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AI ENGINE · the large language models

The model can become yours.

It starts with the best models on the market and reaches, if needed, a private model inside your own infrastructure. The knowledge stays in the Brains: only where the model runs changes.

Three options

One question only: where the data goes.

  1. Standard

    Frontier models

    The best models on the market, through APIs. No infrastructure to run, continuously updated. Configurable per type of operation.

    Default
  2. Alternative

    Open source models

    Hosted on European infrastructure, with inference compliant with GDPR and the AI Act. More privacy from the base model, without fine-tuning.

    Self-hosted
The comparison

Three options, criterion by criterion.

What changes is where the data goes, the infrastructure, and when each one makes sense. The three side by side.

Frontierdefault
Open sourceself-hosted
Dedicated modelfine-tuning
Where the data goes
Sent through APIs to the model providers
Stays in a European inference service
Never leaves: it runs on dedicated infrastructure
Infrastructure
None: managed by the providers
Managed inference service in the EU
Dedicated server, switched on when needed
Compliance
Under the providers' terms
GDPR and AI Act by design
Highest: your model, no external cloud
Cost
Pay per use through APIs, no fixed cost
Pay per use on managed inference
On-demand infrastructure plus the setup path
When to choose it
The large majority of cases
More privacy is needed from the base model
Years of history, and regulated sectors

No vendor lock-in: the choice of model stays yours, and it stays reversible.

Dedicated model · when it makes sense

When AI can really become yours.

With years of structured history the question changes: not “which model do you use” but “can I have one of my own”. The result answers in the language and the logic of your company.

Dedicated server

The model is hosted on a server dedicated to one customer, switched on when it is needed and off when the team is not working. No fixed cost for idle hours.

Not a self-service process

Selecting and organising the data needs support. We accompany the path from mapping the document estate through to validating the results.

Vertical fine-tuning only pays off with enough data, of good quality. The threshold matters: years of structured material, not weeks.

Do you have years of historical data?

We look together at whether vertical fine-tuning fits your case, from mapping the document estate through to activating the model.