01
Diagnosis and requirements
We analyze which models you need, what data you handle and what your data protection regulations require. We define whether the local AI server runs on your hardware (a local GPU) or in a dedicated private cloud.
Experts in local AI servers
Your local AI server to run language models (local LLMs) and RAG agents inside your own infrastructure. Your private AI: your data never leaves your network, with maximum privacy and compliance with data protection regulations.
Local AI server specialists
Running your own server for AI is essential to guarantee maximum privacy for your data and to comply with all your data protection policies. On a local AI server the language models (local LLMs) and the RAG agents run inside your infrastructure, without sending sensitive information to third-party APIs.
We design the full architecture: hardware selection (a local GPU) or private cloud, deployment of local LLMs, a vector database for your knowledge base and RAG agents connected to your systems: your own private AI, end to end. On that foundation you build whatever you need, from a private ChatGPT-style assistant that also knows your documents, your history and your processes, through to autonomous agents wired into your systems. In every case the information stays completely private: it never travels to OpenAI, Anthropic, Google or any other outside company, because the model runs inside your network. We start with a scoped case and measure real performance and cost before scaling. The GDPR and the regulations are covered from the design stage.
Phases of the service
Building a local AI server with judgment requires a clear process. Four phases: understand the requirements, design the architecture and models, deploy and integrate, and maintain the system with real data.
02
Architecture and model selection
We pick the right local LLMs (open-weight) and the vector database for your knowledge base. We design the RAG and agent architecture, sizing GPU, memory and storage to the real case.
03
Deployment and integration
We install the local AI server, deploy the models and RAG agents, and integrate them with your systems (CRM, ERP, files, databases). All inside your network, with access control and logging.
04
Monitoring and continuous improvement
We monitor performance, cost and answer quality. We update models, grow the knowledge base and tune the agents. You keep full control of your data and the model.
AI with your data always under control
100%
Of your data stays inside your infrastructure. Nothing is sent to third-party APIs.
24/7
Your models available without depending on an external provider's uptime.
0
Data sent to train third-party models.
Every deployment is sized to your case: hardware, models and real data volume.
About the local AI server
We answer the frequent questions companies ask before building their own AI infrastructure.
01 What is a local AI server?
It is infrastructure (local or in a private cloud) where the AI models (local LLMs, embeddings and RAG agents) run inside your network. Unlike using public APIs, your data never leaves your environment, which maximizes privacy and makes GDPR compliance easier.
02 Why can I not use ChatGPT with client data?
Because the problem is not the tool, it is the use. The free version of ChatGPT signs no data processing agreement and, by default, uses your conversations to train its models: feeding it personal data about your clients is in practice a GDPR breach, with fines that can reach 4% of annual turnover. A local AI server removes that risk at the root, because the data never leaves your infrastructure: nothing is sent to OpenAI, Amazon, Microsoft, Google or any other outside provider.
03 Which models can run locally?
Open-weight models (families like Llama, Mistral, Qwen and similar) in different sizes depending on your hardware, plus embedding models for the vector database. We choose the balance of quality, speed and cost for your case.
04 Do I need to buy GPU servers?
Not always. If the volume justifies it, a local GPU pays off; if you prefer not to manage hardware, we deploy in a dedicated private cloud where you remain the only one with access to the data. We decide during the diagnosis phase, based on cost and privacy.
05 How do RAG and the vector database work?
We index your knowledge base (documents, files, databases) into a vector database. When you ask, the RAG agent retrieves the relevant passages and the local LLM answers with that information, without the documents leaving your server.
06 Does this help me comply with data protection?
Yes. Keeping data in your own infrastructure is one of the strongest measures to comply with the GDPR and your internal data protection policies. We cover the full regulatory framework on our GDPR AI page.
Build your local AI server with Camacode
Tell us which models and which data you want to keep inside your infrastructure. In the first conversation we validate the case and tell you how we'd build your local AI server with scope, hardware and timeline.