Closed-Loop AI for Smart Manufacturing
Sub-millisecond machine coordination, predictive maintenance, and zero-defect quality inspection — all running at the factory edge over private 5G without cloud round-trips.
Talk to an ExpertThe Factory Floor as an AI-Native Environment
Industry 4.0 promised connected, intelligent manufacturing. OranSense delivers it — deploying a private 5G fabric across the production floor with AI inference running at the edge, enabling closed-loop machine control, real-time quality inspection, and predictive maintenance without a single cloud dependency.
The OranSense edge AI stack integrates with existing PLCs, SCADA systems, and MES platforms via standard OPC-UA and MQTT interfaces. No rip-and-replace — the AI layer augments existing automation investments with intelligence that operates at machine speed.
Key Capabilities
Closed-Loop Machine Control
AI inference at the edge enables sub-millisecond feedback loops for robotic arms, CNC machines, and conveyor systems. Eliminate the latency penalty of cloud-based control architectures.
Zero-Defect Quality Inspection
Computer vision models running on edge GPUs inspect 100% of production output at line speed. Detect surface defects, dimensional errors, and assembly faults with 99.7% accuracy.
Predictive Maintenance
Vibration, thermal, and acoustic sensors stream data to edge AI models that predict bearing failures, motor degradation, and tooling wear days before breakdown. Schedule maintenance on your terms.
AGV & Robotics Coordination
Private 5G provides the ultra-reliable low-latency connectivity that autonomous guided vehicles and collaborative robots require. AI orchestrates fleet movements and collision avoidance in real time.
Energy Optimisation
AI models monitor machine energy consumption patterns and optimise production scheduling to reduce peak demand charges. Typical energy savings of 15–25% without production impact.
Digital Twin Integration
Every machine, sensor, and production line is mirrored in the OranSense Digital Twin. Simulate process changes, test new configurations, and validate AI model updates before production deployment.