CDL’s AI stream propelled Seqera Labs’ growth as the company built its data processing software
Evan Floden didn’t know the COVID-19 pandemic was coming when he and his Seqera Labs co-founder Paolo Di Tommaso joined Creative Destruction Lab (CDL)’s AI program in 2019. But he knew that their open-source software, Nextflow, was popular and that there was commercial interest in the project.
The software deploys and manages custom pipelines to process complex data. It enables scientists and developers to chain together other bits of software that are used for applications like genomic analysis and analysis of DNA, among many other applications. It can run in any location, which has allowed governments around the world to get overnight identification of variants of the novel coronavirus that causes COVID-19. Seqera Labs’ software led to the identification of the alpha variant, for example.
“It’s making a big difference, and it’s still taking place today,” says Floden, who is now Seqera Lab’s CEO.
Floden was still a PhD candidate when he and Di Tommaso started working on Nextflow in 2014. They incorporated and began to turn the project into a company while still transitioning out of the “research world,” Floden says.
The team was just getting started on seriously building Seqera Labs as a venture when they started CDL in 2019. CDL offers a nine-month, objectives-based program to help science- and tech-based startups scale their businesses.
Floden’s background was in biomedicine and biotech, and Di Tommaso’s background was in computer science. The Barcelona-based team had raised about €100,000, just enough to build a MVP with a contractor.
“I’d worked in a startup before, but I think being on the founder side was a completely new experience,” Floden says. “Pretty much everyone at CDL could grasp the importance of the deep tech side of this. And they were willing to mentor you on the bits that you needed mentoring on.”
CDL mentors helped the Seqera Labs team with practical business skills like building pitch decks and storytelling. They also exposed the scientists to the language of venture capital.
“That’s actually one of the biggest hurdles, the whole language of VC. If you come at it green, it’s very difficult to get there,” Floden says.
“Someone says, ‘What do you expect your sales to be?’ And you’re just like, “I never bought enterprise software before, how am I going to sell X?’ It’s really key things like that. Getting some expectations from other people helped a lot.”
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