Refolo

Lola Ojabowale's dad was diagnosed with cancer and had to drastically change his diet, developing sensitivities to some of his favorite recipes. Figuring out what to eat became a struggle. "I thought there had to be a better way to figure it out," Lola recalls, "so I started working on Refolo." At the time, she was managing teams at an eCommerce company and studying math and engineering—not food or health. But the personal problem felt urgent enough to build a solution.

Lola started on Refolo while taking a UX design class part-time, using it as her final project and conducting initial user interviews. The path to MVP was iterative and scrappy: first an iPhone app in Swift, then a Coda doc, then WordPress with plugins, and finally a Bubble app. "It took a year from when I first had the idea to when I had something people could use," she admits. The business model was straightforward—a $15/month subscription for customizable plant-based recipes with shoppable ingredients delivered via Instacart or Amazon Prime.

Lola held a meetup for plant-based eaters in Atlanta that grew to over 100 members, which became a source of initial testers. She also cross-promoted and held virtual events with plant-based influencers on Instagram. "It was fine to get people to sign up but didn't attract any paying users," she reflects. There was "a lot of fake traction"—getting into an entrepreneurship program, collaborations—but none of it converted to revenue.

After two years and "a few thousand dollars" in expenses (mostly hosting at $30-$40/month plus events and conferences), Refolo never gained traction. The core issue: "I didn't have a repeatable process for finding people who wanted to pay for the product." People could find meal-planning information for free and were satisfied with free alternatives. Six months after leaving her job to focus full-time, with no revenue and no promising signs, Lola shut it down. "Selling a solution for a problem that people weren't already investing money to solve" was a fatal flaw.

Lola identified the root causes: poor maker/problem fit, launching too early without experimentation, and targeting a problem without existing willingness to pay. In retrospect, she would "invest in growing a community around the problem first and then figure out what products are needed" rather than starting with assumptions based on one person's experience. She'd also run many small experiments instead of betting everything on one vision.