DioxDigital services

Local AI implementation and consulting, for companies that want to use AI without sending their data out

I work with businesses that see the value of AI but do not want to upload contracts, client records or internal documents into an external service. The answer is to run the model at your place, on your own infrastructure.

AI running at your place, not in someone else's cloud We assess what is worth automating, pick the right model, install it on your infrastructure, and someone stays to show your team how to use it.
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Implementation

The model runs on your server and the data stays in the company

A complete local setup, from choosing the model to the moment your team can use it daily. This is not an experiment left in your hands after the first demo, but a system that keeps working and that you know how to administer.

  • Choosing the model that fits your task, not the largest one available
  • Installation on your own server, a dedicated workstation, or infrastructure you already run
  • Connecting the model to your internal documents and procedures
  • A working interface for the team, with no technical knowledge required

A good fit when

You handle sensitive datacontracts, case files, client records that cannot leave the company
Private
Per-request cost becomes a problemyou use AI often and the subscriptions start adding up
Fixed cost
You want control over the setupwithout price or model changes imposed from outside
Control
Consulting

Before implementation, we establish what is actually worth automating

Most AI projects fail because they start from the tool rather than the problem. The consulting part means looking first at the repetitive work in your company and seeing where a model genuinely saves time, and where it would only be another expense.

  • Identifying the repetitive tasks that consume the most time
  • A realistic estimate of what a local model can and cannot do
  • A comparison between running locally and cloud services, with real costs
  • A staged plan, so you start from one small clear case rather than everything at once

The real benefit

You leave with a clear direction and correct expectations. You know what can be solved now, what is worth postponing, and what each option costs, before investing in hardware or licences.

No hypeI will also tell you when AI is not the right answer
Predictable costsA starting cost, not a bill that grows with usage
How it works

A process in small steps, so you see results before investing heavily

1. Discussion and assessment

We look at the workflows in your company and pick one or two concrete starting cases with a measurable result.

2. Test setup

We install the model and test it on your real data, so you can judge the answers before any purchase.

3. Putting it to work

The final setup, the interface for your team, and the explanations people need to use it without me.

Want to find out whether local AI makes sense for your company?

We start from what you do manually today and work out together whether it is worth automating, without pushing you toward a bigger solution than you need.

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Frequently asked

What companies want to know before the first step

It means the model runs on a server or workstation inside your company, not on an external provider servers. The documents, messages and data you process never leave your infrastructure, and the cost no longer depends on the number of requests.

Not necessarily. Many useful cases in smaller companies run on a workstation with a consumer graphics card, or even on a recent Mac. The first step is deciding what you want to solve, and from there it becomes clear which model you need and what hardware it requires.

Yes. This is one of the most requested uses: the model answers based on your own procedures, contracts or materials, rather than on general information from the internet. It works through an index built over your documents, kept locally as well.

The setup is designed so the model can be swapped without rewriting everything. The integration layer stays in place, and the model behind it can be replaced when a more suitable or more efficient one appears.