Authenticx, a conversational intelligence company that uses artificial intelligence to help healthcare organizations understand their customers, today announced it has raised $20 million in new funding led by Blue Heron Capital.

When people call their healthcare provider for help, they often hear an automated voice tell them that their call is being recorded. For companies, this produces a treasure trove of data about how customers experience their service.

Tapping into these customer voices is what Authenticx does. By using conversational machine learning and AI models at scale to listen for things that are causing problems or issues with customers and anything that will help that company deliver its service it helps companies understand their customer experiences better.

Additional investors also participating in the Series B round included Beringea, Indiana Next Level Fund/50 South Capital Advisors, High Alpha, Mutual Capital Partners, Signal Peak Ventures, Allos Ventures, Elevate Ventures and M25.

Amy Brown, chief executive of Authenticx, told SiliconANGLE in an interview that the platform ingests hundreds of thousands to millions of voice interactions from a healthcare company to listen to themes, pain points, value statements and other sentiments that drive insights into how customers are feeling.

“Many conversational intelligence companies exist to automate communication so that a human doesn’t have to,” Brown said. “How we’re different is our mission is to help humans understand humans. So our focus is utilizing machine listening and machine learning models and AI to interpret and provide organization and structure around this very voluminous and messy data source.”

One feature of the Authenticx platform is known as the Eddy Effect dashboard, named after circular water currents that turn back on themselves to form whirlpools, which helps surface complex problems that are ruining customer experience in healthcare.

For example, numerous customers may be having issues with billing where they keep calling back in order to resolve an issue. But something in the billing system causes them to get transferred to another part of the customer service chain or creates a delay, creating a loop, thus generating frustration. The platform can pick up on this using the conversational intelligence AI model and warn that billing has a problem.

Using this algorithm, Brown said, root causes can be determined, with a prevalence and an emotion assigned. Additionally, management can drill down and listen to individual calls in order to get a sense of what’s going on in order to understand better what needs to be fixed about the system or the interactions in order to resolve the larger problem.

“Leaders can go into our platform and understand ‘I have an Eddy Effect of 25%, the root cause is our billing system, and it is associated with a very negative customer emotion, which includes confusion,’” said Brown.

Billing isn’t the only potential type of service that can benefit from Authenticx platform. The service has also been deployed for providers and life sciences, such as pharmaceutical companies that provide patient support for drugs and nurse lines to discuss symptoms or help with financial support. All of these companies record their calls with customers.

Brown said Authenticx will use this new funding to fuel sales and marketing efforts. It also plans to expand the team, such as machine learning engineers to automate listening for the use cases that the company’s clients care about.

As for the next five years of building out the product, the company will continue to explore the healthcare market and expand beyond providers, life sciences and billing into other healthcare entities such as medical devices, diagnostics, and life and disability insurance.

“There’s a big industry out there today that’s focused on listening to customers through the source of survey data, and we’re here to provide an alternative and a better way of listening to customers by listening to the literal voice of the customer,” Brown said. “We have found that entire organizations can be inspired by listening at scale to data that they already own.”

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