PreSeries wants to make it easier for startups to get funded

The automated platform makes the process more analytical.

By Kara Baskin  |  October 26, 2017


PreSeries for Alexa judged a startup competition at the recent Predictive Applications and APIs conference.

Why It Matters

When deciding whether or not to invest in a startup, venture capitalists sometimes rely on instinct or prioritize companies from people they know. Introducing analytics into the process can help them better assess a new company’s potential.

Judging the viability of an early-stage startup is a somewhat subjective process.

One company, PreSeries, has created a machine learning-powered platform to turn it into an objective, data-driven practice.

PreSeries works by analyzing data about startups to predict which companies have the most promise from an investor’s standpoint. It uses data provided by the companies and gleaned from public information available in databases like Crunchbase.

The platform appeals to investors and start-ups alike, says PreSeries co-founder Arturo Moreno, MBA ’17. “We use metrics based on questions that venture capitalists ask when they want to evaluate a startup. How long has a company worked together? Is it someone’s first time as an entrepreneur? These questions can be answered with data,” he says.

Each company listed in PreSeries has a profile page with quantitative metrics regarding funding, employee count, how many companies the founder has successfully launched, and how many years of experience the CEO has. PreSeries also issues predictive scores that represent the company’s likelihood of success and estimates the potential time frame of a successful exit for an investor to realize a return. Additionally, investors can see lists of both competitors and companies with comparable financing, can search companies by region and specialty, and see where startups rank with regard to concept, seed money, product development, and more.

Moreno says that, until now, due to the subjective nature of venture capital investing, many worthy projects didn’t get funding.

“We aim to create a more analytical, data-driven fundraising experience. Sometimes an entrepreneur tries to raise money, and it takes so long that their idea dies. We want to foster more efficient terms,” he says.

The platform holds value for entrepreneurs, as well, who can use it to assess their idea from a range of metrics and to compare themselves to like-minded companies. They can also compare investors, see how many companies they have invested in, and how much those investments were.

In additional to accessing the PreSeries dashboard online, users are able to interact with it by voice when the PreSeries for Alexa skill is enabled on an Amazon Echo. The platform can score startups in real-time by asking questions like: how old the CEO is; how many co-founders there are; how many years of experience the CEO has; and how many funding rounds have been completed. Alexa asks fewer questions than the full dashboard requires, but enough to make an initial assessment.

PreSeries was on display at the Predictive Applications and APIs conference at the Microsoft New England Research and Development center in Cambridge on Oct. 24. Using the PreSeries for Alexa skill, the platform scored four startups by asking their human representatives a number of rapid-fire, concise questions designed for easy interpretation.

PreSeries then ranked each company based on their answers. Greensight Agronomics — an automated, agronomic intelligence platform — ranked highest with 95.32 points.

In the future, Moreno hopes to add internal data to the ranking process and to possibly expand the Madrid-based PreSeries into Boston. And, of course, he hopes that it makes investing more seamless.

“Most investors don’t have time to research; they prioritize someone they know. We want this to make this whole process more analytical,” Moreno says.