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This startup wants to make expert networks faster and cheaper

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In finance or consulting, finding the right expert to weigh in on a problem is sometimes as tough as the problem itself. 

Just ask Yishi Zuo. While working as a hedge fund analyst, Zuo would ring up subject matter experts to help guide his decision-making. He thought the so-called expert networks he used were helpful but could be much, much better.

“I thought it was a great business model, but there were a lot of inefficiencies,” Zuo said. “And I thought that maybe one day I could build a better platform.”

Zuo enrolled at MIT Sloan in 2016 to bring his idea to life. Things started to come together when he met Devin Basinger, Derek Hans, and Nikhil Punwaney, who became not just his classmates but his cofounders, too. The trio rounded out Zuo’s experience by bringing strong technical expertise to the table. With some inspiration from classmate Jason Liu, the team decided to name their startup after a sports analogy — DeepBench — which means having a strong supporting cast of talented players.

Breaking into the market was their first big challenge. Expert networks have been around for more than 15 years, and there are already a number of players, both large and small. “It’s not easy when there are entrenched competitors in your industry, and you’re an upstart trying to win your first customer — but we did it,” Zuo said.

The company’s first client was an MIT alumnus working for a consulting firm and looking for an adviser in the health care space. Zuo connected the individual with an expert, who also happened to be one of their current MIT Sloan classmates. “It was a perfect fit for what they needed,” Zuo said.

While the team started out communicating with clients via emails and PDFs, today the company operates using a more efficient client portal. DeepBench is fast; it connects clients to experts in 24 to 48 hours, all while using algorithms to hone in on finding the perfect match.

“Our technology and processes allow us to find anyone in any geography in any industry relatively easily,” Zuo said. “That’s a strength not all our competitors have.”

The technology could also lower the cost of service. Today, a client might pay one of their competitors as much as $1,300 for an hour on the phone, according to some estimates. Zuo thinks he can decrease that by as much as two-thirds.

“Just like how Uber uses a technology platform to drastically reduce the cost of transportation, we want to use technology to reduce the cost of sourcing and matching people for paid insights, and dramatically expand the market in the process,” Zuo said.

The company, which is currently raising funds, has also been operating with help from MIT Sandbox, which provides funding for student entrepreneurship projects. The Martin Trust Center for MIT Entrepreneurship and the Legatum Center have also provided support through resources and programming for students, as well as office space.

Zuo, who graduates this year, sees DeepBench growing beyond matchmaking. The team wants to expand the market so that anyone beyond DeepBench’s enterprise customers, such as journalists or consumers, could use the service to find the insights they need at an affordable price. They plan to create online curated private discussion groups that will keep people connected to topics they care about.

“In the long run, we want DeepBench to be a knowledge platform that connects anyone to any type of information,” he said. “People go straight to Amazon when they want to look up a product. They don’t even start on Google anymore. Our dream is to have people go to DeepBench when they want to learn about anything from an expert.”

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