About
Throughout my career, I’ve been driven by one question:
How do we make AI systems dependable enough for production?
I began my career building performance-critical infrastructure for telecommunications and banking, where reliability, scalability, and efficiency weren’t optional; they were fundamental requirements. That systems-first mindset continues to shape how I approach AI today.
Over the past decade, my work has spanned the evolution of AI, from production machine learning to multimodal perception and now to Physical AI.
At EMC Insurance, I led the adoption of machine learning, computer vision, and NLP across core business operations, delivering production AI systems for predictive modeling, risk analytics, document processing, and fraud detection while partnering with executive leadership on AI strategy and governance.
At Orbital Insight and Privateer, I led AI model engineering for large-scale multimodal Earth and space intelligence systems, overseeing R&D and production across a team of computer vision scientists and engineers. My work ranged from developing novel algorithms to delivering operational AI systems for commercial and government customers.
Today, at Labelbees, we’re building an operating platform for Physical AI. We help robotics and AI teams build, operate, evaluate, and continuously improve AI systems in the physical world.
I believe the next frontier of AI isn’t just better models; it’s better systems. The AI systems that succeed will be those that can learn from the physical world, continuously improve, and operate reliably in production.
Beyond building products, I enjoy sharing what I learn with the broader AI community. I’ve been invited to speak at international conferences and universities, including Embedded Vision Summit, GEO Business (London), and Deep Learning World, on topics spanning Computer Vision, World Models, Geospatial AI, and Physical AI.
If you’re building the next generation of robotics, Physical AI, or production AI systems, I’d be glad to connect.