PRIVATE AI/ML · NVIDIA GPU

AI/ML Integration

We build private AI inside your perimeter: GPU infrastructure, private LLMs, RAG assistants on your documents, MLOps and governance, with a pilot in weeks, not quarters. Nothing leaves your network unless you decide it does.

Talk to an expert

VIXEN.UNO ENGINEERING TEAM

12 engineers holding current VCP and 4 holding VCAP

WHAT YOU GET

After the project you have:

✓An AI platform under your control: nothing goes to public services unless you explicitly enable it

✓Assistants working on your documents, respecting access rights

✓Transparent usage: a query log, data and permissions management

✓A team trained to run the platform and develop it further

TECHNOLOGY PARTNERS OF THE VIXEN.UNO ENGINEERING TEAM

WHAT WE SEE IN THE FIELD

Which of these sounds familiar?

✱︎

Shadow AI is already in the company

Employees already use public chatbots on their own: which work data goes there, and where it travels next, nobody knows.

◎

Some data cannot leave the company

Contracts, client databases, financial models. GDPR requires a legal basis and control over where the data goes; for the most sensitive categories it is faster to do it in-house.

≣

Pilots stall

The demo impressed everyone, and there is still no working tool. Months pass, the budget is spent, the result is zero.

⚑︎

No ML engineers

The board wants AI this year, and nobody in the team has built a working LLM platform yet.

THE SOLUTION

What the solution includes

Infrastructure for AI workloads

Selection and supply of GPU servers, fast storage and low-latency networking, from a single server to a cluster. A TCO calculation against cloud GPUs, before the purchase.

Private LLMs in your infrastructure

Deployment of open and commercial models on-premise: vLLM, Ollama, NVIDIA AI Enterprise. Data does not leave your network, access is controlled.

RAG platforms on your data

Assistants that answer based on your documents, policies and knowledge bases, citing the source and respecting each user’s access rights. Working tools for teams, not demos.

MLOps and process automation

A Kubernetes-based platform (KServe, Kubeflow), agent workflows and ITOps automation (n8n, Ansible): routine operations run under rules you control.

AI security and governance

Protection of models against prompt injection, data and permissions management, logging of queries and answers, so that security and legal see who accesses what, and how.

Pilot → measure → scale

We start with a pilot on one process with clear metrics. We measure the result at checkpoints, and scale only what has proved its value.

WHERE THE MODELS RUN

Three deployment options

OPTION	WHERE THE MODELS RUN	WHAT IT MEANS FOR YOUR DATA
On-premise	Your servers	Data never leaves your network
EU data centre	Tier-3, Lithuania	Data stays in the EU on dedicated hardware
Hybrid	Private models + public APIs (OpenAI, Anthropic)	Enabled only by your explicit decision; visible in the query log

Who delivers

One contract. Clear responsibilities.

You sign one contract and work with one project lead. Here is who is responsible for what.

Eurokommerz

AUSTRIA · EU

An Austrian company with 20+ years in the market. Holds the contract and supplies the hardware under the solution – with EU invoicing, delivery and warranty under European law. A single point of responsibility for the project.

Vixen.UNO – engineering partner

UKRAINE · INTERNATIONAL PROJECTS SINCE 2018

AI and infrastructure engineering: RAG platforms, MLOps on Kubernetes, private LLMs in production. 12 engineers hold current VCP and 4 hold VCAP.

Private LLMs · RAGMLOps on Kubernetes

EU hosting: Vixen.UNO’s data-centre partner is Baltneta (Lithuania, since 1996, part of Atea group) – its own Tier-3 data centres, ISO 27001, PCI DSS Level 1, EU data residency. balt.net ↗

More about Vixen.UNO ↗

How we work

Three transparent steps

01free of charge

Talk to an expert

We work through your process and data; you leave with 2–3 possible solution scenarios.

02paid stage

Technical assessment

Analysis of your data, processes and infrastructure: solution architecture, model and GPU selection, and a pilot plan with metrics. The price is fixed before work begins.

Pilot, implementation and support

A pilot in weeks; the scaling decision follows its results. Ongoing support under an agreed SLA.

HONESTLY

When we are not the right fit

✕If you need a simple chatbot for your website, that is not what we do.

✕If you need the result in two weeks, without a pilot or metrics.

FAQ

The key questions, answered

Where exactly do the models and data run?

On your hardware or in a Tier-3 data centre in Lithuania; you choose the option at the assessment stage. Nothing goes to public APIs until you yourself enable hybrid mode for a specific task. What exactly goes there is visible in the query log.

We have no AI team. Who will run all this?

We build and support the platform, and we train your team to work with it. After that it is your choice: your own staff or our support under an SLA.

We tried an AI pilot. It went nowhere. What is different here?

We start with the process and the metrics, not the technology: if the pilot does not show value, we do not scale it. And because the platform runs in your infrastructure, legal and security do not block the launch.

What about the EU AI Act?

A private platform simplifies the core of it: data stays under your control, and queries and answers are logged, so data governance and process documentation become technically possible. The legal compliance assessment is done by your legal department; we deliver the technical part.

How long does the pilot take?

A typical pilot is a few weeks: one process, clear metrics, an explicit success criterion. Based on its results you decide whether to go further.

How do you handle our data during the project?

Engineers work in your environment under your access controls; we do not take copies of production data out of it. NDA before technical detail, a data processing agreement on request, subprocessors named in the contract. The full picture (data access, the documents we sign and where hosting runs) is on the Security & Compliance page.

Who is the contract with, and who is responsible for the result?

The contract is with Eurokommerz (Austria). The engineering work is delivered by the team of our partner Vixen.UNO. A dedicated Eurokommerz project lead stays your single point of contact: questions do not move between contractors.

RELATED DEEP DIVE

From our engineering blog

GUIDE

How much VRAM an LLM really needs

Weights, KV cache, overhead and concurrent users: the formulas behind the GPU choice.

Read the article →

GUIDE

Private LLM vs cloud API: where the break-even is

A token-volume question, not a philosophy. The arithmetic on your own numbers.

Read the article →

GUIDE

RAG on company data: where it breaks

Almost every RAG failure is a retrieval failure. What to fix first and what to measure.

Read the article →

Let’s talk about your project

We reply within one business day