The company is working on solutions to help water utilities anticipate infrastructure failures.
Pluto AI, a predictive analytics platform for water utilities, has wrapped up a $2.1 million seed round.
The round, which comes amid the company’s pilot test phase, saw participation from the accelerator 500 Startups; venture firms Refactor Capital, Fall Line Capital and Unshackled Ventures; and NerdWallet Co-Founder Jacob Gibson.
The company’s product is a platform that extracts and processes data from sensors, meters and other devices, and then feeds it through deep machine learning schemes to deliver insights. The major use case appears to be monitoring the health of assets and anticipating their failure.
“Aging assets contribute heavily to water loss, with the average age of U.S. water pipes at 47 years,” the company wrote in a blog post announcing the seed round. “Replacing them is very expensive! We leverage large amounts of existing data to extract actionable insights that enable water facilities to manage their assets better.”
The space is hardly without competition — plenty of tech companies are turning to machine learning and predictive analytics, including for infrastructure. Pluto’s advantage, per its website, is that it’s built specifically for the quirks of water management.
“We need wisdom that's specific to water,” the company’s website reads. “We don't need some generic ‘artificial intelligence platform’ that uses the same model for many verticals like health care, energy, mining and so on. Artificial intelligence is an amazing tool that can solve really difficult problems, but only if we use it in the right way. Water is a very unique vertical that has a lot of nuances associated with it. An artificial intelligence solution that takes this into account when extracting wisdom will totally outperform a generic artificial intelligence platform.”
The product is set up in a software as a service structure, with tiered pricing based on data volume.
Pluto was launched in 2016 and is based in Palo Alto, Calif.
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