The operating system for physical AI. Control any robot, sensor, or machine with a single sentence — planned and executed 100% offline, on the device itself. No code. No cloud. No leaks.
This is the EdgeMind runtime, running locally. Pick a command — or write your own — and watch a sentence become a validated, executable physical action plan. Zero cloud calls.
Today's assistants can talk. They can't do. EdgeMind Core is the universal control layer that gives AI hands — turning a sentence into motion, sensing and decisions across robots, industrial gear, appliances and IoT. No code. No cloud. No limits.
State what you want the world to do, in plain language. No SDK, no wiring diagrams.
The on-device agent parses intent, retrieves Skills, schedules execution and validates safety.
The Hardware Abstraction Layer fires the right protocol — UART, ROS, MQTT, Modbus — and it's done.
Inference, planning and memory all run on-device.
Data never leaves the machine.
From a $4 MCU to a robot fleet.
Language is the only interface.
Modular, shareable, composable.
A prompt falls through six layers and comes out the bottom as physical action. Each layer is independently swappable — an Embodied Agent Runtime, not an LLM wrapper.
A local LLM parses your words into structured, executable intent: extraction, constraint parsing, temporal reasoning, device grounding and action-dependency analysis. All on the edge.
# "Check the living-room temp. If over 30°C, # turn on the fan and notify me." { intent: "environment_control", conditions: [ { sensor: "temperature_sensor", operator: ">", value: 30 } ], actions: [ "turn_on_fan", "send_notification" ] }
The Planner never touches hardware directly. It decomposes the goal, retrieves Skills, schedules execution, tracks state and recovers from failure — a true autonomous loop.
"Patrol the warehouse. If you hear an anomaly, record it and raise an alarm."
Begin autonomous patrol route.
Stream microphone input continuously.
Detect abnormal acoustic signatures live.
Capture footage on anomaly detection.
Store to local memory.
Notify the operator immediately.
Each device capability is abstracted into a Skill — metadata, an execution API, safety constraints, resources and capability tags. Load them like ROS nodes, isolate them like containers, call them like AI tools.
skill_name: move_forward device: wheeled_robot inputs: [ distance ] outputs: [ status ] permissions: [ motor_access ]
skill_name: capture_image device: usb_camera inputs: [ resolution ] outputs: [ image_tensor ] permissions: [ camera_access ]
Brings Skills to life — safely.
Skills composed on the fly to satisfy any plan.
A hardware skill economy. Publish once, AI-ify anything.
AI should never care whether it's talking UART, GPIO, MQTT, ROS or Modbus. Write device.move(x=1.0) once — the HAL translates it to whatever the metal speaks.
Even with the network down, EdgeMind keeps thinking and acting. On-device inference, offline planning, local vector DB and edge-GPU acceleration — squeezed into low-power silicon with 4/8-bit quantization, KV-cache optimization and speculative decoding.
Camera, LiDAR, IMU, audio, depth and temperature streams fuse into a single Physical World State Representation — so the AI reasons about reality, not just text.
▸ Fused inference: "Possible equipment fault or hazardous event."
When an AI moves a motor, a wrong token can break a machine — or a person. EdgeMind wraps every action in hard, non-negotiable guardrails.
No Skill runs without explicit grants.
Every high-risk action is pre-checked.
Malicious Skills are caged. Never reach:
| Capability | Legacy IoT | Generic AI Agent | EDGEMIND |
|---|---|---|---|
| Natural-language control | Partial | Yes | Yes |
| Control physical devices | Limited | Rare | ★ Core |
| Fully offline | Few | Almost none | ★ Core |
| Modular skills | None | Partial | Full |
| Unified multi-hardware | None | None | Yes |
| Agent planning | None | Partial | Full |
Core runtime, intent engine and HAL on Jetson + Raspberry Pi. First design partners onboarded.
Public Skill definition format, runtime sandboxing and the safety validation suite.
Third-party developers publish drone, industrial and smart-home Skills.
Fleet orchestration, cross-device memory and the universal runtime for reality.
No human should need to understand GPIO, UART, PLC, MQTT or ROS topics ever again. Just describe what you want the world to do.
Open-core today, enterprise-ready tomorrow. All tiers run fully offline — you own your data and your hardware.
See the runtime in action, or get in touch with team ALL GOOD.