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Hyper Anna raises $16 million to address shortage of data scientists with virtual agent

22 August 2017  |  10127 views  |  0 Big data

Hyper Anna, a Sydney-based Australian start-up that provides financial services organisations with a virtual data scientist which is available at the touch of a button, has secured a $16 million series A funding round.

Billed as a Siri for analytics, Hyper Anna gives customers business intelligence and on-demand insights based on natural language requests.

The $16 million round was led by Sequoia China, with significant investments from Airtree Ventures, Westpac Reinventure and IAG Firemark Ventures. Both Westpac and general insurer IAG are two of Hyper Anna’s early customers.

The capital raised will be invested directly into the expansion of Hyper Anna’s operations to international markets in Hong Kong, Singapore, China and USA.

The expansion is already underway, with an office in Hong Kong scheduled to open in September this year, says Natalie Nguyen, co-founder and CEO, Hyper Anna.

“The idea behind Anna is that all businesses, regardless of scope or size, deserve to have access to data scientists to drive value from the data they create and own," she says. "With a shortage of talent in the market Hyper Anna allows customers to scale their data analysis requirements in a very efficient manner.

The overseas roll out will expose the company's AI agent Anna to new languages and cultures, she adds, helping it to learn more about financial services operations at a global scale.

“In each of these markets, many organisations are struggling to find data scientists to do the analytics and get the insights they need. We fix that problem with Anna," says Nguyen. “If you ask ‘how are sales going?’ and I told you that you had sold 100 units, on its own, that information does not provide much insight into the performance of the business. The intention is to track sales against time, and just like a human, Anna picks up on those nuances. Intent is something that humans have a knack for intrinsically understanding, but something that machines have traditionally struggled with.”

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