Jev's layered routing cuts token overhead by 98%
A developer uses Jev as a reflex layer to move simple decisions and routing off heavy LLMs, claiming a 15,000% speed gain and about 40ms API latency.
Most developers treat LLMs like a cure-all, running every tiny agent decision through a $15/M token model. Jev Engineering fixes this structural flaw by separating rapid routing from heavy reasoning. The operational leap: Speed: +15,000% Token Overhead: -98.4% API Latency: ~40ms The execution pipeline: System 2 (Heavy LLM) -> Deep Analysis & Code Jev Layer -> Filter, Score, & Gate Worker Agent -> Execute Action State -> Update Loop By offloading simple yes/no checks, state updates, and routing away from the core model, you stop paying for unnecessary generation tokens. Heavy models should only be triggered when true reasoning is required. Everything else belongs to the reflex layer.
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