Data sovereignty without compromise, open-weight models on your own infrastructure, with air-gapped capability.
Many organizations cannot send sensitive data to public AI providers: for regulatory, business, or simple trust reasons. Local (on-prem) AI deployment solves this: models run on your own infrastructure, data never leaves the environment.
Open-weight models (Llama, Mistral, Qwen) today are competitive with public services on many tasks, especially after fine-tuning. We handle the entire deployment in one hand: from hardware specification to air-gapped operations.
Your data never leaves your infrastructure, neither during development, nor in production.
GPU server specification, procurement, installation, workstation- and server-class.
Open-weight model selection (Llama, Mistral, Qwen), quantization, fine-tuning.
Networks without internet: regulated, classified, industrial environments.
Local AI is more than model deployment: hardware, model, network isolation, operations. We handle all four.
GPU server specification, procurement, racking, cooling design. NVIDIA-focused, workstation- or server-class.
Open-weight models (Llama, Mistral, Qwen), quantization (Q4/Q8), serving (vLLM, Ollama, llama.cpp), API gateway.
Fully isolated environments: regulated, classified, industrial networks. Cross-link to our OT security practice.
Updates, monitoring, security patching, performance tuning, recurring contract or one-off handover.
What for: what latency, what accuracy, what availability. Hardware follows from these.
Specification, procurement, racking, network isolation. Air-gapped is a separate process.
Model selection, quantization, fine-tuning, serving stack installation.
Monitoring, updates, security patching, performance tuning, managed or handed-over operations.
The approach is industry-specific, different priorities for a bank than a retail business. Here are the verticals where it fits best.