A GPU server rack enclosed in a glass room, with the city outside at night

Private & Local AI

We set up AI that runs on your own computers, so your confidential data never leaves your company.

RemSoft.Dev deploys AI that runs where your data already is: on your servers, in your data centre, or on workstations in your office. We select and host open-weight models through Ollama, vLLM or ONNX, build search and RAG over internal documents, size the hardware - including AMD GPUs - and give local models the same tools cloud agents use. Where cloud models are allowed for some tasks, we route between local and cloud so sensitive work stays in-house. Our own engineering runs this way: a bridge that made local LLM agents up to 10x more effective, a Rust runtime that runs Hugging Face models on AMD GPUs, and a UI design generator that never leaves the machine.

Services
RemSoft.Dev Team
RemSoft.Dev Team
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What is included

A private AI system is more than a model on a server. We deliver the whole path from hardware to a tool your people use every day.

  • AI discovery A short, fixed-scope, paid first step: which use cases work with local models, what hardware they need, and what they will cost to run compared with a cloud API.
  • Models & runtimes Open-weight models chosen per task and served through Ollama, vLLM or ONNX - including a broad range of Hugging Face models, not only GGUF/Llama formats.
  • Private knowledge search Search and RAG over internal documents and source code, indexed and served on your own hardware.
  • Tools for local agents A bridge that gives local models sandboxed files, GraphRAG and code search through MCP - the same tool catalog cloud agents use.
  • Hardware & hybrid routing Sizing for NVIDIA and AMD GPUs, and routing that keeps sensitive work local while less sensitive tasks may use a cloud model where you allow it.

Measured in our own local AI work

Up to 10×

higher task effectiveness for local LLM agents through our MCP bridge

32 GB

largest local model tested with the bridge

2 GPUs

indexing and search running in parallel on one AMD workstation

Who this fits

  • Companies bound by GDPR or client contracts that forbid external AI processing
  • Healthcare, finance, industry and public-sector organisations
  • Teams with constant AI workloads where owned GPUs cost less than API calls
  • Air-gapped or restricted networks

Can local models really do useful work?

Yes - when they get the right tools and the right tasks. Smaller local models handle native tool calls poorly on their own; given a purpose-built bridge, they became up to 10x more effective in our tests. See it in practice: an MCP bridge for local LLM agents, a Rust ONNX runtime on AMD GPUs, a UI design generator that runs 100% locally and code intelligence with GraphRAG. The servers themselves are covered by Cloud, DevOps & On-Premise.

Case studies from our work

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