On-Prem AI for Operations

Every team wants AI-assisted operations. Not every team can send their logs, metrics, and incident data to OpenAI. If you operate under GDPR constraints, data sovereignty requirements, or strict security policies, on-prem AI is not optional. It is the only compliant path.

This mission sets up local LLM serving and private RAG pipelines on your existing infrastructure. Your data stays on your hardware. Models run on your GPUs. Nothing leaks.

What's included

• Model serving: vLLM or Ollama deployment on your GPU-equipped nodes, with OpenAI-compatible API
• Hardware assessment: GPU sizing, memory requirements, inference latency targets, quantization strategy (GGUF/AWQ)
• RAG pipeline: document ingestion, vector store setup (local), embedding pipeline, retrieval-optimized prompting
• Operational integration: connect the LLM to your observability stack (Grafana queries, log search, incident context)
• Security hardening: access control, audit logging, prompt injection defenses, network isolation
• Cost analysis: inference cost per query vs. cloud API pricing, break-even on GPU investment

Deliverables

• Local LLM inference endpoint with OpenAI-compatible API, running on your infra
• Private RAG pipeline indexed on your documentation and runbooks
• Grafana/observability integration proof-of-concept (ask questions about your metrics)
• Deployment manifests (Kubernetes or Proxmox VM) with GPU passthrough configured
• Security and access-control documentation
• Model evaluation report: quality, latency, and cost per query vs. cloud alternatives

Ideal for

Teams in regulated industries (finance, healthcare, government) or security-conscious organizations that need AI capabilities but cannot send operational data to third-party clouds.

Tech stack