Private AI Platform

We build and operate a private AI platform directly inside your infrastructure — on Linux and open source technologies. The AI models run locally, so the documents, queries and data being processed never have to leave your data centre.

This service is for companies that:

• want to use AI but cannot or do not want to send company data to public services,
• operate in a regulated environment — finance, healthcare, public administration or critical infrastructure — and have to demonstrate compliance with the GDPR, the AI Act, NIS2 or DORA,
• are looking for practical AI use over their own documentation, tickets, contracts or source code,
• need AI available to internal applications through a single interface,
• want to start small and decide based on real experience rather than presentations,
• are looking for a partner who will not only build the AI environment but also operate it long term.

What you will actually use AI for

The platform is not just an internal replacement for public AI chats. It is the foundation on which specific AI services working with your data and your systems are gradually built.

Knowledge and documents

AI answers questions over your own documentation and can add a link to the source document.

• answers from the internal wiki, policies, manuals and operational documentation,
• summarisation of long documents, reports, e-mails and meeting notes,
• work with contracts — comparing versions, finding specific provisions, extracting details,
• extraction of structured data from invoices, orders and forms for other systems,
• search by meaning rather than by an exact keyword,
• HR and internal policies — onboarding, processes, benefits, internal contacts.

IT operations and support

AI as an analytical layer on top of what you already have — monitoring, logs, tickets, documentation.

• helpdesk support — ticket categorisation and summarisation, finding similar past cases, drafting a reply for approval,
• explanation of logs and error states, looking for the probable cause of an incident,
• an incident summary built from several data sources into one understandable timeline,
• an assistant for administrators and DevOps — configuration, questions about the environment, generating documentation.

Development and internal applications

Local operation makes it possible to work with private repositories and sensitive data as well.

• work with source code — explanation, code review, tests, refactoring, documentation,
• summarising changes, preparing release notes and analysing CI/CD failures,
• a single AI interface for your applications — ERP, CRM, helpdesk, web and in-house systems,
• classification of requests, e-mails and documents, translations, generating and editing texts.

Where to start

We do not recommend introducing everything at once. The proven approach is to start with one specific problem and extend the platform based on the experience gained.

• Private AI chat — an internal alternative to public AI chats. The fastest deployment with immediate value for all users.
• AI over your documentation — answers from your wiki, policies and manuals, including links to the source. The highest practical benefit.
• An AI interface for your applications — a central private AI service used by your internal systems.

Connecting to operational systems — monitoring, helpdesk, repositories — is the natural next phase, once the environment has a proven and secure foundation.

What we deliver and operate

• sizing of the environment and selection of suitable models according to your use, licence terms and language requirements,
• delivery and installation of the necessary hardware in your data centre, or operation on dedicated rented physical or virtual GPU infrastructure,
• installation of the platform — local models, a web interface for users and a single OpenAI-compatible API for applications,
• an optional initial connection to your documents and internal systems, including the basic setup — you then add further documents and sources yourself directly in the web interface, and you drive the further development of the platform on your own or together with an AI integrator,
• basic user training and consultations on practical use,
• continuous 24×7 monitoring, updates, incident handling and SLA support,
• scaling from a smaller proof-of-concept environment up to a highly available platform embedded in company processes.

What the platform looks like

A central AI service is created inside your infrastructure, available to users and applications alike. People work through the web interface, internal systems through a standard API. The AI gateway routes requests to local — or optionally cloud — models. Documents can be connected through RAG and a vector database.

Private or hybrid operation

We prefer the fully private mode — the whole platform and the models run at your site and no AI data has to leave your infrastructure. Where it makes sense for particular tasks, we can add cloud models alongside the local ones. The platform does not, however, depend on an external provider to work.

How the cooperation works

1. We discuss your requirements and the opportunities where AI brings value first.
2. We propose the size of the platform, suitable models and the first area of use.
3. We deliver and install the environment and bring the interface and API into service.
4. We train the users and connect the first data sources.
5. We operate the platform long term, monitor it and develop it based on your experience.

What we pay attention to

• Neither the models nor the data services are exposed to the internet — access leads only through a secured interface.
• Access to documents respects the permissions of the individual user.
• Integration with operational systems starts as read-only. AI analyses and recommends, but does not change the production environment by itself; any write operations are handled separately and with explicit approval.
• For contracts, legal matters and other critical areas, the AI output is supporting material to be verified by a human.
• We assess models from the perspective of their licence and permitted commercial use, and we pin fixed versions for production.
• Operating inside your own infrastructure makes it easier to document where and how data is processed. Assessing your specific regulatory obligations always remains on your side — we are happy to supply the technical evidence for it.

How this builds on our other services

We operate the platform in the same regime as other critical systems — it builds on managed operations and can also run on top of the Kubernetes platform. If you are not sure which first step makes sense for you, start with a consultation.