JeV is TypeSafe AI’s inaugural System One Model, engineered to deliver fast, structured, machine‑native decisions rather than open‑ended language generation. Inspired by Kahneman’s System 1 thinking, JeV trades free‑form text for schema‑constrained outputs (e.g., enum categories, calibrated probabilities, booleans) that are guaranteed to match a pre‑defined type definition, eliminating type‑mismatch errors by construction. Its training employs Reinforcement Learning for Calibrated Decisions (RLCD), which optimizes confidence calibration so that a reported 80 % confidence aligns with actual correctness ~80 % of the time, unlike typical RLHF/RLVR objectives. Sampling is performed in parallel: all decision fields and their probabilities are produced in a single pass, avoiding the autoregressive token‑by‑token generation of conventional LLMs. TypeSafe reports end‑to‑end latency of 70 ms–500 ms for JeV versus 3–329 seconds for frontier LLMs on comparable tasks, implying a 40×–200× speedup; input token pricing is $0.042 per million tokens with output deemed “too cheap to meter,” compared to $0.20–$10 per million input tokens and roughly 5× higher output costs for existing models. Workflow‑level evaluations claim up to 193.6× faster and 444.6× cheaper execution. Demonstrations include a Doom‑playing bot operating at ~10 queries per second (≈$7/hour) and a Wikiracing benchmark where JeV required fewer steps than LLMs in non‑reasoning mode. The model’s “no hallucination” claim is limited to guaranteeing type‑valid outputs, not factual correctness, and all performance figures remain vendor‑reported pending independent verification.