AI-Native OS
EmbeddedOS + EAI Platform
EmbeddedOS is designed from the ground up to run AI workloads on constrained hardware — from 64 KB microcontrollers to 8-core NPU boards. On-device LLM inference, neural decoding, and autonomous agent runtime.
EAI Stack Architecture
EAI Capabilities
On-Device LLM Inference
Run quantized LLMs (1B–7B parameters) on devices with 512 MB+ RAM. Supports GGUF, ONNX, and TFLite formats.
NPU Acceleration
Hardware NPU support for RK3588S (6 TOPS), Cortex-M55 (Helium), and ESP32-S3 (vector extensions). Automatic NPU offload.
Optimised Inference Pipeline
INT8/INT4 quantization, layer fusion, and memory-mapped model loading, tuned for models small enough to run on an MCU.
Manifest-Pinned Models
Every AI model is pinned to a specific version in the EAI manifest. No silent model updates — full audit trail of what ran on each device.
Multi-Framework Support
TensorFlow Lite, ONNX Runtime, and llama.cpp backends. Unified EAI API regardless of model format or framework.
eBot Agent Runtime
Full LLM agent runtime with tool calling, memory, and multi-step reasoning. Deploy autonomous agents on embedded hardware.