AI-powered predictive maintenance for industrial operations
Groundup.ai builds condition-monitoring systems that detect machinery failures before they happen, targeting manufacturing, maritime, logistics, and energy sectors. The stack spans industrial protocols (CAN, Modbus, PROFINET, EtherCAT) and ML frameworks (TensorFlow, PyTorch, scikit-learn) across AWS/GCP/Azure—a configuration that signals deep integration with legacy plant systems and cloud-scale training pipelines. Active projects centered on hardware sensors and proof-of-value work, paired with client onboarding pain points, indicate a hardware-forward go-to-market still optimizing deployment velocity.
Groundup.ai delivers AI-powered condition monitoring for industrial assets without requiring in-house data science teams. The platform uses a three-sensor approach (vibration, sound, temperature) to identify anomalies and predict failure modes, then prescribes maintenance actions. The company serves manufacturing, maritime, logistics, airports, rail, and oil-and-gas operators. Deployment is supported by Groundup Asset Library, a pre-trained model repository designed to accelerate time-to-insight. Groundup operates from Singapore with a small, engineering-forward team focused on hardware integration and customer onboarding workflows.
Groundup combines three sensor modalities—vibration, sound, temperature—with TensorFlow and PyTorch models running on AWS, GCP, or Azure. Industrial protocols (CAN, Modbus, PROFINET, EtherCAT) connect legacy plant equipment to the AI layer.
Groundup.ai is headquartered in Singapore and operates with 11–50 employees, founded in 2021. All current hiring activity is concentrated in Singapore.
The company targets manufacturing, maritime, logistics, airports, rail, and oil-and-gas operators. Clients typically operate fleets of machines or critical infrastructure where unplanned downtime carries high cost.
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