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A recent report found that industrial manufacturers experience 800 hours of unplanned downtime per year, or more than 15 hours per week. Unexpected troubleshooting costs $50 billion annually and lowers productivity and revenue. Most companies manually troubleshoot, but Austin, Texas-based ControlRooms.ai wants to change that.
The company’s AI-powered analytics app automates industrial troubleshooting. The startup announced an oversubscribed $10 million Series A round led by Origin Ventures, Amity Ventures, Tokio Marine Future Fund, S3 Ventures, GTM Fund, Alpha Square Group, and FJ Labs. It raised $13.75 million.
Chemical and energy plant troubleshooting is “virtually the same process today as it was in 1980,” Omar A. Talib, co-founder and president of ControlRooms.ai, told . “The traditional alarm does not provide specific insight into what may be causing problems, so it can lead to long and inefficient searches for errant ‘trends.’ Traditional troubleshooting exercises are exhausting and ineffective.”
The startup claims its turnkey troubleshooting platform lets users start using it “within a week without changes to their current systems” to reduce downtime. Its AI predicts manufacturing plant behavior and finds issues before engineers or control room operators do.
CEO Monte Zweben, an AI expert and serial entrepreneur, and Talilb, who worked for an energy producer AI solutions company, co-founded ControlRooms.ai in 2021. Last year, ControlRoom.ai launched its first product and has five customers.
“A chemical customer uses ControlRooms to track over 10,000 asset parameters in real time, such as pressures, volumes, and temperatures, to reduce reliability violations that degrade asset health,” Talib said.
He thinks ControlRooms works better than competitors because it’s not AI-powered. Talib says it’s “purpose-built for the process engineers, operations engineers, and operations supervisors who work in these heavy industry facilities,” unlike general-purpose AI platforms.
The new capital will accelerate product development and go-to-market in heavy industries like chemical, petrochemical, energy, and materials facilities in the U.S., Asia (China, Japan, and South Korea), Germany, and the Middle East.
The 15-person startup is conducting extensive R&D to add generative AI to its platform. The CEO said, “Imagine if the techniques that enabled the conversational intelligence of ChatGPT-4 and its competitors could be applied to industrial time-series data,” enabling unprecedented predictive applications like helping operations personnel see around corners and avoid surprises.
