(Bloomberg) — Manufacturing is one of the dirtiest corners of the corporate world. A startup of ex-Google engineers thinks it can clean it up with artificial intelligence.
Phaidra, a Seattle-based company, sells AI software to automate building controls for power plants and other industrial giants. It relies on the same patch as their old company, DeepMind, Google’s research lab. For several years, DeepMind let its artificial intelligence system manage the temperature checks inside Google’s data centers, which ultimately led to a significant reduction in the company’s electricity bill.
Phaidra’s algorithms are designed to select the most efficient temperature for unique facilities, such as a steel mill or vaccine manufacturer, and identify when equipment begins to lag in performance. Once in place, Phaidra’s system can reduce a plant’s energy consumption by up to 30% and save considerable capital, according to the startup. “It can immediately make those businesses more profitable,” says Jeremy Brewer, managing partner of Starshot Capital, an investor.
Jim Gao, CEO of Phaidra, sees manufacturing as a sector overlooked by Silicon Valley but ripe for the kind of advanced machine learning cooked up in places like Google. “They’ve been collecting data for so long, but they’re not using it,” he says of his new client base.
Indeed, the industrial sector, which accounts for about a quarter of all greenhouse gas emissions in the United States and continues to grow, is beginning to embrace cutting-edge data science. A report by IOT Analytics projects that industrial AI revenue will reach $72.5 billion by 2025, up from $11 billion in 2018.
Yet most of this usage represents basic tasks such as scanning data or creating online dashboards, not tools in which algorithms run entire control systems without anyone changing the dials. , like the one proposed by Phaidra. Few manufacturers have the capacity to attempt this or the budgets and technical prowess to maintain such a system. “It’s very rare,” says BCG chief executive Jon Van Wyck.
Phaidra, which was established in 2019, says it has several Fortune 100 industrial clients in fields as varied as pharmaceutical development to paper mills. He declines to name specific customers or financial figures. The startup recently raised $25 million in funding from Starshot and Character, investment firms created by other Google alumni. He also named Robert Locke, a 13-year veteran of industrial supplier Johnson Controls, as chairman.
Gao previously worked in DeepMind’s energy team alongside its technical co-founder, Vedavyas Panneershelvam. Engineers are among the few with extensive experience in reinforcement learning, a branch of AI where algorithms are designed to continuously improve. The most famous version of DeepMind is AlphaGo, its system for whipping up the famously difficult board game, Go.
While AlphaGo has been optimized to win Go, Katie Hoffman, another Phaidra co-founder, describes her system as being optimized to reduce the kilowatt hours of the plants it plugs into. Hoffman comes from the industrial sector – most recently as a director at equipment manufacturer Ingersoll Rand Inc. – and says many of Phaidra’s customers work in “critical” areas, with very specific demands on how their factories work. must be cooled and operated. They also rely on outdated, hand-coded software.
“They’re using what’s been around since the 1950s,” she says. “These industrial systems are incredibly difficult to operate on good days.”
DeepMind’s building control algorithms continued to alter dials inside Google’s data centers. And Google has started offering a similar service to its cloud customers. But the founders of Phaidra say their approach is tailored to the particularities of the industrial sector, which is light years away from sophistication of a Google building.
Gao did not share his company’s pricing, but says customers pay Phaidra less than the energy savings they realize through its service.