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Using AI to Match Investors and Entrepreneurs

Twelve months ago, Dan and I decided to turn our attention to a problem that almost all entrepreneurs face - how to identify the best investors for a startup, based on the company's location, stage, sector, and other characteristics.

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Not all entrepreneurs struggle to find the right investor - you would rightly expect serial entrepreneurs based in Boston or the Valley to have the opposite problem.  But most young founders don't know where to start - and are either overwhelmed by the sheer number of angels, funds, and other funding sources in the market, or facing the opposite problem: not knowing who exactly is active in their market or sector.

Research can't always supply the needed answers.  Many investor websites are out of date, and while some investor groups have adopted tight written mandates and their portfolios reflect them, the strategies for many others are less well-articulated, and can involve a subconscious adoption of the famous TMZ ("Thirty Mile Zone") rule, and a propensity to invest behind biases inherent in the partner's backgrounds, rather than the more broadly-stated investment strategy.

[One not-so-well known example involves the (hidden?) make-up of many local angel groups, which almost always include disproportionate numbers of local doctors and dentists - individuals that have made their money from healthcare, who are therefore much more likely to sponsor healthcare-related startups.]

The good news is, with the amount of data we've gathered over the past three years, this problem is not just solvable using AI and deep learning, it can be solved really, really well.  The result is our AI-powered investor-entrepreneur mandate matching engine - a technology that matches entrepreneurs with investors, anywhere in the world, based on their location, industry sector, stage, and approximately 400 other attributes.

Our system creates a ranked list of investors and provides an "Investor Match" score based on hundreds of factors, the most important of which are whether or not the investor is a match for the stage the company is at, whether the investor has invested in (or nearby) the city the startup is located in - and whether they have experience investing in a sector related to the startup's technology or target market.

A few months ago, we decided to take the additional step of creating a ranked list of Potential Acquirers, based on each acquirer's 20 year history of buying or merging with technology companies.   

After two months of testing, we've made both technologies available to all syndicates (and users) of our platform.  We look forward to getting your feedback - and to hopefully helping entrepreneurs more easily locate the groups that could be (and should be!) investing in their startups.

Sharp
John Sharp
Hatcher+

John is a serial entrepreneur and investor, and the Founding Partner of Hatcher+, a next-generation, data-driven venture firm that utilises a massive global database in combination with AI and machine learning-based technologies to identify early-stage opportunities in partnership with leading accelerators and investors worldwide.