Team behind Character venture capital fund launches new pre-seed program

More than two years since the founding of Milwaukee-based venture capital fund Character by John Zeratsky, Eli Blee-Goldman and Jake Knapp, the team has unveiled an expansion of their venture capital efforts.

A new program called Character Labs will help pre-seed founders as they work toward finding a product-market fit.

“Before we invest, I want to show founders what it’s like to work with us,” said Zeratsky. “Not just that we can ask good questions on a call, but that we have something unique and valuable to offer. In return, I want to see a founder in action.”

Zeratsky spent time working for YouTube, Google and VC fund Google Ventures before co-founding Character with Knapp and Blee-Goldman in 2021. Blee-Goldman is a former general partner at Capital Midwest Fund. Knapp also spent time at Google and Microsoft.

Character helps businesses find and expand product-market fit, using the so-called design sprint process Zeratsky helped develop at Google Ventures.

Character Labs will allow startups to quickly test their product and go-to-market strategy through a series of back-to-back sprints. The idea behind a sprint is to quickly build and test a prototype in a matter of days. Participants will leave the program with realistic prototypes and a detailed scorecard of validated risks.

Every startup in Character Labs will receive a $100,000 investment in exchange for 3% equity. Only five companies will be accepted into the Character Labs cohort. The next program is geared toward AI startups, but startups not completely focused on AI can apply. Character Labs will take place remotely and is open to U.S.-based companies.

“We are still seed investors at the core. This program is an extension of our strategy, not a pivot. And we’ll continue to invest most of our fund dollars into startups at that ‘version 0.9’ stage,” said Zeratsky. “But I think Character Labs makes us better seed investors, by showing us how teams work up close, and allowing us to continue to build pattern recognition for what works and what doesn’t at the early stages.”