Samotics B.V., founded in 2015 and headquartered in the Netherlands, builds AI-powered predictive maintenance systems for industrial assets that are difficult to access or hazardous to inspect. The company's core technical domain centers on Electrical Signature Analysis - using sensor data from power lines to infer the mechanical and electrical health of rotating equipment without direct physical contact. That's a meaningful security and operational surface: the data pipeline runs from field-deployed hardware monitoring pumps, conveyors, and fans in remote or dangerous environments, through to the SAM4 platform, where analytics run on that telemetry.
The threat model here is industrial, not enterprise. Samotics operates across water and wastewater, chemicals, steel, oil and gas, airports, pulp and paper, and mining - sectors where unmonitored equipment failure can cascade into environmental incidents, safety hazards, or significant unplanned downtime. The company serves customers on five continents, meaning the platform ingests and processes sensor data from physically distributed, often air-gapped or low-bandwidth industrial sites. Protecting the integrity of that data stream - and ensuring the AI models making maintenance recommendations can't be poisoned or spoofed - is the kind of problem that sits squarely at the intersection of OT security and applied machine learning.
The team combines deep industrial engineering expertise with artificial intelligence, operating with a mission-driven approach aimed at eliminating unplanned downtime and reducing energy waste. If you're thinking about securing data pipelines at the edge, hardening ML inference in constrained environments, or defending industrial control systems against manipulation, Samotics is working on that problem set at real scale.
