RoviSys — a leading independent provider of comprehensive process automation, building automation, and discrete manufacturing automation solutions — today announced Select Consulting Partner status within the Amazon Web Services, Inc. (AWS) Partner Network (APN), the global community of Partners who leverage AWS to build solutions and services for customers.

Select Consulting Partner status in the APN recognizes systems integrators that have successfully deployed customer applications using AWS services. APN Consulting Partners design, architect, build, migrate, and manage cloud solutions built on AWS. Select Consulting Partner status represents an elevated level of engagement and expertise. Achieving APN Select Consulting Partner status uniquely positions RoviSys to help businesses take full advantage of all that AWS has to offer and accelerate their journey to the cloud.

“We are pleased to be recognized as a Select Consulting Partner in the APN,” said Bryan DeBois, Director of AI at RoviSys. “Organizations need a trusted, secure foundation to drive operational decisions and innovation. RoviSys has been delivering solutions that drive those decisions for over 30 years. Our work collaboration with AWS ensures that we can deliver best-in-class cloud automation and information solutions to our clients.”

While many manufacturers use basic rules or modeling approaches to identify issues based on past performance, the RoviSys approach utilizing AWS systems helps industrial companies unlock value from existing data, improve operational efficiency, and decrease expenses by avoiding maintenance time due to equipment failure while reducing false alarms based on misdiagnosed issues.

RoviSys is working with AWS to integrate Amazon Lookout for Equipment for manufacturers in the pharmaceutical, biotech, automotive, consumer goods, oil & gas, and chemicals industries. By leveraging existing equipment and infrastructure, this approach enables advanced machine learning in customer facilities by analyzing sensor data such as pressure, flow rate, RPMs, temperature, and power. It then uses machine learning to predict machine failure, suboptimal performance, and equipment abnormalities with speed and precision.

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