I was trained in politics before I ever built products. I learned how decisions get made, who holds leverage, and how narratives move people. Now I build systems instead of speeches.
When I was sixteen, I was the youngest person in a room full of grown adults fighting over policy I’d written. FOX News was there almost every meeting. The controversy was real—not performative, not symbolic. It was about resource allocation, equity frameworks, and who gets to decide how a district serves its students. That experience taught me more about how institutions actually operate than anything else in my life.
I spent a lot of time after that figuring out what I wanted. I followed my intuition, my passion, and my goals—from the state capitol to Harvard to San Francisco. Now I’m building Intera.
Keep readingClinical trials lose roughly 30% of their participants before the study is over. Not because patients don’t care—because the experience breaks down in ways that are predictable but unmeasured. Burden spikes. Expectation mismatches. Trust erosion. Life getting in the way.
The industry has strong tools for finding patients and getting them enrolled. But there’s still no standard way to forecast whether the people who sign up will actually stay. That’s a behavioral science problem. It requires different data, different models, and a fundamentally different approach than what exists today.
Intera takes trial protocols—schedules of assessments, consent language, site constraints, patient communications—and produces a retention risk forecast: where drop-off is likely to happen, which patient segments are most at risk, and why.
Then we run counterfactuals. Change the visit schedule, adjust the onboarding, shift how reimbursement is communicated—and see how predicted retention moves. The goal is to make drop-off something you can manage before it happens, not something you react to after it’s too late.
The model gets better over time because we embed real patient interviews into live trials. That data—why patients enrolled, what’s hard, what would make them stay, matched to actual outcomes—doesn’t exist anywhere else. Every engagement makes the forecast smarter. Competitors can’t replicate it because they’re not collecting it.
We’re not trying to replace clinical research teams or claim we’ve solved retention. We’re giving them a forecast they’ve never had—directional, grounded in real behavioral data, and useful early enough to actually change outcomes. The work is hard and the problem is genuinely complex. That’s why it’s worth doing.
At sixteen I was writing policy for my school district—resource allocation, equity frameworks, student wellness infrastructure. The policy was controversial. FOX News showed up almost every board meeting. Grown adults—parents, administrators, local politicians—pushed back hard. I read every angry email. Then I sat down with the people who disagreed most and found common ground I didn’t expect.
That experience didn’t make me cynical. It made me pay attention. I learned how power operates in a room, how people make decisions when they’re afraid, what it actually takes to move policy through a system that doesn’t want to move.
I spent a lot of time after that figuring out what I wanted. I taught myself to code at the public library, went to the state capitol, went to Harvard, followed my intuition into rooms I didn’t belong in until I did. The through-line has always been the same question: how do you understand human behavior well enough to make better decisions before the damage is done?
I believe the right way to build a company is being people-first. If there’s a reason we shouldn’t be talking, I’d rather find out in five minutes so we can both spend our time on something better. And if there is a reason to talk—I want to hear what you’re working on.
I’m especially interested in conversations with people in clinical operations, patient engagement, site networks, behavioral science, or anyone who’s thought about why systems built for humans so rarely ask humans what they think.