AI GO turns a plain-language instruction into a running, auditable automation loop — deployed to an on-site edge core in minutes, not weeks. No PLC programmers. No six-figure integration projects. Built for the small factories that traditional automation left behind.
Taiwan alone has over 140,000 small and mid-size manufacturers. Most still log machine temperatures by hand and discover breakdowns the next morning — not because they don't want automation, but because every deployment today is a bespoke engineering project.
PLC logic, protocol wiring and control code are written by hand for each site — a costly, non-repeatable project every single time.
Hire a PLC engineer or commission a system integrator. Both cost hundreds of thousands of NT$ and require ongoing maintenance a 40-person factory can't sustain.
Readings logged hourly by hand get missed. Downtime is discovered the next day — or at month-end, when the losses are already booked.
The bottleneck isn't AI capability. It's the missing execution interface between language and machines — one a factory can operate without a single engineer.
A complete automation is a loop — sense, understand, decide, act, report. AI GO runs the whole loop from one plain-language instruction, with a hard boundary between the AI that understands and the engine that acts.
An LLM parses the operator's instruction — trigger, condition, target, action — into a structured intent spec. It never issues device commands directly.
A deterministic engine validates the spec field by field, then calls only pre-tested, permissioned Skills. Every action leaves a full audit trail.
PLCs, sensors, conveyors, cameras and IoT devices — reached through industrial standards (Modbus, OPC-UA, MQTT), mostly via signals the factory already has wired to its PLC.
The same natural-language surface handles monitoring, alerts, conditional control, scheduled reports and ad-hoc queries. "Log every machine's temperature every five minutes." "Notify me if any line stops for more than five minutes." "Which machine had the most downtime this week?" — one sentence each, no dashboard training, no code.
Devices are packaged as plug-and-play Skills behind a standard interface. Adding hardware means adding a Skill — not re-integrating the system. An open Skill Marketplace lets third parties ship modules for PLCs, robots and cameras, compounding into a network effect no single integrator can match.
And because inference and control run on the Edge AI Core inside the plant, automation keeps running when the network doesn't — with sensitive production data never leaving the site.
"What if the AI misunderstands?" is the first question every factory owner asks. Our answer is structural, not procedural — a mistaken interpretation stops at the spec layer, where a human catches it, before anything physical can move.
Language becomes a structured, human-readable spec before anything executes. The model proposes; it never commands.
The app reads the plan back in plain language. Nothing runs until the operator confirms; critical actions require explicit approval every time.
Only pre-tested Skills fire, gated by role permissions, action whitelists and dry-run simulation. Behavior is reproducible and provable.
Every action is logged for traceability. Cloud and edge are separated by design, keeping OT data inside the plant per industrial security practice.
Everything a factory needs to go from spoken intent to running automation — sold as an affordable monthly subscription plus one piece of on-site hardware.
Natural-language input, plain-language confirmation, live dashboards, push alerts, and conversational queries — "what was line 2's yield last month?" answered with charts.
Accounts, device sync, workflow management, cross-site analytics and remote oversight — the SaaS backbone that scales from one machine to a fleet.
On-premise inference, task planning and device control on commercial edge hardware. Keeps executing through network outages; keeps production data on-site.
Speaks the plant's native protocols
Global smart manufacturing is heading toward US$620B by 2026, yet the economics of traditional integration only work for large plants. We don't fight incumbents for factory-wide contracts — we serve the tier they structurally cannot reach.
Tiered monthly and annual plans — the cash-flow backbone that grows naturally with each customer's devices and features.
One-time hardware revenue that anchors the deployment and drives follow-on sensors, upgrades and Skill bundles.
Revenue share on third-party device modules — near-zero marginal cost, network effects, and the long-term moat.
The core runtime has been built and tested against real embedded devices, sensors and control workflows, validating the architecture across multiple hardware scenarios.
First place among campus teams, validating both the problem and the team behind it.
AI GO's long-term position is the AI Control Layer between language and physical equipment — starting with Taiwan's small manufacturers, then Southeast Asia's, wherever labor shortages meet an automation barrier that shouldn't exist.
AI GO's challenge is more than technical. Systems that act on real equipment demand operational deployment environments, hardware partners, and feedback from organizations automating in complex settings — none of which software alone can supply.
FII's international network, investment ecosystem, and focus on AI-driven societal impact align directly with our mission: scaling AI GO from pilot deployments into globally deployable automation infrastructure for the factories every other solution skips.
Operational settings and industry collaborators to validate natural-language automation where it matters most.
Guidance on scaling edge AI across diverse hardware ecosystems while staying affordable for resource-constrained factories.
Strategic mentorship from industrial-automation leaders, robotics experts and infrastructure-focused investors.
Connections to the industries and regions — including Southeast Asia — that stand to gain most from accessible automation.
AI GO needs two kinds of fluency — how factories actually run, and how AI systems are actually built. The founding team covers both ends, out of National Tsing Hua University.
Industrial Engineering + Computer Science, NTHU. Translates production-floor pain points into product requirements — leading strategy, factory relationships, and the government-subsidy channel.
EE + CS, NTHU. Multi-startup CTO with internationally published research in NLP, LLM agents and model efficiency — architecting the runtime, intent engine, cloud-edge system and Skill SDK.
⌁ Originating from National Tsing Hua University · Hsinchu, Taiwan
We're connecting AI GO with the partners, pilot sites and investors who will bring natural-language automation to the factories the industry forgot.