// Service
Private, on-premise AI agents and local LLMs
Open-weight language models and AI agents deployed on your own hardware or private environment, so your documents and data stay under your control.
AI that runs where your data already is
Cloud AI services are convenient, but every prompt and document you send leaves your company. For contracts, customer data, source code or regulated information, that is often not acceptable. We deploy open-weight language models and AI agents on your own hardware or in your private environment, so your data stays under your control.
What we build
- Local LLMs: open-weight models such as Llama, Qwen, Mistral or Gemma, served on your own server or GPU workstation.
- Private document search (RAG): ask questions about your manuals, contracts and internal documents and get answers with references, without uploading them to anyone.
- AI agents: assistants that do real work in your systems, such as preparing reports, checking data or drafting replies, with clear permissions, logging and a human approval step where it matters.
- Integration: connecting the AI to your databases, file shares and business applications through controlled interfaces.
Why local, on-premise AI
- Privacy and GDPR: personal and confidential data does not leave your infrastructure.
- Predictable cost: no per-token bills that grow with usage; you pay for hardware and setup.
- Independence: no dependency on one AI vendor's pricing, terms or availability; it can even run fully offline.
How we start
- Use-case review: pick one task where AI saves real time and where the data is sensitive.
- Pilot: a working setup on your hardware or a test server, measured against criteria you set.
- Production: hardening, access control, logging, backups and documentation, so your team owns the result.
Frequently asked questions
Can AI agents run without sending our data to the cloud?
Yes. With open-weight models running on your own hardware, prompts, documents and answers stay inside your network. The system can even run without an internet connection.
What hardware do we need for a local LLM?
It depends on the model size and how many people use it. Smaller models run on a single workstation with a modern GPU; larger models and more users need a dedicated GPU server. We size it with you during the use-case review.
Are local models as good as ChatGPT or Claude?
For many focused business tasks, such as answering questions about your own documents, extracting data or drafting text, current open-weight models do the job well. For the hardest general tasks, the largest cloud models are still ahead. We help you choose honestly, task by task.
Is on-premise AI GDPR compliant?
Keeping data on your own infrastructure makes GDPR compliance much easier, because no personal data is transferred to an outside AI provider. You still need the usual measures, such as access control, logging and a clear purpose, which we build in.