Massachusetts-based SaaS startup Cumulus Digital Systems, whose cloud platform monitors industrial data for quality assurance, will announce an $8 million funding round later today. Cumulus plans to use this money from GEC to advance its platform with additional connected-tool integrations and advanced data analytics for failure prediction. Once the company builds its primary tech stack, it wants to expand further into industry markets including power generation plants, aerospace facilities, and datacenters. Cumulus had previously raised $7 million since it was founded in 2018.
Cumulus collects engineering data and photographs from field tools like bolted joints and pressure valves via Bluetooth, both online and offline, and pulls it onto its cloud-based enterprise control center. It measures work quality at the individual and group level to analyze whether machines are working properly.
Matthew Kleiman, Cumulus cofounder and CEO, presents the platform as a unique digital solution for industrial maintenance and construction. In an interview with VentureBeat, Kleiman said, “We are going into a very analog facility. Maybe, you know, we joke if they have their information in Microsoft Excel, they’re really advanced, but there’s some truth to that. Now sometimes some of the really more advanced companies, they already have this digitized and they have a system that we can plug into with an API and easily pull this data back and forth. And so we either have to clean up that data and upload it into our system or help our customers get the data digitized in the first place.”
Cumulus matches a facility’s new field data against its existing data when creating benchmarks. It is now expanding to aggregate more data across similar types of facilities, which could improve accuracy and efficiency. All of Cumulus’ applications are hosted on AWS. Its web application is hosted on CloudFront, and its mobile applications are built with React Native.
“It was really trying to figure out … how do we surface the right information at the right time, Kleiman said. “If the data is not uploaded correctly, then you’re not going to get the results you want. You know the expression: garbage in, garbage out.”
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