Q things like that. I also think about obviously someone that might've built the, you know, inventory, you know, robotic system to manage Amazon warehouses, to get delivery time down by a millisecond, because then we spend more. I think about someone like you work with DARPA, right? You know, great university going to, I mean, why is your background and your team sort of best suited to win this space?
A Two things. The three co-founders, Daniel Gitesh and myself, like you mentioned, worked on the world's first full autonomous helicopter, won national awards for the work we did with aerial autonomy. So we had deep expertise in physical AI for aerial autonomy, and my thesis focused on how to make robots curious, and they're curious here in warehouses about boxes, barcodes, inventory, and workflows. So that, that makes the technology alignment quite strong. And the second thing is over the last six or seven years, how our stack has evolved. Now our stack is the only stack in the world that you can put on a moving camera that you buy out of Best Buy and turns it into an autonomous data gatherer. So that's the technical side of it where we have a data moat and a tech moat of, of given our technical backgrounds. But also over the years, We have assembled a bench of people from Amazon, from Uber, from, uh, Secret with deep logistics background. So our product has evolved to support logistics players in how they want to be supported. So I think those two combinations give us, uh, an unfair advantage to, to serve effectively in this space.
AI assessment note: “I think those two combinations give us, uh, an unfair advantage”