Strategy and Solutions

Zymr helped the client develop an Azure-native RTLS SaaS platform capable of aggregating data from an extensive network of IoT devices across numerous retail locations. The process began with hands-on experimentation using IoT sensors in their laboratories, enabling real-time data collection and object modeling. They established an Azure Analytics framework and employed Agile engineering methodologies to showcase the platform's core features while also crafting sophisticated analytics and machine learning algorithms for enhanced insights. Demonstrating the system's real-time location services (RTLS) capabilities, Zymr utilized the BLE token simulator to generate a high-volume data stream from over 1000 stores. The final platform was built upon a microservices-based Azure-native architecture, offering a robust array of APIs and automated business process management coupled with clear analytics.

Business Challenges

The client sought to develop an innovative retail industry RLTS solution that would leverage their BLE IoT sensors to monitor consumer foot traffic in shopping malls and stores. Building the RLTS platform would require expertise in BLE IoT and AI/ML along with Azure-native application development. The platform had to be developed as an Azure-native RTLS solution that could seamlessly work with their BLE iBeacon IoT devices in the retail industry. It was also expected analyze the real- time traffic of consumers and offer proximity marketing apps.

Business Impacts / Key Results Achieved

Zymr AI/ML and IoT experts helped the client architect a massively scalable multi-tenant Azure SaaS Platform tailored to their needs. The platform could securely manage 100K loT devices deployed in shopping malls and retailers. It was also capable of model foot traffic as an Al-analytics platform with rich information for retailers. Ultimately, the platform helped the client automate business workflow between internal systems, retailers, marketers.