Private AI and local LLMs for businesses in Corsica
Private AI means running an artificial intelligence model on a server on your premises, or with a host you control, without sending your data to the big online services. In Bastia, we design local LLMs for businesses and organisations that handle sensitive information.
Private AI, local LLMs, on-premise AI: what do they mean?
When you use a consumer AI assistant, your text travels to the provider's servers, sometimes outside the European Union. There an LLM, a large language model that can understand and write text, processes it and sends back the answer. That is cloud AI.
Local AI works differently. An open model, whose files can be downloaded and used under the terms of its licence, is installed on a machine you control. This is also called on-premise AI, or AI without the cloud: questions, documents and answers never leave your network.
Sovereign AI is a broader idea: AI whose infrastructure, data and dependencies you control, whether that is a machine on your premises or hosting in Europe, with models nobody can withdraw from you overnight.
Who on-premise AI makes sense for
- 01
Healthcare
Medical and allied health practices, care facilities: health data is subject to strict rules. A local AI can help sort post or draft administrative documents without sending that information to an outside service.
- 02
Professional practices
Solicitors, accountants, notaries: professional confidentiality sits uneasily with tools whose servers you do not control. A private LLM lets you summarise, search and draft on client files discreetly.
- 03
Local authorities and public bodies
Residents' files, council decisions, internal documents: sovereign AI helps keep public data fully under control.
- 04
Construction, industry and engineering firms
Plans, know-how, tender responses: strategic information that is best kept in-house.
- 05
Remote sites
A local AI keeps working when the internet connection is slow or down, an asset for an organisation based inland in Haute-Corse, for instance.
Local AI or cloud AI: making an informed choice
Local AI is not always the right answer, and we will tell you so. The most powerful models are only available online. Open models running on a business server are very capable at summarising, sorting, extracting or answering from your documents, but generally lag behind on the most complex reasoning.
- 01
Confidentiality
Local wins: nothing leaves your premises.
- 02
Raw quality
Cloud wins on the most demanding tasks; the gap is often small on everyday tasks, and worth checking on your real cases.
- 03
Costs
Online, you pay per use; locally, the bulk is a hardware investment, followed by electricity and maintenance. The right choice depends on your volumes.
- 04
Mixed approach
Sensitive data is processed locally, tasks with no confidentiality concerns by an online service. This is often the best compromise.
The AI server: the hardware behind a private LLM
Running a language model needs a suitable machine, in particular one or more graphics cards (GPUs) with enough memory: they do the model's calculations. The size of model you can use, how fast it answers and how many people can use it at once all depend on them.
We size and install dedicated AI servers on your premises, then look after their maintenance. A single server can host several uses: an internal assistant, document search, automation of repetitive tasks.
How we deploy your local AI
- 01
Understand
Target uses, data involved, number of users, constraints of your premises and network, budget and timeline.
- 02
Design
Choice of open model, server sizing, architecture and interfaces. We show you mock-ups and, where possible, trials on your own examples before you sign off.
- 03
Build
Installation, configuration, security hardening, tool development, then quality and speed tests on your real cases. You follow progress throughout.
- 04
Launch and support
Go-live, hands-on handover to your team, hardware and software maintenance, model updates when a better version comes out.
Security, backups and quality of a private LLM
- 01
Local does not mean secure
The server must be protected like any sensitive equipment: restricted access, updates, encryption, network segmentation.
- 02
Backups
Document indexes, configurations, histories: anything of value must be backed up, ideally off site.
- 03
Answer quality
A smaller model must be assessed on your real tasks before it is adopted; we measure its quality with you rather than relying on promises.
- 04
Licences
Every open model has its own licence, sometimes with restrictions on commercial use. We check them before any choice is made.
- 05
GDPR
Local AI makes data easier to control but does not remove GDPR obligations: purpose, retention period, individuals' rights.
What shapes the budget for on-premise AI
- 01
Target model size
The larger the model, the more memory and computing power it needs, and so the more substantial the hardware.
- 02
Number of users
Serving one person at a time or a whole team at once does not call for the same machine.
- 03
Uses to build
Assistant, document search, automations: each tool built on top of the model is a piece of work in its own right.
- 04
Environment and maintenance
Location, power supply, ventilation and network, then hardware upkeep and model updates over time.
Technologies and methods
- Open models
- Private LLM
- GPU server
- RAG
- On-premise hosting
- Linux
Frequently asked questions
Is a local AI as good as ChatGPT?
Not in every respect. Open models are very effective at summarising, sorting, extracting or answering from your documents, but the most advanced models from the big providers remain ahead on complex reasoning. We test on your real cases before recommending a solution.
Do we need a server room for a local AI?
Not necessarily. Depending on the power required, the machine can be a workstation or a server. It does need a reliable power supply, good ventilation and a place on your network. We check these points on site.
Does a local AI work without the internet?
Yes. Once installed, it runs entirely on your network. A connection is still useful for updates and remote maintenance, which you can restrict as you see fit.
Is local AI enough to comply with GDPR?
It helps a great deal, since data stays with you, but compliance also depends on what you process, how long you keep it and who has access. We address these questions from the design stage.
Can we start in the cloud and move to local later?
Yes, provided the application is designed to switch models, which we always plan for. You can start with an online service for less sensitive data, then move to a local server.