The machine learning trend is showing no signs of slowing down, as Austin, Texas-based CognitiveScale became the latest startup in the space to land a significant funding round.

The company revealed this week that it has just bagged $21.8 million in a Series B funding round led by Intel Capital and Norwest Venture Partners. The startup, launched two years ago by veterans of IBM, plans to use the cash to bolster its product portfolio and expand global sales in order to meet rising demand for cognitive, machine learning-based applications.

Both venture funds will have a representative take a seat on CognitiveScale’s board as part of the deal, the company said.

“This funding will accelerate our mission to bring scalable, practical [artificial intelligence] to the enterprise,” Manoj Saxena, executive chairman of CognitiveScale, said in a statement. Saxena might be a familiar face to some, as he previously held the role of general manager of IBM’s Watson cognitive computing unit.

CognitiveScale’s cloud-based software is designed to apply machine learning to business processes. So far, the company has filed more than 60 patents pertaining to its Deep Cognition Engine, which it describes as a set of “industry specific AI algorithms” which can adapt to elastic market and user conditions. The company also boasts of customers in the financial services, healthcare and retail sectors.

Other notable executives at the company include founder and CTO Matt Sanchez, who previously worked at IBM’s Watson Lab, and CEO Akshay Sabhiki, who helped build healthcare applications at IBM before joining the company.

CognitiveScale is one of a clutch of startups that have set their sights on the potential of machine learning technologies to make predictions through analyzing enormous volumes of data, especially unstructured data from social media and other sources. Intel is likely taking a stake in the company in order to complement its own machine learning efforts, for example optimizing Spark analytics for platforms powered by its chips.

To date, CognitiveScale offers two versions of its machine learning platform. The first is a customer-facing platform that operates at the “edge,” analyzing data to glean insights such as consumer preferences. The second offering is an internal tool that uses “self-learning autonomous processes” to provide “contextual insights” to enterprises about specific markets. The company claims that both platforms can learn from new data and customer interactions to provide companies with “advice that accounts for dynamic changes in goals, preferences and the business environment.”

CognitiveScale’s platforms are available on IBM’s, Microsoft’s and Amazon Web Services’ clouds.

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