Jevstiller distills Jev into a local model with a 98% agreement guarantee

Jevstiller places a small local model in front of the Jev classification API, returning answers in ~15 ms on CPU instead of ~300 ms. It guarantees that, for a chosen target like 98%, the system's overall output agrees with Jev at least that often. It uses a frozen bge-small encoder with a multinomial logistic regression head trained on Jev's full probability distribution, retrained every 2,000 new answers and shadow-tested before promotion. A router combining a confidence threshold and a kNN out-of-distribution scorer decides whether the local model answers or the request is forwarded to Jev. The post derives the agreement formula A = 1 − c·e and explains why a naive confidence threshold breaks the guarantee. Costs, limitations, and a benchmark reproduction command are included.