By executing tools locally and summarizing offline, EAM completely eliminates context-window bloat, slashing per-task costs by over 90%.
The Brain vs. Muscle Architecture. We use massive Cloud LLMs solely as the "Brain" for planning. EAM acts as the "Muscle" — a sub-300M parameter local reflex layer that routes API calls instantly without deep reasoning, maximizing edge performance.
By compiling our models with extreme 4-bit quantization, we bypass the standard 1-2 second cloud network latency. EAM dispatches multiple tool calls simultaneously, finishing tasks in milliseconds.
To make Agentic AI and complex agentic tasks easily accessible and globally available.
Building the foundational sub-300M parameter edge model for localized execution.
Optimized specifically for agentic task execution over general reasoning.
Translating agentic tasks into physical actions to seamlessly control real-world devices and robotics.