Lunchclub, An AI Relationship Curator Startup, Raises $4M From a16z To Connect Professionals Offline

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For professionals who are looking to get ahead in their careers, find funding for their startup, or explore new business ideas and partnerships, current existing professional social networks like LinkedIn make connecting with the right people in person difficult. Furthermore, LinkedIn does not have dedicated features to helping transform online connections to offline relationships. Other products that act as personal CRMs require a lot of upfront work from the user to add value in their professional lives. However, LinkedIn was purchased for $26.2B by Microsoft in 2016, which indicates that there is a lucrative, adjacent opportunity around connecting people offline in a seamless, efficient manner at scale.

Leibson says, “We live in a network-bound referral-based society where people tend to pattern match in ways that end up giving more opportunity and dollars to people with similar backgrounds. Most investing and hiring processes leverage personal networks, which excludes industry outsiders and underrepresented communities.”

Lunchclub’s mission is “to power a future of work where making new connections is easy, meaningful, and fun.” The startup addresses this problem and its subsequent $1B+ market by leveraging machine learning (ML) to churn through user’s data across private and public domains to produce curated connections. The onboarding process is simple, as it only takes five steps to complete and begin using the product. The user answers a series of questions, fills out a short bio, and selects the times and neighborhoods they are available. The startup’s machine learning algorithms use three sorts of data: the user-generated data provided during onboarding, as well as public and private information related to the user. Lunchclub’s ML algorithms then determine the mutual relevance between two potential connections while avoiding adverse selection.

John Peurifoy, CEO of Floating Point Group, had this to say about using Lunchclub: “[The company] has helped us in fairly tangible ways (i.e., we met one of our best and closest advisers through Lunchclub in NYC, we have met countless students and candidates for recruiting, and overall we have had some great experiences). I personally keep coming back to the product to meet new people - not in a superficial manner of ‘Oh, I just want to learn about someone and meet them,’ but in a real, instinctual, and intentional way. I would compare [the experience] to being in school, and particularly being a part of a very high achieving group (i.e., MIT). Everyone you meet on there is fascinating…it’s challenging to get that after once you enter the real world. Why would I spend an afternoon talking to someone whom I may never speak to again? Surprisingly, that is one of the most satisfying things ever.”

Sam Udotong, an avid user of Lunchclub and CEO of Fireflies.ai, gushes about the startup. “Lunchclub’s algorithm is amazing. It usually matches me with a high-quality contact who is directly relevant to me at that time, whether we’re hiring or fund-raising.”

If Lunchclub’s ML algorithms find that there is enough relevance between the two users’ sets of aggregated data, then the match is made between the two. The users connect, and the only thing left to determine is a meeting spot within a neighborhood, as the other logistics have already been confirmed through the onboarding process. After meeting, users can leave feedback on how the meeting went and the quality of the connection established, which Lunchclub uses to improve its matchmaking process via collaborative filtering, which is an unsupervised machine learning technique that allows one to make automatic predictions based on preferences and tastes of users.

“The key to our product’s value is having access to good data,” says Lunchclub’s CEO, Vladimir Novakovski. “More importantly, none of the user’s private data is shared. We don’t sell it to third parties. We only rely on the aggregated information to make matches. With the new round of funding, we are using the money to expand our teams across engineering, product, and community to continue to expand globally and create value for our users.”

The team is composed of Novakovski, COO Hayley Leibson, and CTO Scott Wu. Novakovski previously was CTO of Euclid Analytics, which was acquired by WeWork in 2017, and a former member of Quora’s machine learning team. Leibson founded Lady in Tech, a tech and lifestyle blog for women and an Entrepreneur in Residence at Runway. Wu was a former software engineer at Addepar.

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