Private AI Infrastructure

Data Never Leaves Your Network

Every time you send a prompt to ChatGPT, Claude, or Gemini, your data travels to someone else’s servers. For most casual use, that’s fine. For businesses handling sensitive client data, proprietary processes, or regulated information — it’s a non-starter.

We deploy production-grade AI models on infrastructure you own and control. Same capabilities. Full data sovereignty.

What We Build

GPU Server Design & Deployment

Purpose-built AI inference servers tailored to your workload. We spec the hardware (NVIDIA RTX, A-series, or consumer-grade options depending on your needs), build the system, install the inference stack, and hand you the keys.

Private LLM Hosting

Open-source language models (Llama, Mistral, Qwen, DeepSeek, and others) running on your hardware with the same conversational intelligence you get from cloud APIs — without the per-token costs or data exposure. At high usage volumes, on-premise AI typically pays for itself within 6-12 months compared to cloud API costs.

AI Inference Serving

Production-ready inference with vLLM, Ollama, or LocalAI — load-balanced, monitored, and optimized for your throughput requirements. We set up the full stack: model serving, API endpoints, monitoring dashboards, and automated health checks.

Model Optimization

Not every business needs a 70-billion parameter model. We select, quantize, and fine-tune models to match your use case and hardware constraints. A well-optimized 8B parameter model running locally often outperforms a generic cloud model for domain-specific tasks.

Who Needs Private AI

• Healthcare practices processing patient data under HIPAA
• Legal firms handling privileged client communications
• Financial services with regulatory data residency requirements
• Defense contractors working under CMMC compliance
• Any business that considers its internal data a competitive asset

Why We Can Do This

This isn’t a service you can get from a typical AI consultancy. Most AI shops are cloud-native — they know APIs and prompts, but they’ve never racked a server, configured a network, or managed bare-metal infrastructure.

We’ve spent 20+ years building and administering systems. We run our own GPU infrastructure. We deploy AI models on our own hardware daily. When we spec a private AI system for your business, it’s because we’ve already solved every problem you’ll encounter — on our own gear first.

What It Costs

Hardware costs vary by workload — a capable starter system begins around $5,000, while high-throughput multi-GPU setups scale from there. Our consulting engagement starts with a specifications and architecture document, followed by procurement guidance, deployment, and ongoing support. Every recommendation comes with a clear cost-benefit analysis against cloud alternatives so you can make an informed decision.