Private local AI that never sends your data outside the company
Contracts, customer data, technical documentation and accounting records shouldn't end up in public chatbots. We run language models on a server in your company and connect them with the documents and systems you use every day.
- Data stays on your server, in line with GDPR and trade secret protection
- An assistant that answers questions from your company documents
- Predictable cost without per-query fees
What a local AI model is
A local AI model (local LLM) is a language model similar to those behind ChatGPT, running on a computer or server owned by the company. Questions and documents are processed on site without being sent to an external provider. Only authorised people have access, and the whole system works without an internet connection.
Open models such as Llama, Qwen, Mistral, Gemma and the Polish Bielik are now good enough for most office tasks: summarising, searching documents, drafting replies to customers and extracting data from invoices and orders. We pick the model for the task, language and hardware, test it on your examples and only then deploy it.
What the implementation covers
Workshop and use-case selection
We look for the tasks where AI saves the most time and calculate the return before you buy hardware.
Hardware selection
A server or workstation with a GPU sized for the model and the number of users, from one department to the whole company.
Model deployment
Installing and configuring the runtime (Ollama, vLLM, llama.cpp), choosing and testing models, performance tuning and updates.
Document assistant (RAG)
AI answers questions based on your procedures, contracts, offers and documentation and cites the source.
Integrations
Connections to your ERP, Microsoft 365, e-mail and line-of-business systems via API, plus n8n automations.
Staff interface
A browser chat with company sign-in, conversation history and permissions per document collection.
Security and compliance
Access control, query logging, an AI usage policy and documentation in line with GDPR and the EU AI Act.
Training and support
We train users and the administrator, monitor operation and update models.
From idea to working AI
Workshop
Two to three hours with the people who do the work. We pick one or two use cases to start.
Prototype
A test on your documents on our hardware. You see the answer quality before investing.
Deployment
Installation on your server, integrations, permissions and training.
Growth
Measuring results, adding use cases and updating models.
Example use cases
- Accounting firm: searching tax rulings and drafting replies to clients
- Manufacturing: an assistant for process engineers and maintenance that answers from machine manuals and routing sheets
- Trading: reading orders from e-mails and PDFs and creating documents in the ERP
- Public administration: searching resolutions and regulations, drafting replies to citizens
Frequently asked questions: Private local AI
What hardware does a local AI model need?
It depends on the model size and the number of concurrent users. For one department and a mid-sized model a workstation with a 16–24 GB GPU is enough. For a whole company and larger models you need a server with one or more professional GPUs. We size the hardware after the prototype, when the real load is known.
Is a local model as good as ChatGPT?
For general knowledge and the hardest tasks the largest cloud models still lead. For business tasks such as answering from documents, summarising and data extraction, a well-chosen local model gives comparable results because it answers from your data, not general knowledge. We verify quality on your examples before deployment.
Is local AI GDPR compliant?
Processing on your own server makes compliance much simpler, because data is not passed to an external provider or outside the EEA. You still need to define the purpose, permissions and retention of chat history. We prepare this documentation as part of the deployment.
What is RAG?
Retrieval-Augmented Generation is a technique where the model first retrieves passages from your documents and then answers based on them, citing the source. The AI knows your procedures and prices without expensive model training, and every answer can be checked.
How much does a local AI deployment cost?
The cost consists of hardware, implementation work and support. The smallest deployment for one department is mainly a GPU workstation and a few days of work. We give an exact quote after the workshop, together with the expected time savings.
Does a local model need to be trained on company data?
Usually not. Connecting documents via RAG is enough, and the model uses them for every answer. Fine-tuning is only needed in special cases, for example when the model must write in a very specific style or format.
Related services
- AI trainingAI training for management, office staff, IT teams and public servants, on site and online.
- Process automationn8n, Power Automate, ERP integrations via API, data analytics and management reporting.
- ManufacturingIT on the shop floor and in the office, production data for management, automation and AI for engineers.
See what AI can do with your documents
Book a free consultation. We pick one use case and show a prototype on sample data before you decide to buy hardware.
- Phone
+48 501 035 571 - E-mail
kontaktaritmasoft.eu