by Frederick Daso
If you’re a C-suite executive or institutional investor, chances are you’ve face a problem that you personally couldn’t solve on your own. Your immediate circle of co-workers and colleagues in your industry aren’t able to find an answer either. Going through your Rolodex, you’re unable to find someone to solve your problem.
This is where expert networks come into play. These are groups of individuals who are subject matter experts in their respective fields. Hedge funds, Fortune 500 companies, mutual funds, and other entities regularly call upon these professionals to divulge help them solve problems they can’t figure out on their own. Finding the right expert for a particular problem is not easy. Companies such as the Gerson Lehrman Group function as matchmakers for their clients and experts. The expert network industry is a lucrative opportunity, generating over $1 billion in revenue throughout 2017.
Devin Basinger, Yishi Zuo, Derek Hans, and Nikhil Punwaney see a chance to disrupt the expert network industry as a whole through their startup, DeepBench. DeepBench seeks to free the “world’s knowledge by matching expert advisors with those who need their insights.”
“Our goal with DeepBench is to build a technology-driven, human-assisted platform for navigating expert networks,” Basinger proclaimed.
Devin, Yishi, and Derek are MBA students at the MIT Sloan School of Management. Zuo was the first to bring up the idea of a startup focused on matching knowledge seekers to domain experts in one of their first-year business classes. As a former hedge fund analyst, he was always in need of information to help guide his investment decisions. Basinger could relate to this problem as he came from a management-consulting firm, where his job was to provide outside expertise to companies who needed help solving problems unique to them. The two found common ground in their struggle to find the right experts to talk to for an affordable cost.
“There was a high cost to accessing expert knowledge while I worked in industry,” Zuo said.
How could Zuo and Basinger build a company that stands out from the established players? Would they be able to bring something new to the expert network industry?
Zuo posed these questions and more to his good friend Hans, also a first-year MBA student. Hans had a technology background building social enterprise mobile applications for a Fortune 500 technology firm. He believed that Zuo and Basinger were targeting a socially valuable problem, and suggested using technology to build a marketplace where clients and experts could transparently found one another in a frictionless process.
“After hearing Yishi’s idea, I knew a technology-driven approach was the way to go,” Hans said.
Zuo loved hearing what Hans had to say and asked him to join the team. Hans accepted. However, Zuo and Hans thought the team was still incomplete, thinking they could use another technical co-founder. The two were in a class called New Enterprises last spring when they met Punwaney. Unlike the other two co-founders, Punwaney was an undergraduate at MIT, majoring in Course 6-2 (Electrical Engineering and Computer Science). Punwaney was a skilled coder with a passion for creating algorithms to help build a better world. When the rest of the team pitched the idea of DeepBench to him, he immediately understood the significance of the opportunity and the impact they could have in the knowledge-sharing ecosystem. His previous experience as a software developer at a top management consulting firm and social media business networking website fit well with the team and the problem they were trying to tackle. Punwaney was onboard.
“I was enamored by the team and more specifically by the vision DeepBench had to build and develop expert networks using a technology driven approach,” Punwaney concluded.
Basinger joined on as the fourth co-founder soon after Punwaney, solidifying the DeepBench team.
The four co-founders spent any remaining time they had in the spring after classes to work on DeepBench. They were able to make significant progress throughout the summer, as entrepreneurship-focused programs provided critical resources they needed to work. MIT Sandbox provided DeepBench seed funding to get started working during the school year. The Martin Trust Center for MIT Entrepreneurship’s New York City Summer Startup Studio provided them office space and a stipend for expenses throughout the summer, as well as access to leading VC firms and industry professionals. Throughout this current academic year, The Legatum Center at MIT has graciously hosted the DeepBench team in their offices as they continue their work.
Even though all four co-founders agree that the “MIT ecosystem has been very helpful”, they still struggle to juggle the demands of academics with their entrepreneurial pursuits. The DeepBench team has been able to bring additional team members onboard, but they know they are at full strength when everyone can work full-time.
Still, the team has been able to make progress despite their classes “getting in the way”. Their long-term dream is to turn finding experts from a human-driven, technology-assisted process to a technology-driven, human-assisted process using artificial intelligence. In the future, these four individuals want to democratize the ability to find experts by having free information for everyone, and a full-service model for customers who need more detailed knowledge.
DeepBench is here to reach into the deep well of humanity’s specialized knowledge and bring it to the surface for individual and collective use.