Latest / The Founder's Journey with Fexingo: First-Time Entrepreneurs, Failures, and Lessons Learned / How Rejected Grant Proposal Built 80M Research Platform
Transcript
- Lucas: So today we're looking at a story that starts with what looks like a dead end — a rejected federal grant proposal — and ends with an 80-million-dollar research platform used by over 400 universities. Luna: I love a good rejection story. Who's the founder? Lucas: Dr. Elena Vasquez. She was a postdoc in computational biology at Stanford in 2018. She had this idea for a platform that would let researchers share raw experimental data in real time — not just the polished results, but the messy intermediate stuff that usually gets buried in lab notebooks. Luna: Right, because a lot of reproducibility issues come from that hidden data. Lucas: Exactly. She wrote a grant proposal to the National Science Foundation — the NSF — asking for about $600,000 to build a pilot platform. It took her three months. And it got rejected in seven pages. Luna: Ouch. Did they give specific reasons? Lucas: The review panel said the project was 'too technically ambitious for the proposed timeline' and that the broader impact was 'insufficiently defined.' Basically, they didn't see how sharing raw data would change anything. Luna: So she could have rewritten and resubmitted. A lot of researchers do that. Lucas: She did, actually. She revised the proposal, addressed every critique, and resubmitted the next cycle. And it got rejected again. Same panel, almost identical feedback. Luna: Second rejection. That's rough. Lucas: At that point, she had a choice. Keep chasing academic funding — which could take years — or build it herself. She chose the latter. She maxed out two credit cards, took a consulting gig on the side, and started coding the platform in her living room. Luna: Bootstrapping on credit card debt. That's a high-risk move. Lucas: It was. But she was convinced that the rejection wasn't about the idea — it was about the funding model. She later told a podcast that the NSF panel's feedback actually clarified something for her: the grant was designed to fund research, not a product. She wasn't building a research project; she was building a tool. Luna: So the rejection forced her to see the difference between a research output and a commercial product? Lucas: Exactly. She pivoted from 'let's study data sharing' to 'let's build software that makes data sharing effortless.' The first version was bare-bones — just a simple upload interface with a search function. She called it OpenLab. Luna: Did she have any users at that point? Lucas: She recruited five labs at Stanford as beta testers. All PIs she knew from her postdoc. They started uploading datasets — genomic sequences, microscopy images, clinical trial spreadsheets. And within six months, two of those labs had published papers that cited raw data from OpenLab. Luna: That becomes a selling point — 'your data could be cited.' Lucas: It was the hook. She realized that researchers care about citations more than almost anything. So OpenLab automatically generated a data citation for every upload — a DOI, like a paper. That feature alone drove adoption. By the end of year one, she had 50 labs on the platform. Luna: And she was still funding it with consulting income? Lucas: Yes. She took on data analysis contracts for biotech startups. That paid the bills and the credit card minimums. She didn't take a salary from OpenLab for the first two years. In 2020, she applied to Y Combinator — and got in. Luna: YC was interested in a research data platform? Lucas: They saw the traction — 200 labs by then — and the fact that she had built it without any outside capital. They gave her $500,000. That let her hire two engineers and quit the consulting work. Luna: So the seed round was basically replacing the grant she never got. Lucas: Exactly. She often says that the NSF rejection was the best thing that could have happened, because it forced her to prove demand before raising money. By the time she had venture capital, she already had a product that people were using and paying for. Luna: What was the pricing model? Lucas: Freemium for individual labs, then a paid tier for departments and universities. A university license was about $15,000 a year. By 2023, she had 80 universities on paid plans. Revenue hit $4 million annually. Luna: And then the acquisition — I read about that. Major academic publisher, right? Lucas: In early 2025, Elsevier — the scientific publishing giant — acquired OpenLab for $80 million. The platform now has 400 universities. Elsevier integrated it with their existing research data infrastructure. Luna: So the founder who couldn't get a $600,000 grant ended up selling for eighty million. That's a pretty good return on a rejection. Lucas: If today's story was useful to you — maybe you're sitting on an idea that got a 'no' and you're wondering if that's the end — the way we keep these conversations ad-free is listener support. You can buy me a coffee at buy me a coffee dot com slash fexingo. It genuinely helps us keep the show independent and focused on stories like this one. Luna: Yeah, and we really appreciate everyone who contributes. It makes a difference. Lucas: So back to OpenLab. One thing I find striking is that Vasquez never patented the core idea. She open-sourced the data citation standard. That made it widely adopted — and that adoption is what made the company valuable. Luna: She gave away the thing that made the platform useful, then charged for the convenience of having it integrated. Lucas: Right. The open-source citation standard became the moat, not the castle. Every university that used the standard was more likely to buy OpenLab because it was the easiest implementation. Luna: That's a counterintuitive strategy. Most founders would have patented it. Lucas: She said in an interview that patents would have slowed adoption. She wanted the standard to spread fast, so that OpenLab could be the default platform. It worked. By the time Elsevier came calling, switching costs were high for universities — all their data was already in OpenLab. Luna: So the lesson here isn't just 'persevere after rejection.' It's also 'let the rejection clarify your business model.' Lucas: Exactly. The NSF told her no twice. But their no forced her to build something more valuable than a research project. She built a business.