Success
Stories

A leading automotive manufacturer experienced frequent production line failures causing $15M annual losses. They needed real-time equipment monitoring without relying on cloud connectivity due to network reliability issues on the factory floor.

We deployed edge AI nodes on 250+ production machines with NVIDIA Jetson hardware running TinyML models. The system processes vibration, temperature, and acoustic data locally, predicting failures 2 weeks in advance with <5ms latency.

A portfolio of 45 commercial buildings faced skyrocketing energy costs and needed automated climate control that responds to occupancy patterns while reducing cloud data transmission costs.

We installed edge computing gateways running AWS IoT Greengrass across all buildings, processing occupancy sensor data, HVAC metrics, and lighting controls locally. ML models optimize energy usage based on real-time occupancy patterns without constant cloud communication.

A logistics company operating 120 delivery vehicles needed to implement autonomous navigation in warehouse facilities. Cloud-based processing created unacceptable 200ms+ latency for safety-critical decisions.

We deployed edge AI inference on each vehicle using Qualcomm Snapdragon Ride platform with custom-optimized ONNX models for object detection, path planning, and collision avoidance. All critical processing happens locally with <10ms decision-making latency.

A city of 2M residents struggled with severe traffic congestion, averaging 90-minute commutes. Traditional cloud-based traffic analysis was too slow for real-time signal optimization, and bandwidth costs for transmitting camera feeds were prohibitive.

We deployed edge AI nodes at 850 intersections processing video feeds locally using Intel Movidius VPUs. Computer vision models analyze traffic flow in real-time, optimizing signal timing dynamically. Only metadata is sent to the cloud, reducing bandwidth costs by 95%.