Your data stays yours
Every prompt you send to a cloud LLM leaves your network. Legal documents, client records, internal knowledge — none of that should touch a third-party API. Local models process everything on-site.
We set up private, on-premise AI systems for businesses — local LLMs, RAG pipelines, voice agents, and custom integrations. Your data never leaves your network. No cloud subscription. No vendor lock-in.
Cloud LLMs get the headlines. But for a lot of businesses, local deployment is the smarter move — not for technical reasons, but for business ones.
Every prompt you send to a cloud LLM leaves your network. Legal documents, client records, internal knowledge — none of that should touch a third-party API. Local models process everything on-site.
Cloud APIs charge per token. A busy internal tool can burn through thousands of pounds a month. A local model runs as many queries as you want — the only cost is the hardware you already own.
Network outage, restricted environment, remote location — a local model doesn't care. It's on your machine. It runs. This matters more than people realise until the moment the internet drops.
OpenAI changes pricing. Anthropic deprecates models. Google shifts policies. A local deployment is immune to all of it. You control the model, the version, and the update cadence.
We can fine-tune a local model on your specific data, writing style, or knowledge base. The result is a model that responds like it understands your business — because it was trained on it.
Network latency adds overhead to every cloud API call. A local model — even a smaller one — can respond faster for short tasks because the round trip is zero. Sub-second responses, consistently.
Every engagement runs through the same four stages — from the first conversation to a live, integrated AI system running on your infrastructure.
We start by understanding your use case — what you want AI to do, what data it needs to work with, and what hardware you're running. No jargon, no upsell. Just a clear picture of what's needed and whether local AI is the right call.
1–2 calls · FreeWe install and configure the model runtime on your machine — Ollama, LM Studio, or vLLM depending on your setup. We pick the right model for your use case (Mistral, Llama, Phi, Gemma, or a fine-tuned variant), configure memory and GPU allocation, and make sure it runs reliably before touching anything else.
1–3 daysThe model becomes useful when it's connected to your actual workflows. We build the layer that links it to your tools — a RAG pipeline over your documents, a chat interface for your team, an API endpoint your existing software can call, or a voice agent that takes verbal input and acts on it.
1–2 weeksWe document everything, walk your team through what was built, and hand over control. You're not dependent on us to keep it running. But we stay available for model updates, new integrations, and performance tuning as your needs evolve.
Ongoing — as neededEvery deployment ships with the same core set. What varies is which integration layer fits your use case.
Ollama, LM Studio, or vLLM installed, configured, and running reliably on your hardware. GPU acceleration where available.
The right open-source model for your use case — not the biggest one, the right one. Quantised for your hardware, benchmarked for your task.
Your documents indexed into a local vector database. The model answers questions with citations from your actual knowledge base.
A web UI your team can use immediately — or a voice layer (Whisper + TTS) so they can talk to it. Built on our Celestos stack.
OpenAI-compatible REST API from your local model. Drop-in replacement for cloud API calls — change one URL, nothing else.
Stack diagram, configuration reference, and usage guide. Your team can understand, maintain, and extend the system without coming back to us.
Multi-step task execution — the model plans, acts, observes, and adjusts. Integrates with files, APIs, browsers, and system tools.
Model trained on your data, your tone, your domain. Responses that feel like they came from someone who knows your business — because the model does.
We work with the best open-source tools in the local AI ecosystem — nothing proprietary, everything auditable, all running on your infrastructure.
Celestos is our own local AI agent — voice-driven, task-capable, and running on local models or any OpenAI-compatible endpoint. It's the same stack we deploy for clients.
Voice input, real-time reasoning, tool execution, and local model support built into one agent. We run it ourselves, on local hardware, every day. If you're wondering whether local AI can actually work in practice — this is the answer.
Local AI isn't for everyone — but for the right business, it's the only sensible choice. These are the situations where it pays off most.
Contracts, case files, financial records — a local RAG pipeline lets your team query internal documents with AI answers, without any of that content leaving the building.
Company wikis, SOPs, product manuals — a local chatbot that actually knows your documentation. Staff ask questions in natural language and get accurate answers from your own content.
Patient data, research findings, clinical notes — environments where data residency is non-negotiable. Local deployment is the only AI option that's compliant by architecture.
An AI support agent trained on your product docs and past tickets. Handles first-line queries accurately, without your customer data touching a third-party API.
GitHub Copilot alternative that runs on your machine. Full codebase context, no code leaving your environment, and no monthly subscription. Works with VS Code and any editor via API.
When your internet goes down, your local AI still works. For operations-critical use cases, local deployment is the only option that guarantees uptime.
Tell us what you need. We'll tell you whether local AI is the right call — and if it is, exactly how we'd build it.
Or email us directly at contact@unitar.app