Private AI Infrastructure

Every team is already using AI. The question is not whether to adopt it — it is whether the data feeding it stays under your control.

→ Your team uses ChatGPT, Claude, Copilot, or Gemini — and someone in legal or compliance has already raised a concern → You handle client data, IP, or regulated information that cannot leave your infrastructure → You've been told "we can't use AI for this" — and you need a way to change that answer → You want AI that works for your whole office, at a predictable cost, without a per-query bill

Private LLM Inference

Your own instance of open-source models — Llama, Mistral, Qwen, DeepSeek — running on private GPU infrastructure in EU datacenters. No per-token billing. No training on your data.

AI Assistants for your team

A private ChatGPT-like interface connected to your documents, your knowledge base, your internal systems. Built on AnythingLLM or Open WebUI, configured for your workflow.

Autonomous Agents

AI that doesn't just answer — it acts. Document processing, data extraction, workflow automation, report generation. Agents that run inside your infrastructure and connect to your existing tools via API.

RAG — Your documents, searchable by AI

Feed your contracts, manuals, case files, or product documentation into a private vector database. Ask questions in plain language, get answers with citations. No document ever touches a third-party server.

→ Legal teams that need AI assistance but cannot send client communications or privileged documents outside controlled infrastructure

→ Financial services under DORA and GDPR that require auditability of every AI interaction and data residency within EU borders

→ Healthcare processing patient data that needs AI assistance without touching US infrastructure or violating data residency requirements

→ R&D and product teams protecting IP — source code, research, proprietary datasets that must never appear in a commercial AI training pipeline

→ Development teams running CI/CD, code review, or documentation generation that cannot use GitHub Copilot due to IP or compliance policies

→ Any organization that has already asked "can we use AI for this?" and heard "no" from legal or compliance

• Responds instantly — no trans-atlantic round trips between your team and the model
• Costs become predictable — fixed infrastructure, not per-token billing that spikes with usage
• Models can be fine-tuned on your proprietary data without exposing it externally
• Audit trails are yours — every query, every response, logged in your systems under your retention policy

• Compute — dedicated GPU infrastructure in EU datacenters, sized to your workload
• Inference engine — Ollama or vLLM, depending on model size and throughput requirements
• Interface — Open WebUI or AnythingLLM, configured for your team
• Vector database — Qdrant or ChromaDB for RAG workloads
• Monitoring — TOWER keeps an eye on GPU utilization, uptime, and response times
• Integration — OpenAI-compatible API endpoint, drops into your existing tools

Not your thing? Skip to Related missions.

• Latency — EU inference: 5–15ms round-trip vs. 80–120ms for US API endpoints. At scale, the difference between a snappy interface and one that feels broken.
• VRAM — 7B model @ Q4 quantization = ~4GB VRAM. 70B @ INT4 = ~42GB. Most business use cases run comfortably on a single A100 80GB.
• Serving — vLLM for high-throughput production workloads, Ollama for simplicity and fast iteration.
• RAG — chunk size 500–1000 tokens, 100–200 token overlap. Embedding model choice matters more than most people think.
• Vector DB — Qdrant for performance at scale, ChromaDB for simpler setups.
• API — OpenAI-compatible endpoint. Drop-in replacement for any tool already calling OpenAI — no code changes needed.
• Models — Llama 3, Mistral, Qwen, DeepSeek. Model selection depends on language requirements, context window, and task type. We assess per deployment.
• Fine-tuning — LoRA/QLoRA on your proprietary data, stays entirely within your infrastructure.

Read the Pilot Book: Private AI Infrastructure — full setup guide, hardware requirements, model selection, and honest cost breakdown. No fluff.

• Sensitive Industries — full sovereign stack for legal, healthcare and financial services
• Developer Stack — private GPU for code assistants and CI/CD pipelines
• Infrastructure — the hardware layer behind every PILOT deployment

• Private GPU — compute specs, model sizing, and throughput estimates
• Integration — connecting private AI to your existing tools and workflows
• TOWER Monitoring — GPU utilization, uptime, and inference performance tracking

Ready to brief us on your requirements? Request access and we'll assess your use case.