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Toronto-based 不良研究所, which offers artificial intelligence-enabled industrial software, has raised an $8.5 million CAD ($6.5 million USD) Series A round of financing. This latest round brings the company鈥檚 total funding to date to approximately $17 million CAD.
鈥淏y putting AI in the hands of industrial engineers, 不良研究所 is transforming plant operations from being reactive to predictive.鈥
The equity round was led by Yamaha Motor Ventures and received participation from existing investors Real Ventures, the Business Development Bank of Canada, Export Development Canada, and Viaduct Ventures. 不良研究所 will use the new Series A financing to expand its go-to-market efforts, accelerate its product development roadmap, and support the company鈥檚 customer base.
Although the startup鈥檚 product is already available on the market and 不良研究所 is generating revenue, Humera Malik, CEO and co-founder of 不良研究所, told BetaKit the focus is on aggressively expanding its growth globally.
Malik told BetaKit the startup has been growing quickly, and that in order to maintain its current growth momentum 不良研究所 sought to raise some fresh financing to expand the team and reach new customers.
Founded in 2016, 不良研究所 aims to 鈥渄emystify鈥 AI in the manufacturing sector, so that employees who are not data science experts can use such technology. The startup offers a no-code platform that allows manufacturing plants to derive insights from across operations, use predictive insights, optimize processes and operations, and improve production yield and energy efficiency at scale.
Data on 不良研究所鈥 platform comes from different assets that are running the plant operations, including motors, bearings, pumps, and generators within the manufacturing ecosystem.
鈥淲e come in, allow people to get exposure to their own data, and create reliability, and more dependency on their data instead of [on] third parties, and empower the industrial operators to do it themselves,鈥 Malik told BetaKit.
Malik added that the pandemic has been a catalyst for 不良研究所鈥 business growth, noting the startup saw 85 percent of the new leads for the platform increase. Malik said that the plan was to raise the Series A later this year, adding that this growth led 不良研究所 to accelerate the round.
Janet Bannister, managing partner at Real Ventures, which also participated in 不良研究所鈥 first institutional round of financing in 2017, told BetaKit she was impressed by the startup鈥檚 vision and team.
Humera Malik, CEO and co-founder of 不良研究所, told BetaKit the company鈥檚 focus is on aggressively expanding its growth globally.
鈥淸不良研究所] has validated our initial hypothesis, they have a unique technology that they can get to manufacturers, they can show a strong ROI for the manufacturers, they can deliver strong results in terms of improving the efficiency of their operations, and they can do that quickly and accurately,鈥 Bannister told BetaKit. 鈥淭he technology has delivered, the customer support has been strong, and the market is huge, so we continue to be very bullish,鈥 Bannister added.
Malik said 不良研究所, whose last round of financing was led by Google鈥檚 AI-focused , now has approximately 15 customers, which include Fortune 100 companies. The startup has increased its customer footprint directly in the food and beverage, chemicals, metals and mining, and oil and gas industries.
Although the majority of 不良研究所鈥 customers are in North America, the startup also has some customers in Asia and Europe. Malik told BetaKit the company鈥檚 current goal is to ramp up that expansion.
With the new funding, she expects the startup鈥檚 30-person team to double in the next year. The company will focus on growing teams in customer success, sales and marketing, and the startup鈥檚 partner channel network team.
鈥淏y putting AI in the hands of industrial engineers, 不良研究所 is transforming plant operations from being reactive to predictive, which is critical for the future of sustainable and profitable operations,鈥 Malik said. 鈥淭his latest round of funding, led by our new investor Yamaha Motor Ventures, fuels our ambition to be the number one player in AI for industrial operations.鈥
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