AI on your own premises: local systems, hybrid operation and a benefit you can actually calculate

In most companies, AI is already in use — just not where anyone decided it should be. Staff paste quotes, contracts and customer data into public chat services because it makes their work noticeably easier. Ban it, and you lose the benefit. Let it run, and you lose control of your data.

There is a third option, and this article is about it: AI systems we set up and run for your company — in our own data centre or on your premises, optionally connected to established providers such as Anthropic or OpenAI. You decide which data may leave your building and which may not.

Three designs, and what each one is for

There is no single right answer. There are three, and which one fits depends on your data, not on the technology.

• Cloud AI: you use the large providers’ models through their interfaces. Quickly available, nothing to operate, always current — but your input leaves the building.
• Local AI: language models run on hardware you control — in your server room or in our data centre. Your data stays where it is. The price is an operation that must be planned and maintained.
• Hybrid: the common case in practice. Confidential work runs locally, everything else through the cloud. A rule set decides per request which path is taken — rules you define.

The third option is usually the most economical: you pay for the more demanding local processing only where it is actually needed, and use the speed and breadth of the large providers for the rest.

When local is the right choice — and when it is not

Local AI is not an end in itself. It pays off if at least one of these applies:

• You process personal data, health data or trade secrets that may not leave your premises, by contract or by law.
• You operate critical infrastructure or fall under NIS2 and must demonstrate where data is processed.
• Your customers contractually require processing on your own premises or in a specific country.
• You want AI to work on your own records — quotes, project files, maintenance reports — without handing those records to a third party.

If none of that applies, the cloud route will usually get you there faster. We will tell you so. A local system nobody needs is expensive standstill.

What we provide

We set the systems up and run them — tailored to your company, not as an off-the-shelf package. That includes:

• Selecting and sizing the environment based on your actual use cases rather than a datasheet
• Operation in our own data centre or on your premises — you decide where the data sits
• Connection to your records: file storage, knowledge bases, line-of-business applications
• On request, a connection to providers such as Anthropic or OpenAI, with rules for which request takes which path
• Access rights that follow your existing permission structure — whoever may not see a file will not get it from the AI either
• Logging that shows who asked what and when — the basis of any evidence
• Operation, updates and monitoring while the system is live

We are certified to ISO 27001, advise companies in the critical-infrastructure sector and support the implementation of NIS2 requirements. As a Microsoft Solution Partner with the Modern Work designation, we know the environment most of these systems ultimately have to work in.

The part most people skip: making the benefit measurable

A great deal is claimed about the benefit of AI and very little is measured. So we will not quote you a percentage from a study — we set up the measurement in your company, on your processes, with your numbers. That is the only way to get a statement that holds up in a board meeting.

Step 1: record the starting point before anything runs

This is the step most often missing — and without it every later figure is worthless. Together we pick two or three processes that occur often and can be measured, and record for each:

• How often does the process occur per month?
• How long does it take today on average, start to finish?
• How many people are involved, and at what cost level?
• How often does rework happen, and what does one round of rework cost?
• How long does the customer or the next department wait for the result?

These five figures are the baseline. They are recorded once and then left alone — otherwise you end up comparing two moving targets.

Step 2: start small, on a real process

No company-wide rollout. One process, one department, a manageable period. That keeps the effort small if it turns out the idea does not carry — and that happens. A trial that produces a result is a success even when the result is “not worth it”.

Step 3: after eight weeks, record the same five figures

Same processes, same method, same people. The difference is your result — and against it stands the effort: hardware or operating cost, setup, training, ongoing maintenance. What remains is the value. Calculated, not estimated.

One caveat we would rather state up front: time saved is not yet money. It becomes a result only when the time gained is actually used for something else. That question belongs in the same calculation — otherwise you end up with a figure nobody can find in the accounts.

Where it tends to pay off first

The use cases that show something quickly share three properties: they occur often, they follow a pattern, and their result can be checked.

• Making knowledge in your own records accessible: a question gets an answer with its source — instead of a search through folders.
• Preparing recurring documents: quotes, reports, minutes — the draft is produced, the review stays with a person.
• Pre-sorting incoming enquiries and linking them to the right case.
• Making technical documentation and maintenance reports searchable, even after years of growth.

What we do not recommend: starting with the hardest case because it promises the most. The first attempt should show something, not solve everything.

What getting started looks like

We begin with a conversation, not a quote. It settles three things: which data may not leave the building, which processes cost the most time today, and what already exists — in technology, licences and prior work. From that it follows whether local, hybrid or cloud is the right route, and in which order to proceed.

We look after environments of up to 50,000 users and work within roughly 100 kilometres of Singen — from Constance and Villingen into Switzerland. Distance does not matter for operations; for the preparatory work and the handover it helps if someone can come by.

If you want to know whether this adds up for your company, get in touch. We will also tell you when it does not — that saves both sides time. More on our approach under AI & Automation and in our LLM strategy.