18 months after raising a $16.5M series B round, Montreal headquartered Mnubo has been acquired by a Massachusetts-based maker of optimization software. For more than 30 years, Aspen Technologies has helped customers optimize the design, operation and maintenance of critical assets in complex, capital-intensive environments. The transaction is valued at an impressive CA $102M.

Founded in 2012, Mnubo became one of the darlings of the Montreal tech space, and enjoyed tremendous success helping its customers leverage the rapidly evolving world or industrial IoT. A number of companies on both sides of the border operating in this field have been acquired over recent years, illustrating just how valuable IIoT technologies are becoming to the world’s major manufacturers. Mnubo had enjoyed success in foreign markets as well, perhaps most notably in Japan, where it had already opened an office.

“The global adoption of AI and IoT technologies is powering the next wave of industrial-digital enterprises. The Mnubo AI and analytics infrastructure was purpose-built to accelerate the digital transformation of traditional industries by democratizing the power of artificial intelligence and machine learning. Our location in Montreal’s world-class AI ecosystem enables AspenTech to establish a Centre of Excellence for these cutting-edge technologies, and to attract some of the best talent in this space. We are very excited to continue to develop innovative AI solutions that target the industrial internet of things at enterprise scale, under the AspenTech umbrella,” said Frédéric Bastien, co-founder and CEO of Mnubo.

Aspen is clearly pushing itself deeper into this space as the potential for exponential growth is clear. Just last month Aspen acquired Sabisu, a UK-based company that provides a flexible enterprise visualization and workflow solution to deliver real-time decision support.

The company says these acquisitions will enable AspenTech to accelerate the distribution of embedded AI in both its existing and future solutions. By combining first principle engineering models and deep process expertise with AI capabilities, these solutions will enable the automation of knowledge and data-driven decision-making for continuous improvement across the design, operation and maintenance lifecycle of industrial assets.