As Physical AI moves from prototypes to deployment at scale, reliable localization has emerged as a foundational layer. Designs that worked in the lab break down when exposed to real-world conditions.  

Success requires a deep understanding of the complex interactions between sensors and software as conditions change, and translating this knowledge into a system design that delivers localization consistently and with high confidence.   

FUSE was created to help engineering teams acquire this knowledge faster. Hear directly from those who have deployed at scale as they share their architectures, design patterns, and hard-earned lessons.