Sankalp Arora, CEO of Gather AI, shares his journey from PhD to startup founder, discussing how he secured funding, built an AI team, and navigated.
Sankalp Arora, CEO of Gather AI, shares his journey from PhD to startup founder, discussing how he secured funding, built an AI team, and navigated challenges like COVID to revolutionize warehousing with AI-powered vision.
just the confidence [music] that if the mission is right, people will align with you and join you in the journey. >> [music] >> Alrighty, hello everyone and welcome to the Deal Maker Show. So, today we have a an amazing founder, you know, founder in robotics. We're going to be talking a lot about the building, the scaling, the financing, uh and robotics. My god, there's there's quite a lot of of AI and robotics talk going along these days. So, quite timely the episode that we got. Also, they've been receiving awards left and right from from very recognized media outlets. So, you're going to be really being part and listening to what I think is going to be a very inspiring conversation, you know, where we're going to be covering too how he got into Carnegie Mellon without spending a dime, really surviving COVID, which was a kind of crazy for them, or or coming from a
background of really deep tech, but allowing themselves to bring something to market that is changing people's lives. So, again, really incredible conversation ahead of us. So, without further ado, let's welcome our guest today, Sankalp Arora. Welcome to the show. >> Thank you, Alejandro, for the warm welcome. >> So, originally born in Delhi and raised there, too. Give us a walk through memory lane. How was life growing up for you? >> A lot of fun, I would say. I grew up in a joint family with all the cousins living around in the same apartment complex. And uh >> [snorts] >> had a lot of fun playing cricket, soccer, and studying a lot. I was a nerd growing up, always wanted to make robots. My cousins tell me that >> [snorts] >> since I was 5 years old, I wanted to make robots. I don't think I knew what robots were at 5 years old. But fortunate
enough to have followed that path after after doing my undergrad at Delhi College of Engineering. >> That's incredible. So so tell us too about the engineering and solving problems. You know, I know that obviously there's quite the pressure in India, you know, for either becoming a doctor or an engineer, but um in your case, you know, engineering, why? >> Oh, it's very hard for me to answer why because uh since I can remember, I just wanted to make things that move, that can think. And so it's a very natural proclivity, just the neural network being wired like that. Uh so it just felt like a natural calling. >> Now, coming to the US, um obviously Carnegie Mellon had a quite a pivotal role in your life and career. Um I mean, you literally got every degree that anyone can think of, you know, from the university, which is spectacular. But but tell us coming to the US,
Carnegie Mellon, what was that journey like because that was incredible for you? >> While I'm in my undergrad, I'd been making little robots that whatever little funding in the undergrad would would allow or enable. But knew sort of the best school in the world to learn robotics is Carnegie Mellon. And one of the people that I worked with during my undergrad, we worked on India's first fully autonomous tank. He referred me to a professor here at CMU. >> [snorts] >> I didn't quite my me and my family didn't quite have the means to afford an education at Carnegie Mellon. Uh but there was an understanding with my professor Sanjeev Singh who said that if you come here and I like your work, I'll offer you a staff position. And that will in turn lead to your masters being paid. So, my dad took a loan to fund my flight tickets. And I was here. Fortunately enough in those three
months, Sanjeev really liked my work and offered me the staff position. And I did my masters and PhD covered covered both by Sanjeev's lab and Department of Defense uh from then there on forward. The most exciting part of being at CMU is you get to work on stuff that is in just unimaginable. So, I got to work on the world's first fully autonomous helicopter, which is like a childhood dream come true as uh far as projects go. Uh and then focused my thesis on how to make robots curious in physical spaces. And an expression of that is is in our company Gather AI, which is gathering data using AI through curious robots. >> So, then talk to us to about Gather AI and how Gather AI comes together because this was obviously coming out of uh some of the work that you were doing at the Carnegie Mellon and and also the way that you guys spin it out. Um so, talk to us about how the whole
origination of the idea and and how you guys went about incubating it and then bringing it to life. >> So, I I think towards the end of my PhD I realized that a way to the most direct way to have a positive impact through my inventions on someone's life is entrepreneurship. Uh where I can actually meet people whose uh life my inventions change maybe a little bit in in a positive fashion. And we had just published a few papers around how to make robots autonomous just using cameras and had gotten interest from some of the largest autonomy companies in the world to license out the tech technology. So, it seemed like the perfect time to also see if we can have that direct impact on the world. >> [snorts] >> So, we joined not having worked before. This is my first job ever. So, was was quite a bit to learn and we knew deeply about our domain and space of of robot autonomy
and curious robots, but very little of the world outside of that. There's a CMU incubation program called VentureBridge and Innovations Fellow. As a part of that, we and then Department of Defense funded us for customer discovery. We did about 175 customer discovery interviews from we had the hammer to find the right nail, which was supply chain and supply chain visibility. And decided that if we can make curious robots around what's happening within the four walls of a warehouse, it's an urgent need that we can address of a large enough market that we can have a global impact. So, that's where the core thesis of gathering data using AI comes from. >> [snorts] >> The fascinating part was there were other companies including YC funded ones that have that had tried this before. And failed to materialize the technology as it should. So, whenever you went out to market to raise,
most of the people said that this is not possible cuz others have tried it. And and uh it just doesn't quite work. And for us really the turning point came when during doing those uh investor interviews, we were also able to get a couple of customers on board to say I've seen this in my warehouse cuz we could we had full faith in our tech stack and our tech ability. >> [snorts] >> And if they build it, I'll buy it. Plus in SF, uh we actually ended up, you know, right near Salesforce Towers. We went to a couple of investor offices. We actually ended up flying drones and setting up a mock warehouse in little investor offices to show them the stuff working. And that's what got us our first funding in. Uh that was back in early 2019. Right. >> So, I guess I guess let's let's let's step a let's rewind back, you know, just so that the people get it. What ended up being or
becoming Gather AI? And how do you guys make money? >> [snorts] >> So, we digitize workflows and inventory data within warehouse through cameras that you can buy out of Best Buy. Whether it be drones or cameras on forklifts. By digitizing those workflows, we then offer not just real-time visibility, but the ability for these warehouses to ship more goods on time and in full while reducing the amount of labor they spent on shipping each and every item they ship out. A simpler way of saying it is for most of the people who click and the thing appears on the door magically, we enable that for larger percentage of that time that thing appears on time as promised to you by that retailer or by that e-comm vendor. >> Now, talk to us then about the way that you guys finance the operation. And I guess even before that, what would Can say was the first early sign that really got
you thinking that it has this had a lot of potential and that it was going to work. >> There are two two ways of looking at work. One is product market fit and other is the core technology cuz what we were building and have built is a world's first. Uh where >> [snorts] >> no other stack or team in the world can can just take a camera and turn it into an effective data gatherer with just software. Uh So this we had we had quite a bit of faith in our in our tech capabilities. The product market fit I think really showed itself when we raised in in early 2019 mid-2019 we launched our very basic website with no advertisement at all. And uh I think within the first month I had about $5 million in pipeline with with no active outreach with people really looking out to reach out to us to say, "Yes, this is a problem. Please solve it." And then it took us 3 years to develop
the tech after that. But…