About Our Guests
When Thomas "T-Rob" Robinson joined Domino Data Lab eight and a half years ago, the company was focused on helping pharmaceutical companies and financial institutions build better AI models. With a background in math, computer science, and enterprise finance technology, he understood that the hardest problems in AI weren't about algorithms - they were about enabling domain experts to build trustworthy systems at scale. Meanwhile, Joel Meyer was at the Department of Homeland Security, leading their first AI task force and grappling with fundamental questions about how government could effectively adopt AI technology. Joel, like a number of government-to-startup guests before him, recognized a vendor that held the potential to address the very problems he was working from the inside. Today, T-Rob and Joel are working toward the shared vision of bringing enterprise-grade MLOps to critical national security missions.
About Domino
Domino Data Lab built its reputation helping some of the world's most demanding organizations develop AI systems. Their platform enables deep domain experts to build competitive models while maintaining rigorous governance - essential for industries like pharmaceuticals where mistakes can cost billions. This combination of speed and trust have put them in a privileged position as the advent of new AI models have sparked urgent questions across the government. Today, Domino provides the infrastructure and expertise to help organizations build and deploy AI systems they can trust - with the belief that general models are insufficient for highly targeted use cases.
Key Takeaways
Start With Commercial Traction: Domino's path demonstrates why proving your technology in commercial markets first creates credibility that translates to defense. By solving hard problems in regulated industries like pharmaceuticals and finance, they built evidence of capability that helped overcome the "new vendor" barrier in defense. For founders, this means focusing on sustainable commercial revenue before attempting the longer defense sales cycle.
The Platform vs Product Decision Matters: Early on, Domino chose to be a platform that enables others rather than competing with their customers by building specific AI models. This strategic choice meant they could partner with major defense contractors instead of competing with them, significantly accelerating their market entry. The lesson: sometimes enabling others to succeed is a faster path to scale than trying to own the entire solution.
Cold Submission Can Work - If You're Solving a Real Problem: Despite conventional wisdom about needing to shape requirements, Domino won their DIU contract through a cold submission. This worked because they had already solved similar problems in commercial markets. For founders, this means product-market fit matters more than relationships - if you're genuinely solving a critical problem, there are paths to market even without extensive defense networks.
Integration Creates Moats: When DIU selected five companies to work together, Domino focused on being the integration layer rather than competing on individual capabilities. This position made them essential to the overall solution while allowing others to excel in their specialties. The lesson: in complex systems, the integrator often captures more value than point solutions.
Production ≠ Scale: While winning a production contract was significant, Domino is clear-eyed that it's just one step toward sustainable defense revenue. They're still working toward being programmed in budgets and achieving reliable funding streams. Crossing the "valley of death" isn't a single event - it's a series of increasingly meaningful milestones that each require distinct strategies.
For more about Domino: domino.ai
For more about T-Rob: https://www.linkedin.com/in/thomassrobinson/
For more about Joel: https://www.linkedin.com/in/joeltmeyer/
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