Large language models can reason about the world — but they can't act in it. EdgeMind Core is the missing runtime that lets AI agents perceive, reason about, and directly control real-world devices: sensors, robots, Arduino, FPGAs and industrial hardware — fully offline, low-latency, and without rewriting your existing systems.
Today's models have powerful reasoning. But the moment an AI needs to control a sensor, a robotic arm, an Arduino board or industrial equipment, it falls back on engineers writing custom integrations, device protocols and control logic by hand — turning every deployment into expensive, one-off engineering.
Capable of language, reasoning, planning and decision-making.
Fragmented protocols, custom firmware, isolated control logic.
Custom firmware, protocol handling and control logic make each automation a costly, non-repeatable engineering project.
Small and mid-size factories face labor shortages and knowledge gaps, but lack the engineering and AI teams to deploy automation.
Even simple device coordination takes weeks or months to integrate — keeping AI out of real-world environments entirely.
The real bottleneck for Physical AI isn't model capability —
it's the absence of a standardized execution interface that lets AI generalize across real-world devices.
A Local-First Physical AI Runtime that gives AI agents a unified capability layer and a modular skill system — so they can sense, reason, and control real devices directly, without rewriting your hardware.
Natural-language intent, reasoning and planning from any LLM or agent.
A unified capability model lets the agent discover what each device can do; modular skills translate intent into safe, permissioned actions — locally, with low latency.
Sensors · Arduino · FPGAs · robotic arms · cameras · edge devices.
Instead of bespoke firmware development or custom system integration, EdgeMind exposes every connected device through a unified capability layer. An AI agent simply asks what a device can do — and a modular skill system handles how it gets done.
The result: AI understands device capabilities and executes real-world tasks from natural-language instructions — monitoring environmental conditions, coordinating robotic actions, or driving industrial equipment — without anyone building custom AI infrastructure from scratch.
Because it's local-first, EdgeMind keeps running where the cloud can't: on the factory floor, in classrooms, in remote sites and low-network environments — with permission-based safety controls on every action.
EdgeMind transforms fragmented hardware into AI-accessible systems — designed from the ground up for reliability outside the data center.
Inference and control run on-device, so actions fire in real time — no round-trip to the cloud, no jitter on the factory floor.
A local-first runtime that stays online when the network doesn't — keeping Physical AI persistent in remote and disconnected environments.
Device behaviors are packaged as composable skills, so new hardware plugs into the same interface instead of demanding a fresh integration each time.
Every action passes through explicit permission gates — giving operators control over what an AI agent is allowed to do in the physical world.
Hardware EdgeMind speaks natively
The same runtime, the same interface — deployed across environments where conventional AI integration is too slow, too costly, or simply impossible.
Coordinate machines, monitor conditions and automate workflows without an in-house AI team — closing the labor and knowledge gap that holds SMEs back.
Students and makers control real hardware through natural language — turning Arduino, sensors and robots into a hands-on Physical AI playground.
Offline edge inference keeps automation running in remote facilities and low-connectivity environments where cloud AI breaks down.
We've built and tested the core EdgeMind runtime in real-world hardware environments, validating the technical feasibility of the architecture across multiple scenarios.
The next wave of AI won't live in a chat window — it will live among us, operating physical systems reliably over the long term. EdgeMind Core is building the infrastructure layer that makes Persistent Physical AI possible.
EdgeMind Core tackles a challenge that needs more than technical innovation — it needs strong global partnerships across industry, infrastructure and emerging AI ecosystems.
Physical AI systems can't be validated by software alone. They demand real-world operational settings, hardware integration partners, and feedback from organizations deploying automation in complex environments.
FII's international network, investment ecosystem and focus on AI-driven societal impact make it uniquely aligned with our mission — to scale EdgeMind from pilot implementations into globally deployable Physical AI infrastructure.
Access to large-scale operational settings and strategic industry collaborators to validate Physical AI where it actually matters.
Guidance on scaling edge AI across diverse hardware ecosystems while staying accessible to organizations with limited engineering resources.
Strategic mentorship from global industry leaders, robotics experts and infrastructure-focused investors.
Connections to the industries and communities that will benefit most from accessible, real-world AI automation.
A founding team combining product vision and deep systems engineering, building out of National Tsing Hua University, Taiwan.
Leads product vision and strategy for EdgeMind Core — translating the Physical AI thesis into a runtime the world can deploy.
Architects the local-first runtime, capability layer and edge execution that lets AI agents safely control real-world hardware.
⌁ Originating from National Tsing Hua University · Hsinchu, Taiwan
We're connecting EdgeMind Core with the partners, deployment environments and investors who will help bring Persistent Physical AI to life.