When you want the strongest model doing the work.

Claude, Codex, Gemini. The best models in the world do not run on a machine in your office, they run in data centres built for them. You are not renting metal there, you are renting thinking, and only as much as you actually use. We build the system around it: your knowledge, your tools, your processes.

What speaks for it How setup works

What you get

What speaks for this route

Always a model that is up to date: Whatever is at the top today, you can use right away. And when something better arrives next month, we swap the model. Which one fits best depends on the task, on privacy, on cost and speed, not on a leaderboard. Hardware ages, a model change is a setting.
You pay for what you use: No purchase, no machine sitting on a shelf losing value. Light use costs little. What it adds up to in your case we work out roughly beforehand.
Reachable from anywhere: Team, branches, phone on the road, your website. A hosted system is reachable by default, without anyone opening up your firewall.
Simply grows with you: More people or more throughput changes nothing about the setup. With a machine in the office the same step would mean buying new.
Your own machine if you want one: If you would rather run an open model under your own control, you get a GPU server in Germany instead of an access key. Both belong in this branch.
Hybrid is possible: Sensitive data stays local with you, the strong model handles the rest. What goes where we agree beforehand.

When this is the right way

Three typical reasons

The task needs the best model

When it comes to language, judgement or code and a small model visibly cannot keep up. You see the difference in the result immediately.

Several people, several places

When it is not one person at one desk but a team across locations using the same system.

The system hangs off your website

Anything that has to be available to visitors or customers around the clock belongs on a machine that is always up.

How it goes

From conversation to a running system

01
Conversation
What should the system do, how many people use it, which data is involved. From that follows cloud, local or a mix of both.
02
Scoping
Which models, which provider, which connections. And if a machine of your own makes sense, which one. You get a concrete quote including running costs.
03
Build
We build the system around the model: your knowledge, your tools, your processes. Tested with real cases from your working day.
04
Operation and support
Monitoring, updates and backups run on our side. You get access and we stay reachable.

Examples

What already exists in this branch

Existing pages with more detail. They belong to this branch even though their address sits elsewhere.

Your own AI server

The version with a machine of your own: a server with graphics power in a German data centre, running an open model under your control.

FAQ

Frequently asked

Isn't that just ChatGPT then?

No. With a large provider you are a user on their platform, under their rules, their interface and their limits. Here the model is only the engine. Around it sits a system with your knowledge, your tools and your processes, and that one is yours.

Why is the dedicated AI server here and not under local systems?

Because we mean local literally: the machine stands with you and belongs to you. A server with real graphics power runs on rented hardware in a data centre. That is a good route, but it is hosted. Calling it local would be mislabelling, even when it stands in Germany.

What about data protection?

When a large model does the work, the text you send leaves your building. There is no arguing that away. Business agreements can settle that it is not used for training, and we tell you upfront which data is involved at all. If something fundamentally must not leave, the local branch is the more honest proposal.

What does running it cost?

With the large models you pay per use rather than a flat monthly fee. What that means for you depends on how much runs through. We work it out together before the start so there is no surprise at the end of the month.

Can I switch later?

Yes, in both directions. Many start with a strong model in the cloud and later move sensitive parts onto a machine on site. Or the other way round, when one workplace becomes a team. We build so that stays possible.

Not sure which fits you?

That is the most common question, and one conversation usually settles it. We will tell you honestly whether your case needs the strong model from the cloud, belongs on your own machine, or splits across both.