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Company: Uniphore
Industry: Technology
Functional Use Case: Data Store, AI/ML

Uniphore believes conversations are a company’s most valuable asset. Based in California, Uniphore helps enterprises cultivate trust and more fruitful relationships with their customers by applying artificial intelligence (AI) to sales and customer service communications. Using real-time and post-call analyses, Uniphore’s products help organizations improve the efficiency and efficacy of their customer-facing employees, resulting in increased customer satisfaction and loyalty. Process automations reduce response times and costs while boosting revenues.

Unlike many companies that promote emotion AI capabilities but only analyze words, Uniphore has developed a comprehensive platform to account for three modes of sentiment expression, including a participant’s tone and facial expressions, in addition to the words used. “If you look at scientific research, 75 percent of communication is nonverbal,” says Saurabh Saxena, head of technology and VP of engineering, Uniphore.

Since the onset of COVID-19, many businesses have had to turn to digital value selling and remote sales scenarios. Uniphore’s technology helps them gain insights on “reading the room” in virtual settings. “It can really pay off to analyze how well a customer is emotionally connecting during a conversation, and do it with a multi-modal approach,” says Saxena.

Ensuring speed, reliability, and scalability with Cassandra

Realizing this would require aggregating a massive volume of sentiment data with high reliability and performance, Saxena and his team set up an open source Apache Cassandra® database.

Although the company operates on AWS, it did not see Amazon Keyspaces as a viable option for its database. “It’s not Cassandra, it’s a CQL layer over a DynamoDB storage engine,” says Saxena. “With the large data volume we produce, it was not the right solution for us. We needed the kind of storage optimization and partitioning that Cassandra offers.”

Guiding Higher Emotional Engagement on Demand

With Cassandra, Uniphore’s emotion AI platform can seamlessly capture and process approximately 200 data points on every meeting participant’s face at 24 frames per second. The company’s software monitors participants’ changing emotion and engagement levels. “High frame rates are necessary to capture very quick facial muscle movements, which exponentially increases our data volumes,” says Saxena.

The company blends this data with analyses of voice tone, for example, noting excitement when the pitch of a speaker’s voice rises to higher notes. Finally, Uniphore’s apps use NLP technology to assess word choices. “We have about nine or ten different AI models we run in real time to coalesce the data into something meaningful for our clients,” says Saxena. “The huge technical challenge of handling this voluminous data is where Cassandra really shines.”

Uniphore’s apps can guide a client’s representatives in real time or be used to train them soon thereafter with analytics of their very own communications. For example, a sales rep can see how much more successful they are in piquing a customer’s interest in certain value propositions versus others, when they are successful in satisfying a customer’s objections, or what the customer’s reaction is to a particularly impactful slide in their deck. By helping connect all the dots on what is or isn’t resonating with customers, representatives learn to build stronger rapport and close more deals.

Saxena is happy with how Cassandra is scaling with Uniphore’s business. “In the last month, we averaged more than two million reads per day on a relatively new product, with a handful of customers,” he says. “We’re starting to go big, and we’re already hitting 30 million writes per day, quickly approaching three million reads daily—and that’s all primarily within a six-hour period each day when meetings are happening for our earliest customers, which are all in the Pacific time zone.”

Stack Includes: AI/ML, Apache Cassandra, AWS, Datastax Astra

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