Information systems and digitisation

AI on your own infrastructure

You want to use language models and AI tools, but the documents must not leave the organisation? We will deploy artificial intelligence in your own infrastructure: we will design the hardware, deploy local models and we provide operation long term — as part of the IT infrastructure we already manage.

For organisations that cannot or do not want to send data into third-party clouds — from libraries to industry.

Why on-premise models

Public AI services mean that your documents and data go to a third party. A local language model runs on your own servers — in a private environment that we design and operate. For organisations with sensitive data, for public administration and for industry this is often the only acceptable route to AI.

What we deal with

DEPLOYMENT · FROM USE CASE TO OPERATION

  1. Assessment of the use case and data
  2. Environment design (compute, GPU)
  3. Deployment of an on-premise model
  4. RAG over your documents
  5. Operation and security
A developer workstation working with an on-premise language model
Developer workstation — working with a local language model.

Frequently asked questions

What hardware do we need for an on-premise model?
It depends on the model and on the number of concurrent users — the design of the environment (compute, GPU) is therefore a separate step before deployment. We can design and supply the hardware and build the model on infrastructure we already manage.
Do our documents go anywhere outside?
No. The model runs on your servers, in a private environment. That is the whole point of an on-premise deployment — the data does not go anywhere, unlike with public AI services.
What is RAG and why do we need it?
RAG (retrieval-augmented generation) means that the model answers from your documents: it finds the relevant passages and builds the answer on them. There is no need to retrain the model for this, and you can see what the answer was based on.
Can a local model handle Slovak?
Quality differs between models and is developing fast — that is why the use case assessment also includes a test on your data. Only then is a specific model chosen, not the other way round.
Who operates it after deployment?
Operation and security are the last step of a deployment and we can take them over — as part of our infrastructure services, with monitoring and support under the P1–P4 priority model.

Related services

Virtualisation and infrastructureThe environment in which local models run. Identity protectionAccess security for AI and data. Applied research and AIThe research direction the offering comes from.

AI on local models · Consultation

Want AI without data leaks?

Describe your use case to us — we will assess whether and how an on-premise model makes sense.

Discuss an AI use case

E-mail: qintec@qintec.sk · Phone: +421 2 3240 1217 (working days 9:00 – 15:30)