A leading IoT Solution Provider Scaled Their Vibration-based Sensor Analytics Solution
A transformative shift towards scalable, efficient, and data-driven industrial IoT analytics, setting new standards for operational excellence and customer satisfaction in the industry.
Download the Case StudyOur client, a prominent industrial IoT solution provider specializing in vibration-based sensor analytics, sought to enhance its platform to accommodate anticipated market growth. Collaborating with Info Services, the project aimed to develop a scalable diagnostic solution, minimize human intervention, enhance operational safety, and introduce an intuitive mobile application for superior customer interaction.
Our client, a technology firm founded in 2000 by Penn State University researchers, specializes in energy harvesting, wireless sensors, and smart medical devices. Located in Pennsylvania, they drive sustainability and competitiveness through a Comprehensive Machine Health Platform, powering over 58M in unplanned downtime since 2018, and solving machine health problems worldwide.
Consumer Electronics and Software
Our strategic approach embodied a thoughtful assessment of the existing platform, the definition of an AWS-based target architecture, and the execution of diverse work streams. Continuous collaboration with the client organization ensured seamless progress. The following key components guided our strategic approach:
Conducted a comprehensive evaluation of the existing platform to identify scalability challenges and bottlenecks.
Outlined a target state architecture on AWS, emphasizing best practices and tangible benefits.
Executed multiple work streams focusing on data lake development, machine learning models, test automation, DevOps enhancements, and mobile app development.
Engaged in ongoing collaboration with the client team to manage build activities and provide continuous support.
Info Services implemented a scalable diagnostic solution by redesigning the architecture with microservices, reducing human dependency. Leveraging AWS services, Postgres, React, and Azure DevOps, ML models were integrated for maintenance predictions, alongside introducing a high-reliability mobile app for enhanced customer experience.
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