Decentralized AI systems for industrial machine operations and predictive maintenance
prenode builds edge-deployed AI systems for industrial machinery, with a heavy Azure + Kubernetes architecture optimized for local inference without raw-data transfer. The company is scaling sales capacity (5 open roles) while maintaining a lean engineering core—a go-to-market shift for a 2018 KIT spinoff. Active work on cloud-edge data integration and IIoT platforms suggests a move from research-stage prototypes toward production infrastructure that operates across cloud and machine-local environments.
prenode develops decentralized AI systems for the industrial sector, enabling machine operators to run AI inference locally—reducing latency, preserving data privacy, and avoiding cloud-dependency for real-time decisions. Founded as a spinoff of Karlsruhe Institute of Technology, the company applies federated-learning and machine-specific AI to predictive maintenance, quality prediction, and downtime reduction. Based in Karlsruhe, Germany, the 11–50-person team operates across engineering, data, sales, and marketing, with current focus on scaling go-to-market motion while building out modern data infrastructure and cloud-edge integration capabilities.
prenode runs on Azure (Data Factory, Data Lake, Machine Learning, IoT Hub, Kubernetes Service, Functions), Databricks for analytics, Kubernetes and OpenShift for orchestration, and Node.js/React/TypeScript for frontend. Python and Java handle backend and ML workflows.
prenode is developing a cloud-edge IIoT data platform, modern data infrastructure, and backend services. Key projects include cloud-edge data integration, infrastructure-as-code implementation, and IIoT applications combined with AI solutions for industrial use cases.
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