On-Premise LLM Deployment
We install open-source language models (Llama, Mistral, and others) on your own server — no cloud, no data ever leaves your network.
LOCAL AI · SECURITY · ON-PREMISE
AI Setup & Configuration
We deploy your own AI right inside your office: your data never leaves the building, while automation, analytics, and smart assistants run fast, secure, and fully offline.
About this service
What it is and how we do it
Local AI solves one specific problem: you want to use language models, but you cannot send contracts, medical records, personal data or commercial correspondence to an outside service. We deploy open models — Llama, Mistral, Qwen — on servers inside your own infrastructure. The model runs on your network, requests never leave it, and the system keeps working even with the internet disconnected.
The real value comes not from the model itself but from connecting your own data to it. We build retrieval over your internal documents (RAG): policies, contracts, technical documentation and correspondence become a knowledge base an employee can query in plain language and get an answer with a link to the source. Access rights come with it — people only see answers drawn from documents they are already allowed to read. We match the model to your hardware, give you honest GPU requirements, and say plainly when a task would be cheaper on a cloud API — in which case our AI process automation service is the better fit.
What We Offer
Our Capabilities
Complete Data Privacy
All processing happens inside your infrastructure: documents, correspondence, and internal databases never leave the office or reach third parties.
Process Automation
We configure a local AI assistant to handle requests, document workflows, customer support, and routine internal tasks.
Predictive Analytics
We analyze your company's accumulated data with local models — forecasts, reports, and insights with zero risk of leaking beyond your perimeter.
Knowledge Base & RAG
We connect the AI to your internal documents and databases via RAG, so answers are grounded in your company's own up-to-date data.
Hardware Sizing & Setup
We calculate the compute you need, select the right server and GPU, then configure and tune the system for your company's workload.
Good fit
When to talk to us
- Security policy, a regulator or a client contract forbids sending your data to external services.
- The company has accumulated a large body of internal documents and finding anything in them quickly is impossible.
- Staff already use public chatbots for work tasks, and you want that back inside a controlled perimeter.
- You need AI running on an isolated network or at a site with an unreliable internet connection.
How We Work
Our Process
- 01
Needs Analysis
We study your company's processes and pinpoint where local AI delivers the most value — automation, analytics, or support.
- 02
Infrastructure Design
We select the server, GPU, and deployment architecture sized to your data volume and workload, with no internet exposure.
- 03
Model Installation
We deploy and configure the language model locally, then test performance and answer quality.
- 04
Integration
We connect the AI to your internal systems, documents, and databases through secure local channels.
- 05
Testing & Security
We verify internet isolation, access controls, and load resilience before going live.
- 06
Launch & Support
We roll the system into production and provide ongoing support, model updates, and scaling as you grow.
Tech Stack
Tools & Technologies
OllamaLlama 3MistralLangChainPythonDockerNVIDIA CUDAQdrant
Our work
Projects in this area
SecureKazAI
An on-premise AI platform for large organisations. A secure enterprise solution that unlocks AI without sending confidential data outside the company's infrastructure.
Vue.js · Django · AIFAQ
Questions about this service
What hardware does local AI require?
It depends on the model size and how many people use it at once. For a small team and a mid-sized model one server with a professional GPU is usually enough; dozens of concurrent users or larger models need several GPUs. We size the configuration before starting and match the model to the hardware you are willing to buy — not the other way round.
Is a local model weaker than ChatGPT or Claude?
On general tasks, yes — the large commercial models are still ahead. But for typical corporate use (document search, summarisation, data extraction, drafting emails) the practical gap is small, while the gain in privacy and predictable cost is decisive. We test several models on your real data and show you the results before any rollout.
How does the AI get access to our internal documents?
Through RAG: documents are indexed into a vector database inside your network, and the model answers from the retrieved passages with the source cited. Access rights are preserved — an employee only gets answers from documents they already have permission to open.
How is this different from your AI automation service?
Local AI is about where the model runs: we deploy it inside your infrastructure so data never leaves the company. AI automation is about what the model does: document processing, chatbots, forecasting, computer vision. The two are often combined — first the local model, then the specific automation use cases built on top of it.
Explore More
Other Services
Ready to get started?
Tell us about your project and we’ll get back to you within 24 hours.
Contact Us