Case Study - Automating Investor-Founder Matching

//DealFuze case study

Overview

The art of investor-founder matching comes with endless spreadsheets and countless hours spent trying to find the perfect fit. It's not just about industry, company stage or investor involvement but also if the party's personalities align. We were approached by DealFuze to partner and create a solution to this problem by crafting a product that acts as a fractional investment analyst, with the goal of automating the deal sourcing pipeline by optimising investor-founder fits to drive successful funding opportunities.

The core of the solution is a matching algorithm that is powered by AI. We wanted the product to be as frictionless as possible to integrate into existing workflows, and also assist in data sourcing and profiling. Each founder and investor is mathematically compared on factors such as industry, experience or investment preferences, and this decision is supplemented by a personality profile generated for each person. The personality profile is AI-engineered through analysing their LinkedIn activity and the responses they provide in their DealFuze profile, enhancing decision-making by combining quantitative analysis with qualitative behavioral insights.

What we did