Building On-Premises AI Analytics for Industrial Automation with Ollama

TL;DR : We built local LLM-powered analytics for industrial OPC UA data using Ollama, ASP.NET Core, and TimescaleDB — enabling anomaly detection, forecasting, and cost optimization without sending sensitive process data to the cloud. The Problem: Cloud AI Doesn't Work for Industry When you're running a power plant, district heating network, or manufacturing facility, your process data is: Sensitive — production metrics, energy consumption, equipment performance High-volume — 2000+ measurement points per second, 172 million rows per day Network-constrained — industrial networks are often air-gapped or firewalled Compliance-critical — GDPR, sector-specific regulations, trade secrets Cloud-based AI analytics mean: Uploading gigabytes of time-series data daily Monthly subscription fees that scale with data volume Latency (query → cloud → response takes seconds, not milliseconds) Vendor lock-in and data residency concerns What if you could run GPT-class AI models locally, on the same server