About Us

Every business makes mistakes, designs imperfect interfaces, or leaves questions unanswered. Customer service can help turn these issues into opportunities by providing resolutions to increase customer loyalty and long-lasting relationships. Yet, businesses spend $1.3 trillion on 265 billion customer service calls every year and customers and agents are still unhappy; agents spend their days answering mundane calls while customers wait countless hours on hold.

We built Replicant to fill these gaps – to give customers instant solutions to their most common customer service issues so agents can focus on what they’re good at – fixing problems creatively and connecting with customers, knowing that transactional work is handled by the Thinking Machine™. We built Replicant to end long hold times, frequent call transfers and high customer effort. We built Replicant to give call center executives scalable, elastic customer service that can flex up or down based on customer demand. We built Replicant to solve problems as a team, to help people become more productive, and because just like you, we are satisfied when our customers are satisfied.

I thrive at the intersection of aesthetics and technology. After studying Artificial Intelligence and Machine Learning at MIT and following the invention and acquisition of Hunch, the engine that now powers billions of recommendations per day at eBay, I founded an art gallery of computer code to explore the actual and aesthetic utility of algorithms. After years of pairing traditional artisans like painters and sculptors with data scientists and deep learning specialists, and puzzled that I’d never had a very good conversation with a machine, the team and I decided to build Replicant to elevate AI to a natural, human level of communication. With a team hailing from industry, research, and linguistic conversational theory, Replicant’s holistic approach to the design, development, and deployment of the Thinking Machine™ is cutting-edge and carefully crafted to provide exceptional customer experiences.