0. Access it using either of these model names: Jev-1.13.0 or Jev-Latest For developers building AI applications, agents, automation systems, and decision pipelines, this represents an exciting direction: Turning AI from merely a “text generator” into a direct, structured decision layer within software. Try it out: https://t.co/ppprVoKA22 For more details, see the LLM Service documentation at https://t.co/toijCbney0. @justinsuntron @BAI_AGI #TRONEcoStar