Kev is a family of small decision models (0.8B–9B parameters) built on Qwen3.5 for classification tasks like customer support ticket routing. Users can run pretrained weights locally on CUDA or Apple Silicon, with a TypeSafe-compatible API and web playground for testing.
RoboKrunch benchmarked TypeSafe's Jev decision model on simulated warehouse robot fleet triage, finding it costs $24.57 per million decisions with sub-second latency and requires no training data. Self-hosting a small model becomes cheaper above ~1M decisions per month, but Jev wins on startup cost and simplicity for smaller deployments.
Jev, a TypeSafe AI decision model, evaluates structured state against typed questions and returns choices, scores, and probabilities without text generation. The AI SDK's experimental_evaluate API exposes Jev through the Vercel AI Gateway in TypeScript, enabling applications to route decisions—like support ticket departments or severity scores—based on model assessments while keeping business logic in code.