Cohort — A New Professional Social Graph

“Professional? Social? Isn’t that what LinkedIn is for!” Nope.

Eamon Leonard
Nov 18, 2016 · 8 min read

Couldn’t we think of something easier to work on?

When I started thinking about this space a few years ago, I wanted to make it really simple to make introductions. At first, I thought the best way to do that was to automate the double opt-in intro. I soon realised, however, that before you can introduce two or more people, you really need to understand the world they live in, all the moving parts around them, the hows, whys and whens.


  1. Reach. Even if we have a reasonably good idea of who we have social capital with, it’s hard to discover that about their friends. The very best human networks are built on strong Friend-of-a-friend relationships, yet it is surprisingly difficult and time consuming to understand not only who knows who, but how well they know them, what those people are knowledgable about or have interests in, and how can that be leveraged to a positive outcome.
  2. Application. Because of fragmentation and lack of insight to reach, it is hard to know where and how to apply the social capital we have worked hard (whether through conscious effort or not) on acquiring through out our lives and careers.

100 million people & 700 million relationships…

…and growing

  • Natural Language Processing — mapping natural language needs to queries, to surface relevant helpers.
  • Deep Learning — used in both conceptual search and natural language processing, deep learning is an advanced machine learning technique we use to form contextual connections between terms.
  • Classical Machine Learning — a number of models we use for relationship and knowledge prediction draw on well-known machine learning techniques.
  • Social Network Analysis — we analyse people in the context of their networks, detecting communities they belong to and their importance to those communities.

Making the complex, simple and immediately useful.

  • Framing network search around problem solving. i.e “I’m looking for someone who can help me with sales” or “I’m hiring a product manager with mobile experience in San Francisco”, — searches can be interpreted by the machine to make recommendations, but also read by people in your network, paving the way for a more social search.
  • Using a Broker-Helper paradigm as a means to present options for solving the problem. This makes it easier for the user to sort through people who can potentially help out.
  • Team collaboration, where a number of friends can combine their networks to achieve their goals, effectively piggy-backing on favour exchanges made by others in their team.
  • Building in reciprocity, where we balance the idea of self-interest with helping those in your community and extended community. This is such a fundamental part of good networking practice, and Cohort is able to show users how they can help those close to them.
On show here: social search, broker-helper recommendations.

A 52-week sprint 😱

We knew this was a big problem to solve, and we expected it would take us time to get to a point where we could demonstrate power, utility and ease of use. If we were to start on this problem today, knowing what we know now, it probably would take less time, but not by much.

Before I go…

As an early stage startup, we mainly exist because a handful of people were able to see a better world with Cohort in it. Shout out to Jonathan, Martin, Curt, Karl, Dave, Dylan, Harper, Joe, and Sean — the true believers ✌️💰


If you’d like to sign up to Cohort’s beta, you can do so here.

Cohort Analysis

A collection of thoughts, ideas and experiences from the team building Cohort. We help you find the people you need, through the people you already know and trust.

Eamon Leonard

Written by

Dubliner. Fan of whiskey, conversation and good people — www.eamo.net

Cohort Analysis

A collection of thoughts, ideas and experiences from the team building Cohort. We help you find the people you need, through the people you already know and trust.