Industrial AI

Give AI Agents Access to Your Factory. Without Giving Up Control.

UMH contextualizes factory data for AI and is built to be configured by AI. Agents monitor, adjust, and improve your production 24/7, all through CLI and APIs. Nothing reaches your shop floor unchecked.
See how it fits your setup
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AI works with expert-contextualized data, not raw signals
Make your legacy equipment AI-ready
CLI and APIs built for AI agents
Machine handbooks to AI-ready data pipelines, automated
AI agents access data outside your OT, every command validated
Open source core, zero vendor lock-in

Trusted by global enterprises

The Challenge

Every factory wants AI agents improving production. None are ready.

Your factory data sits where AI agents shouldn't be
Factory data lives in OT environments, the most vulnerable part of your infrastructure. There's no validated interface between AI and your shop floor. No command validation, no access control, no guardrails. Every AI vendor that needs your data is a risk you carry alone.
AI agents can't work with raw factory data
The knowledge that makes machine data useful sits in your engineers' heads. What a value means, what's normal, what's critical. Without that context built into the infrastructure, AI agents are guessing, not deciding.
Making factory data safe and AI-ready the traditional way is too slow and expensive
When the platform itself is AI-native, agents configure the contextualization, build the pipelines, and set up the guardrails. What used to take years takes days.
"AI is a topic where almost daily I get asked by top management: what are we doing with AI in production? And I always have to explain that you need to have data to start. Therefore, my standard answer is: what we are doing with AI is providing a common data platform with UMH so that we'll be able to use it"
Head of Processes, Digitalisation and Applications
Our solution

The layer between AI agents and your factory.

UMH sits between AI agents and your factory. It creates a secure boundary around your OT, contextualizes your data with your engineers' expertise, and gives agents full programmatic control through CLI and APIs. Because UMH itself is AI-native, setting all of this up takes days, not years.
AI agents never touch your factory directly. UMH validates every command, logs every action, and controls every access point. Agents get what they need without getting in.
Secure boundary between AI agents and production
Machine handbooks, TIA exports, and your engineers' knowledge, mapped into the infrastructure. Agents work with data that already has meaning, not raw signals.
Your engineers' expertise, built into the data
Traditional setup takes years. UMH is built to be configured by agents through CLI and APIs. Agents build the very infrastructure they run on. Six-figure projects become days.
AI-native architecture. From years to days.

What automotive manufacturers achieve with UMH.

First ever
AI agents running in production, safely
90% lower cost
Six-figure application rebuilt in days
10x
Use cases shipped by AI agents

Engineers provide context. Agents scale it and build on top.

Step
Connect and contextualize

Your OT team connects machines, maps context from handbooks and TIA exports, and defines validation rules.

step
•
Connect and contextualize
Who:
Jeremy (CTO)
Format:
Video call with screen sharing

The format:

  • 30 minutes to code.
  • 15 minutes to discuss what you'd change to make it production-ready.

The format:

  • 30 minutes to code.
  • 15 minutes to discuss what you'd change to make it production-ready.

The format:

  • 30 minutes to code.
  • 15 minutes to discuss what you'd change to make it production-ready.

The format:

  • 30 minutes to code.
  • 15 minutes to discuss what you'd change to make it production-ready.
Step
Agents scale the infrastructure

Once guardrails are in place, agents replicate: more machines, more pipelines, more contextualized data. All validated.

step
•
TEXT
Agents scale the infrastructure
Who:
Jeremy (CTO)
Format:
Video call with screen sharing

The format:

  • 30 minutes to code.
  • 15 minutes to discuss what you'd change to make it production-ready.

The format:

  • 30 minutes to code.
  • 15 minutes to discuss what you'd change to make it production-ready.

The format:

  • 30 minutes to code.
  • 15 minutes to discuss what you'd change to make it production-ready.

The format:

  • 30 minutes to code.
  • 15 minutes to discuss what you'd change to make it production-ready.
Step
Agents run against UMH, not your machines

AI agents access contextualized data through CLI and APIs. They never touch your OT environment.

step
•
TEXT
Agents run against UMH, not your machines
Who:
Jeremy (CTO)
Format:
Video call with screen sharing

The format:

  • 30 minutes to code.
  • 15 minutes to discuss what you'd change to make it production-ready.

The format:

  • 30 minutes to code.
  • 15 minutes to discuss what you'd change to make it production-ready.

The format:

  • 30 minutes to code.
  • 15 minutes to discuss what you'd change to make it production-ready.

The format:

  • 30 minutes to code.
  • 15 minutes to discuss what you'd change to make it production-ready.
Step
Use cases multiply

Same infrastructure, unlimited applications on top. What used to take months takes days.

step
•
TEXT
Use cases multiply
Who:
Jeremy (CTO)
Format:
Video call with screen sharing

The format:

  • 30 minutes to code.
  • 15 minutes to discuss what you'd change to make it production-ready.

The format:

  • 30 minutes to code.
  • 15 minutes to discuss what you'd change to make it production-ready.

The format:

  • 30 minutes to code.
  • 15 minutes to discuss what you'd change to make it production-ready.

The format:

  • 30 minutes to code.
  • 15 minutes to discuss what you'd change to make it production-ready.
At scale: One set of guardrails, built once by your engineers. Agents scale it across machines, sites, and use cases.

Frequently asked questions.

Can I bring my own AI models and use cases?

UMH is the platform AI applications run on. Bring your own models, agents, and use cases. UMH gives them data, APIs, and guardrails.

What happens when an AI agent sends a bad command?

UMH validates every command before it reaches production. Every action is logged and traceable. Guardrails are built into the architecture, not added as an afterthought.

Does this replace our existing systems?

No. UMH connects to the machines and systems you already run. It shapes that data for AI consumption and provides the API layer in between. Nothing gets ripped out.

Where is our data hosted?

Your data stays in your infrastructure. UMH is a German company with an open source core and a zero lock-in model.

How long does deployment take?

First machine connection takes about 90 seconds through the management console. A production pilot with multiple machine types typically runs 4-6 weeks.

START WITH UMH

Bring AI agents into your production. On your terms.

Talk to our engineers about your setup. Or explore the platform yourself, fully open source.
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