Rupak Doshi is a scientist-turned-entrepreneur and the co-founder & CEO of OmniSync, creators of the flagship platform TurboInnovate. With a background spanning research at Cambridge, Scripps Research, and UCSD, as well as industry experience in biotech drug development, Rupak has spent the last decade helping deep tech ideas move from lab benches to global markets. Through OmniSync, he has helped unlock more than $4 billion in federal innovation capital for startups and built tools that streamline commercialization pathways using AI.

In this episode, Rupak shares how his journey from academia to entrepreneurship shaped his vision for democratizing innovation. He discusses the challenges of bridging science with business, his unconventional approach to hiring, and why OmniSync is creating a category-defining platform to accelerate deep tech commercialization.

In Today’s Episode We Discuss:

00:01 – Introduction to Rupak and OmniSync’s mission

00:52 – Growing up in India, early love for biology, and pivot to biotech

02:00 – Research journey: UK master’s and PhD at Cambridge, postdoc in San Diego

03:07 – Moving into biotech industry and the importance of partnerships

04:30 – Founding OmniSync and the aha moment that shaped their mission

06:19 – Building company culture: hiring for drive, not resumes

08:01 – Signals of hustle and how OmniSync identifies talent

12:51 – Biggest lessons transitioning from academia to entrepreneurship

16:16 – The origin and evolution of TurboInnovate

20:28 – End-to-end user journey for startups on the platform

22:26 – Customers: universities, startups, Fortune 1000, and government

24:09 – Defining a new category of innovation intelligence

25:10 – Personal story: tattoo, family, and passions outside of work

26:24 – Competing in a crowded “AI for research” market

29:27 – OmniSync’s diverse cap table: blending venture, government, and academia

31:21 – Go-to-market: why conferences are OmniSync’s highest-ROI strategy

34:01 – A hard pivot: moving from human experts to fully automated AI workflows

37:04 – What’s next: building autonomous commercialization feedback loops

42:01 – Looking ahead with AI

44:52 – Transition to rapid fire

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