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An AI support agent receives a simple message: a customer was charged twice. Before composing its sympathetic reply, the software must identify the issue, select the right workflow, check whether more information is needed, and decide whether to escalate. The visible conversation sits on top of a small forest of decisions. Using a reasoning model for every branch can feel like convening a research committee to operate a traffic light. Sometimes the intersection deserves careful analysis. Most of the time, the application needs a quick judgment with a useful estimate of uncertainty. This is the opportunity behind Jev and the recent proliferation of decision models. Their appeal lies in making semantic judgments inexpensive enough to insert throughout software. The competition now includes Amazon, Cloudflare, and OpenAI, suggesting that the economics of these small decisions deserve as much attention as the capabilities of the largest models. What Jev actually computes Read more