Case Study: Control achieves real-time network optimization with Quix

Ensuring data speed and resiliency in a mobile IoT application

Control is a telemetry company focused on race car data connectivity, helping teams transmit vehicle data reliably from tracks like Daytona, Le Mans, and Nurburgring. Its challenge was that devices would connect to the first available mobile network at a new venue rather than the best one, causing up to 23% performance degradation and requiring manual configuration that was time-consuming and error-prone.

Quix helped Control build a real-time machine learning solution on its stream processing platform, connected to Azure Event Hub, to transform, contextualize, train, and deploy models that automatically rank networks and update device configurations live. Quix enabled Control to create 82 ML models in just two weeks, automate connectivity optimization, guarantee network performance, and reduce operational burden while improving resilience without adding extra monitoring effort.

Control

Nathan Sanders

Technical Director