AI that belongs to your company.
We advise you on where AI genuinely helps in your processes, build your own AI environment on-premise or in a private cloud, and then run it for you. Your data stays where it belongs: with you.
From the idea to day-to-day operations.
AI is not a product you buy off the shelf, it is infrastructure that has to fit your processes. We cover all three steps.
Your own AI infrastructure
We plan and build your AI environment: GPU servers on your premises or dedicated hardware in our data centre in Frankfurt am Main, with open language models, a web interface for your team and connections to your business applications.
Consulting on use cases
We look at how you actually work and tell you honestly where AI saves time and where it only creates effort. You get prioritised use cases with effort, benefit and risk, including the data protection side.
Administration & optimisation
We keep models, components and servers up to date, monitor load and response times, and sharpen the results: better prompts, cleaner data, more suitable models. AI only gets really good once it is in use.
Your data trains nobody but you.
The fastest route to AI runs through a public service. The safest one runs through an environment you own.
Quotes, contracts, customer histories, HR records: the moment content like that lands in someone else's service, you hand over control, and the questions of processing agreements, storage location and reuse arrive immediately. If the model runs on your hardware or on dedicated servers in our data centre instead, every request stays inside your environment.
That does not make public services off limits. For uncritical tasks they are often the cheapest option. We help you draw the line rather than ignore it.
What that means in practice
A private AI environment is not an end in itself. It is what makes it permissible to feed the AI real company data in the first place.
- No sharing with third parties, no training on your content
- Operated in Germany, GDPR-compliant and with a DPA
- Access controlled through roles and permissions
- Predictable cost instead of billing per request
Benefit first, technology second.
We do not start with the question of which model is the biggest, but with the question of which work you want AI to take off your hands.
Together we collect the tasks that cost a lot of time and keep repeating, then rate them by effort, benefit and data protection risk. The result is a prioritised list, not a slide deck.
We pick models and hardware to match the use case and decide with you what runs on-premise and what belongs in a private cloud. You get a clear cost comparison for both routes.
We set up the environment, connect your documents and systems, dCM for example, and bring it to your staff with roles, permissions and an interface they can actually use.
Updates, monitoring and backups are on us. And we review regularly whether the answers can get better, through cleaner data, different models or adjusted processes.
Where AI really carries its weight.
Not future music, but tasks that work reliably today.
Finding knowledge instead of searching
Manuals, contracts, minutes, project folders: your staff ask in plain language and get the answer with its source, instead of clicking through folder structures.
Correspondence & drafts
Quote texts, answers to recurring enquiries, minutes and summaries: pre-written in your company's tone of voice, while people still do the approving.
Analysing & classifying data
Categorising enquiries, reading documents and receipts, naming anomalies in the figures: AI does the groundwork so your people can decide instead of sort.
We run this ourselves.
We do not recommend AI infrastructure we have not used. Our own models run on our own hardware, with the same interface our customers get.
That includes the uncomfortable experience: which model size still answers smoothly on which hardware, where answers fall apart when the underlying data is messy, and how much care an environment like this actually needs. That is exactly the learning curve you skip by starting with us.
And because we develop software and run infrastructure, our responsibility does not stop at the server: we connect AI to the places where work actually happens, right into your business applications.
One partner for all of it
"Model, server, integration and operations from one place. When something goes wrong there is one person to call, not three suppliers pointing at each other."
Good to know.
Why run your own AI instead of using ChatGPT or Copilot?
What hardware do we need?
Does the AI have to sit on our own premises?
Can the AI access our own data and systems?
Where is the best place to start?
What does it cost to run?
Let's talk about your first use case.
Whether you already have a concrete idea or still want to work out what is possible at all: we look at your processes and tell you honestly what is worth doing and what is not.