For many companies today, the question is no longer whether to use AI. It is where the company’s data goes in the process, what the bill will be at the end of the month, and whether anyone can trace afterwards who used it for what. With on-prem AI, the artificial intelligence runs on the company’s own infrastructure and works according to the company’s own rules.
Why is this worth thinking about now?
In many places, AI is no longer an experiment but part of everyday work. With that come questions nobody thought of at the first rollout. These are the three we see most often.
It is hard to predict the cost. The monthly bill does not always show what added up to what. On top of that, it is no longer only colleagues but also AI agents that use the capacity, which makes consumption even harder to forecast.
The company depends on a single provider. An external provider can change its prices, its terms, or even whether the service is available, at any time. If daily work is built on it, that is a serious business risk.
It is unclear what leaves the company. With public AI tools, it is hard to say afterwards what company data ended up in an external system. Meanwhile, the EU AI Act, NIS2 and DORA increasingly expect transparent, verifiable and documented operations from the organizations concerned.
A turnkey solution in the company’s own environment
Grepton’s on-prem AI solution is not a single product but a complete, working and operated system. It covers everything from the hardware and the user interface to access management and the backup device.
- Local AI infrastructure: from smaller, entry-level devices to server-integrated solutions, sized to the number of users and the planned usage.
- Smart router:decides request by request what stays in the company and what may go to an external large language model.
- Familiar chat interface and company login: colleagues work in a chat interface they already know and sign in with their existing company accounts, so usage can be measured and logged per user.
- Connection to existing systems: the solution connects to document stores and business applications, and permissions stay exactly as they are.
- Everything from one partner: hardware, rollout, operations and a backup device from a single partner.
The six building blocks of on-prem AI
You define the router’s rules, including which data must never leave the company. With a strict setting, everything stays in the company. With a more flexible setting, complex but non-sensitive requests can also go to an external, larger model, but only in a regulated way and with logging. You can see how the router decides in an example.
Where does a request go?
Pick an example!
Illustrative example. The actual rules are defined together.
What does your company gain?
Sensitive data stays in the company. Tasks that run locally are processed by the company’s own infrastructure, so personal data and trade secrets do not unnecessarily reach a third party. The company also depends less on any single external AI provider.
You can see and trace what is happening. The system keeps a log of who used what and which requests went to an external provider. For the organizations concerned, this helps with the documentation expected under NIS2, DORA and the EU AI Act. Important: we do not promise that this makes a company automatically compliant. The solution provides the technical foundation on which provable, verifiable operations can be built. It supports the company’s own compliance processes but does not replace them.
More predictable costs. The cost of local capacity is known in advance, so you do not find out at the end of the month what the AI cost.
Adaptable to the company’s own way of working. The solution can also be fitted to the company’s documents, processes and tasks.
Who should be thinking about this?
Companies where AI is already part of everyday work, and where the value of data, business risk or regulation makes it matter where and how that data is processed. Typical examples are financial institutions and insurers, healthcare providers, the public sector, manufacturing companies and software development teams.
Each decision-maker has a different priority:
- IT lead: that the system is easy to operate and fits smoothly into the existing environment.
- Information security lead:that they know exactly where data goes, who can access it, and what can be logged.
- Development lead: that the team can work efficiently and the source code stays protected.
- Finance lead: that costs are predictable and the investment pays off.
Why Grepton?
Downloading a language model is easy. Building an enterprise solution that works securely and reliably is a system integration task.
- Integrator mindset: the solution fits into the company’s existing identity, permission and operations environment, rather than running as a separate island.
- Our own experience: we used it in our own live, internal processes first, not just in demo environments.
- Open-license software layer: the model can be replaced later, and there is no hidden model licence cost behind it.
- Everything with one team: alongside AI, the server environment, security, Microsoft and Linux systems and cloud integration are handled by the same team.
Five steps to your first measurable result
Click the steps!
Let’s talk about what your company could gain from its own AI!
In a short online conversation, we’ll go through which tasks could benefit most from AI running on your own infrastructure, and what a rollout would look like at your company.
- We pick the 2–3 tasks where AI can deliver the most business value.
- We estimate the capacity you need, based on the number of users and planned usage.
- We discuss what logging and auditability your regulatory environment requires.
Book a free consultation! Find the full solution, further details and frequently asked questions here: https://grepton.hu/en/on-prem-ai-on-your-own-infrastructure-grepton/
