AI for data that can't leave the company
We build on-premise LLM deployments (Llama 3, Mistral, Qwen) and private RAG on your knowledge base. Your data stays with you — nothing flows to OpenAI, Microsoft or Anthropic. For law firms, clinics, finance and GDPR/NIS2-regulated companies.
Book an on-premise AI consultation
We'll check whether your data and processes fit a local LLM
Entreprises ayant choisi l'IA sans le cloud OpenAI
Déploiements où les données n'ont jamais quitté l'infrastructure du client
A local LLM with RAG over our case base sped up document drafting. Client files never left our server.
An assistant based on a local model helps doctors find protocols. Fully GDPR-compliant, no patient data leaves the perimeter.
We set up Open WebUI + Llama 3 on our own GPU. The whole team has AI without 50 ChatGPT Enterprise seats and no leak risk.
Why on-premise AI makes sense for regulated businesses
We combine hardware, open-source models and security deployments
Data never leaves your business
The whole stack (model, RAG, interface, logs) runs on your hardware or in your private cloud.
Compliant with EU and local law
We design deployments for GDPR art. 32, NIS2, financial regulators, the EU AI Act and sectoral guidance.
Open-source instead of vendor lock-in
Llama 3, Mistral, Qwen — you can always swap models, hardware vendor or deployment direction.
What we build in the on-premise variant
Each deployment fits your industry, company size and regulatory requirements.
Local LLM on a GPU server
Ollama or vLLM on your hardware (RTX 4090, A100, H100) or in a private cloud (Hetzner GPU, OVH, Azure Private).
RAG on your knowledge base
Document indexing (PDF, Word, case files, mail, intranet), local embeddings, on-prem vector database.
Web assistant for the team
Open WebUI or a custom frontend with SSO, access control, conversation history and multi-language support.
Integrations with your systems
API to CRM, helpdesk, ERP, tickets — AI with controlled access to real company data.
What you get after deployment
A working local LLM
Configured server, model, RAG and interface ready for the team — with admin documentation.
GDPR audit and AI policy
Compliance document, access policy, log retention, incident response plan.
Training and support
Team workshops (how to write prompts), admin guides, SLA on infrastructure maintenance.
How we deploy local AI
Data and requirements audit
We check what data, hardware, regulations and processes will truly benefit from AI.
Model and architecture choice
We pick the LLM (Llama 3 8B/70B, Mistral, Qwen), GPU hardware, vector database and interface.
Pilot and production
We launch a pilot on 1-2 processes, measure quality and security, then scale to the whole company.
Frequent questions about local AI
What hardware does a local LLM need?
For 7-8B models, an RTX 4090 (24GB VRAM) and a server in the €3.5-6k range is enough. For 70B models you need A100/H100 or a cluster. We'll advise the right setup for your team and knowledge base.
Is a local LLM as good as ChatGPT?
Llama 3 70B reaches GPT-4-level quality on many business tasks (summarization, classification, text generation, RAG). Llama 3 8B is lower but sufficient for many internal processes.
Is this legal under GDPR and NIS2?
Yes — in fact, it's recommended. Local AI is the safest option under GDPR art. 32 (security of processing) and NIS2 (supplier control). We deliver a compliance audit alongside the deployment.