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Service

Local and On-Premise AI

Deploy artificial intelligence on your own infrastructure: confidentiality, data control and independence from external services.

Local LLMsSelf-hostingConfidentialitySovereigntyGPU infrastructureLocal RAG
Describe my needContact us
The problem

When data cannot go to the cloud

Some organisations cannot — or do not want to — entrust their data to an external AI service:

  • confidentiality: medical, legal, strategic data;
  • data control: knowing exactly where it is processed and stored;
  • sovereignty: independence from foreign actors;
  • infrastructure control: internal security or continuity requirements;
  • specific environments: isolated sites, limited connections.

For these companies, the question is not "AI or no AI", but "how to benefit from AI without giving up data control".

Simple explanation

What is local AI, simply

Local AI runs on the client's infrastructure — company servers or a chosen datacentre — rather than fully depending on an external AI service.

Concretely:

  • a local LLM is a language model running on your machines;
  • documents stay with you: they are never sent to a third party;
  • local RAG allows this AI to answer from your internal documents, without data leaving;
  • the GPU infrastructure is sized to run these models with acceptable performance.

The choice between local AI and cloud AI is not ideological: it is a question of balance between confidentiality, cost and need (see our article *Local AI vs Cloud AI: How to Choose?*).

What Technea delivers

AI deployments on your infrastructure

Technea studies and sets up:

  • local LLMs: model choice, installation, configuration;
  • self-hosting: AI hosted on your servers, under your control;
  • local RAG: the document assistant that answers without sending your documents elsewhere;
  • infrastructure sizing: which compute needs, which machines.

The Lab's work on local AI and self-hosting directly feeds these choices: which models, which performance, which limits.

Use cases

Concrete examples

  • Law firm or legal department: analysis of sensitive documents without data leaving.
  • Industry: assistance to teams on a site with limited connections.
  • Public organisation: data sovereignty requirements.
  • Cautious company: starting with local AI before evaluating external services.
The method

How local AI is decided and set up

1. Need analysis: which use cases, which data, which constraints. 2. Feasibility study: which models can answer, on which infrastructure. 3. Sizing: compute, memory and storage needs. 4. Deployment: installation and configuration on your infrastructure. 5. Evaluation: answer quality, performance, operating costs.

The benefits

What it changes

  • data stays with you: no departure to a third party;
  • complete control: models, versions, updates, access;
  • independence: no dependency on an AI service provider;
  • controlled costs: no per-usage billing;
  • continuity: AI works even without an external connection.
FAQ

Frequently asked questions

Local models are progressing quickly and cover many needs: document search, assistance, analysis. Very large cloud models remain better on some cases. The right choice depends on the need.
It depends on the chosen model and the usage volume. The feasibility study precisely sizes the required infrastructure, including GPU needs.
The initial investment (hardware) is higher, but usage costs are controlled: no per-request billing. Over time, the balance depends on volumes.
Yes. Documents are indexed and searched on your infrastructure: the document assistant answers without any data leaving.
An architecture can combine both: local AI for sensitive data, an external service for the rest. It is a question of architecture, not dogma.
Local AI is much more than software: a language model running on your machines, with all the compute and storage infrastructure that goes with it.

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Business cybersecurity: defence-in-depth architecture, data protection, access control and continuity for small and medium businesses.

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Passer à l'action

Your data must stay with you?

Let's study whether local AI answers your need and your constraints.

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