People often say, “Just build an AI product.”
It sounds simple, almost casual — as if all you need is a weekend, a laptop, and a model.
But building one—truly building one—stretches every part of you. Your architecture choices get tested. Your patience gets tested. Your discipline, your stamina, your optimism… sometimes even your health.

In the last year, I built three end-to-end healthcare AI tools — NephroCompass, Carelytics (Hospital BI), and a Blood Donation Decision Support System — mostly by myself. I wrote code between doctor’s appointments. I trained models while waiting for lab results. I designed product flows on days when recovery felt heavier than usual. And I did it while job searching, rebuilding my life after a kidney transplant, and trying to create something that might make the journey a little easier for the next patient.

What this year taught me is that you don’t need a large team or a perfect dataset to build something meaningful. What you do need is clarity, courage, and the humility to let constraints become your best teammates.

This talk isn’t a highlight reel. It’s the honest, backstage story of what it takes to build startup-ready AI products when it’s just you, your determination, and a problem you refuse to ignore.

• How to design an MVP that doesn’t fall apart the moment a clinician or patient touches it
• How to move fast when your data is limited, messy, emotional, and deeply human
• How to label data without a room full of annotators
• How to evaluate models when “good enough” isn’t good enough — not in healthcare
• How to choose deployments that make sense when you’re one person, not a 50-engineer team
• How to define ROI early so your product doesn’t drift into “cool demo” territory
• And the part no one talks about: building while healing, while doubting, while starting over — and still believing that it matters

This isn’t a product demo.
It’s a field guide — part architecture, part scrappy tactics, part survival manual — for anyone building AI in the real world with limited resources, imperfect data, and a heart full of stubborn purpose.

If attendees walk away with new frameworks, great.
But I hope they walk away with something deeper: the reminder that meaningful AI doesn’t begin with a big team or a giant budget. Sometimes it begins with one person, one lived experience, and one quiet decision to try anyway.