Gather AI: $15M Revenue With Drones and 170% Net Retention - Sankalp Arora

Nathan Latka: Where are you sort of sitting in this space?

Sankalp Arora: I think physical AI is at the same point where ChatGPT was, uh, in around M 2015, where it's just finding its roots.

Nathan Latka: What's the average customer maybe paying per year?

Sankalp Arora: Our average deal size is about half a million dollars per year.

Nathan Latka: But what is your largest customer pay today? On an annual basis or range is fine.

Sankalp Arora: I would say about 3 to 4 billion dollars a year.

Nathan Latka: How many individual customers are you working with today?

Sankalp Arora: About 30 to 40 logos. Independent customers.

Nathan Latka: Hey folks, my guest today is some cup ora. He is building Gather AI. It's a Pittsburgh based physical AI company that uses autonomous drones and computer vision to deliver real time inventory intelligence for warehouses serving a variety of customers like Giottus, Axon and others. He holds a PhD in robotics from Carnegie Mellon University, where he built one of the world's first safe autonomous helicopters for DARPA and has raised 74 million bucks. Today we'll talk about a Series B which was led by Keith Block's Smith Point Capital Sunkop. You ready to take us to the top?

Sankalp Arora: Yes, sounds, um, good news and thank you for having me.

Nathan Latka: All right, tell us for folks that are not familiar with sort of the work, you know, uh, Nvidia is doing on SDKs to, you know, foundation models to program the physical world. Jeff Bezos is, you know, reportedly raising a fund to invest in this space as well. Obviously he's got a lot of warehouses to manage. Where are you sort of sitting in this space?

Sankalp Arora: Yeah, so I think physically I, as it's being defined today is robots operating in physical spaces, of course. And um, historically the approach to that has been to design specific autonomy stacks for specific use cases. And what the latest and the greatest of industries trying to do is build a foundational model, much like ChatGPT, that can do anything for text purposes, can do anything for physical spaces. And I, uh, think if you get an equivalence, I think physical AI is at the same point where ChatGPT was in around 2015, where it's just finding its roots, but people now have belief that it can deliver that that wasn't possible before. And we somehow sit in the middle where we have a mixture of foundational models and classical AI techniques which are, uh, probabilistic robotics driven to actually make these robots safely work in physical live environments and provide value today instead of, you know, providing value seven years down the line.

Nathan Latka: And in your opinion today, if you looked at every engineer in the world, every person building something, who's the best sort of person to build This, I mean I think deeply about what must be happening in Ukraine with these folks on the front line programming physical spaces, commanding to the drones, you, you know, having them, you know, hit and protect, certain things like that. I also think about obviously someone that might have 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?

Sankalp Arora: Two things, the three co founders, Daniel Gitesh and myself, like you mentioned, worked on the world's first fully 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 makes a 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, uh, data mode and a tech mode given our technical backgrounds. But also over the years we have assembled a bench of people from Amazon, from Uber, from uh, cgrit 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 serve effectively in this space.

Nathan Latka: And so with that background again, I've got your website now pulled up here. We'll talk about the 40 million Series B here in a second. But you use sort of the terminology physical AI for intralogistics. Is your plan here to sort of be Switzerland an open source foundation model that anyone can use or is it really to try to, you know, walled garden sort of Apple app store approach and keep it all internal.

Sankalp Arora: Sneedar, I have a very different view on it if you allow me, please. I think uh, our view is squarely focused on how do we deliver value to the end customer, which means we can have really good foundational models but the customer could not care less. They really care about how does it hit their top line and bottom line performance, which is either how does it impact their labor or how does it impact their uh, shipping rates or at the rate at which they are shipping outputs, facility throughputs so we provide the value to the customer as an end result, which is mostly actually accessed by them through our web dashboards and insights, not through our physical AI. Uh, our physical AI is a mode to get them that, that value. So we really uh, think of ourselves as the solution that generates the end value and not just provides the empowering technology to do so.

Nathan Latka: So you're, there's a trend in AI right now which is charge against the job to be done, not necessarily for credit usage or seat based approach. Am I hearing you correctly? You're charging Barrett distribution centers on some job to be done. There's not a massive engineering team there calling, you know, you know, tool your different foundation models to do work. You're doing the work for them.

Sankalp Arora: That's right. That's right.

Nathan Latka: Interesting. Make this hit home. Can you use this real example since it's on your website? So you know they have uh, to store four 500,000 series baggage shoebox, is it? So, okay, so they're a shoe company. Tell us the work you're doing for Barrett.

Sankalp Arora: Okay, so Barrett, we are actually in multiple kinds of facilities. They are one of the leading third party logistics players in us. But for this specific example, do you know Stadia Goods? They sell, It's a, it's a marketplace for vintage shoes.

Nathan Latka: Spell it. Stadium Stadia.

Sankalp Arora: S T A D I A.

Nathan Latka: Okay, go ahead, I'm pulling it up.

Sankalp Arora: Yep. So you get all these collectibles, collectible sneakers that range anywhere from one, uh, hundred dollars to $15,000. And uh, that shoe facility that you were showing images of holds about half a million pairs of these shoes. And when customers pay, you know, even in this case, when customers Pay, let's say $1,000 for their shoes, they want it next day. But when a person goes there to pick this pair of shoes, which is a case, and they don't find it there, they basically have to pull a software and on and say, oh, I can't find the shoes there. So let's spread out and find that pair of shoes that needs to go out. And suddenly a pick that was supposed to cost $2 ends up costing $10 to $15. What we came in and were able to do in partnership with Barrett was reduce those kind of location errors by 70%.

Nathan Latka: And that's because of this drone we see here in the graph. This is you basically going and finding the SKU that was purchased on new sneakers to get to the delivery time faster.

Sankalp Arora: Yes, but it's even better than that that those drones are continuously flying, taking images so you not only need to find that targeted shoe, you actually know at any given point how your warehouse is laid out and how things are moving. So you can make decisions in real time on how to do better picking, how to do better sorting. And that 70% decrease in inventory misplacement errors for someone like Barrett was the difference between keeping the Stadia goods account and making them happy versus having them move to someone else because they came to Barrett because they were unhappy with their last 3 PL and how their inventory was maintained.

Nathan Latka: Guys, remember, I am not just a YouTuber. I'm investing in my third fund. We've deployed $250 million into 550 software companies so far. Again@founderpath.com if you're interested in capital, I would love to cut you a check because I know you're investing in your education. You watch my show. So sign up@founderpath.com and when you get the onboarding email, I reply and I see all those, just reply and say nathan, I found you through YouTube and I'll make sure to prioritize you. I would love to cut you a check. Check out founderpath.com this makes a lot of sense to me. Thank you for educating me on how this works. How do you price this? What's the average customer maybe paying per year? Is this sort of an enterprise motion with million dollar per year accounts or is it more sort of bottoms up? I imagine it's probably more enterprise but please you tell me.

Sankalp Arora: Actually it's a bit of both when we. So just one thing I'm really proud of. I'm proud of the, of the thing that we've built our team is our net revenue retention. Annualized is 170% annualized. So it is best in class. So we start with you know, five to seven facilities in a network which is usually uh. Our average deal size is about half a million dollars per year in terms

Nathan Latka: of subscription for those seven facilities.

Sankalp Arora: Yeah, five to six facilities I would say yes. And uh, then that ends up by second year going to 50 odd facilities and by third year we are looking at 100 plus facilities and taking that forward. So it starts with a little enterprise deal but expands into network wide fairly quickly and fairly often.

Nathan Latka: This is a uh, traditional land and expand by but coming from the guy that built the world's first autonomous helicopter which we love. Uh, this is your engineering sort of sales kicking in here. What is don't name the customer. That wouldn't be appropriate obviously. But what is your largest customer pay today on an annual Basis or range is fine.

Sankalp Arora: I would say about three to four million dollars a year.

Nathan Latka: This is so impressive. How many, I'm just not familiar with how many warehouses exist in the world. You have 170% net dollar retention. How many customers are in the world that you think you could get on gather AI and deliver that 3 to $4 million of sort of contract value annually.

Sankalp Arora: So this is very interesting. So looking at uh, warehouses in US let's not even look at the world. If you look for warehouses that are above 100,000 square feet, that's about 150,000 warehouses.

Nathan Latka: Wow.

Sankalp Arora: And uh, for us for example to serve a decent chunk of uh, that business so that we are generating $100 million worth of revenue on our side because that usually means that we are generating a value of about 3 to $400 million. Sorry, $100 million of ARR on our side. That is usually means we are generating 3 to $400 million of revenue for our customers. Is just serving about 100 customers with 10 warehouses each. So that's just 0.1% of the market that's available. So we have an opportunity here to really serve a massive number of customers to alleviate their day to day pain points.

Nathan Latka: And how many you just gave the 0.1% NUR. But how many individual customers are you working with today like a Barrett? Are we talking like 10 or a thousand or 100?

Sankalp Arora: Oh yeah. I like to say we are just starting out. This is still very much day one. So I would say about uh, 30 to 40 logos independent customers.

Nathan Latka: Okay. And I don't want to put you on the spot but I mean can I multiply the 30 logos times a half million bucks a year? That would put you at about a 10 or 15 million run rate today, let's say roughly.

Sankalp Arora: Right.

Nathan Latka: Okay, great. And can you share what growth looks like if you're at uh, 15 million ish today? Where were you one year ago? Obviously you're growing because net revenue retention is 170% but that doesn't even include new logos out.

Sankalp Arora: Yeah, it doesn't. Yeah. So we grew 2.5x last year, year over year and this year we are forecasting anywhere between 2 to 3x. Again wild.

Nathan Latka: I want to get your more your background story. We know about the great work you did at darpa, uh, and autonomous helicopter. But you list many industries on your website. Three PL Logistics, retail, manufacturing, Life sciences and four separate solutions. AI Copilot, MHA Vision, AI Vision and sort of how it works solution. When did you write the first Line of code for this platform. And what was your first target industry and solution?

Sankalp Arora: Our uh, first target solution was helping warehouses get their inventory accuracy much higher, which is our drones flying around in the warehouse taking inventory. The first line of code was for this solution was written right after my PhD defense, so late 2018. And it took us three years from then to get to the market to get the tech ready because no one had ever built this tech around things that you can buy out of Best Buy. And we really wanted that for scalability, for serving our customers with effective hardware which is robust, but at the same time, uh, for our unit economics to look like SaaS instead of RAS. So it took us three years to get the tech stack out there where it was performing reliably for our customers. And since then we have grown dramatically year over year.

Nathan Latka: So 2018 was first line of code. Did I hear you correctly? 2021 is first customer?

Sankalp Arora: Yes, that's right.

Nathan Latka: How did you fund the business over those three years?

Sankalp Arora: We have since we were pitching a tech that was not being done in the world so we knew we needed sufficient capital.

Nathan Latka: And then you just recently announced while you. I don't want to spoil the announcement, talk to us about the Series B. Uh, and we don't love celebrating funding sort of on the show, but obviously you need it to build a revolutionary technology. So talk about how much you raised and then what you plan to use the money for to help your customers like Barrett.

Sankalp Arora: So as I said, we grew 2.5x year over year last year with those retention numbers that we are really thankful for. But at the same time the sales motion had scaled beyond founder led sales, beyond me and thankfully so, uh, because our new sellers were doing much better uh, than I was and uh, as a result we had sufficient proof points to say that now we need to pour more gas into this GTM engine and scale, which is where a $40 million Series B round came about. With SmithPoint Capital leading and taking most of that round. That is, uh, I think founded by Keith Block, ex CEO of Salesforce and Chris Little. And they just saw how enterprise physical operations are going to change. Even if they won't be fully autonomous, they will be fully digitized. And that's what we do, digitize those physical operations. And now it is going towards not just scaling our drone, uh, based inventory monitoring product, but also scaling our forklift, uh, vision product which puts cameras on forklift, the same tech stack on forklifts and suddenly whatever your forklifts are carrying and wherever they are placing it is digitized. So your warehouse turns m into an Amazon Go store without a wide network of cameras that Amazon Go stores needed. So instead of things, we think of it like drones were finding things.

Nathan Latka: Hold on. Can you show me a picture? I'm sunk up. I'm trying to find. As you're getting. I'm trying to find a picture. Do you have the forklift anywhere on the site?

Sankalp Arora: Let's see. I'll have to check. Give me a second. Okay, I think if you go to Solutions and go to mhe Vision.

Nathan Latka: Mhe Vision.

Sankalp Arora: Okay.

Nathan Latka: Oh, here we go. Forklift number two. This is you.

Sankalp Arora: Yes.

Nathan Latka: Okay, so you're the software and the hardware.

Sankalp Arora: Yes. So the operators get live feedback on whether they're doing the right transaction, whether they're picking up the right thing, whether they're putting away the right thing from the truck to put away, to replenish back onto the truck. So as a result, uh, your whole operations becomes digitized and then we can start providing insights on how to run operations better and not just find your missing things in the warehouse. So we have gone through.

Nathan Latka: You've made the decision not to be heavy. Sorry to cut you off, but you've made the decision not to be heavy. Opex, you're not selling all the yellow forklift here. You're, you're basically saying, look, we're going to build software that's so flexible you can plug it into any off the shelf hardware. So you are hardware Switzerland.

Sankalp Arora: That's it. Yes, that's true.

Nathan Latka: Smart. Really, really smart. Okay. Very, very interesting. Sunkulp, do you have a data advantage here? In other words, the more IoT devices installed, and whether it's a drone or a forklift that you have installed, you're collecting, I, um, imagine, millions of data points every single month at this point. How have you structured sort of your database to normalize the data, run an ETL process on it, and help the next Barrett that signs up have a lower sor a loss rate or inventory loss rate? Is there a compounding data machine underneath here?

Sankalp Arora: Yes. I'm so glad you asked that question. I think that's what keeps us, uh, in the lead in terms of the data insights we offer to the customers. Because we, and we have proven examples, I would request Amanda to share them with you. Now, our neural nets can read barcodes that not even a barcode scanner, a line scanner that you have on your grocery store can read. And mostly because the millions of data points, the millions of barcodes and boxes that we are seeing, bunch of our Neural nets are running self supervised experiments on them so there is no human in the loop. They improve themselves as they run these experiments and then a small number that they have trouble with in self supervised fashion get to people annotating those examples and then feeding it into the training loops.

Nathan Latka: So I appreciate you coming on today. Is there anything I missed that you want to make sure we talk about here over the last 60 seconds?

Sankalp Arora: Yes, uh, there's something that. It's a bit of a personal message because you said a lot of the audiences, existing founders and some VCs. I would say the biggest struggle that a, uh, founder faces, and this is every founder that I've ever talked to, is the loneliness and the mental health cost that comes with dealing with uncertainty. So the best thing I would encourage the fellow co founders is across, across the world is to be vulnerable with that side and seek help. Because I think to me as well that's something I discovered is change the quality at which and the level at which I was performing when I was able to address and seek help in those domains actively.

Nathan Latka: Is there a personal story you're comfortable referencing? Did you deal with a bout of depression?

Sankalp Arora: Oh, yeah, sure, yeah. I'm super, super comfortable sharing. Actually. Up until one and a half years ago, I didn't know that life, that people were existing that don't have suicidal thoughts. That was my life up until then. And I thought everyone deals with it. So, so am I. Because that's how you were having.

Nathan Latka: You were having suicidal thoughts as you're passing 9 million of revenue. Everyone would say, oh my gosh, this is an amazing. At least if they're just reading your Twitter bio. But you were dealing with suicidal thoughts at the time.

Sankalp Arora: Yes, yes, yes. And that was being. That was my sort of. I was carrying that since my teenage years, but they were getting really louder and mostly because of the sense of responsibility and pressure and I discovered the right mixture of medicines and discovered that there is life without suicidal thoughts. And I think that just made me a much happier operator and a much happier builder. And that shows in our work and what we are doing.

Nathan Latka: Well, I can't wait to see the impact that you and your team obviously are going to have on the world. To your point, you're like touching 0.001% of your market right now. My only regret is I didn't find you earlier and demand that you let me put in $100,000 check because I think what you're building is really compelling intellectually. It's interesting. And the growth speaks for itself. So, Sunkulp, if people want to follow your journey going forward after this interview, where can they find you online?

Sankalp Arora: The best place is actually LinkedIn or, uh, reach us through our website.

Nathan Latka: Just at a series B of 40 million with SmithPoint Capital selling 10 to 15% of the business, that would be about a 270 to 400 million valuation. Somewhere in that range with 75 people. But more importantly, he's really touching one of the first founders I'm seeing. Really touch on foundation models for the physical space, which is the next big focus frontier. He's touching a very small percentage of his potential customer base. Right now there's over 150,000 warehouses in the US with more than 100,000 square feet. He's touching just 30 customers today. Largest ACV 3 to 4 million. As he continues to expand his solutions like MH MHE vision into multiple industries like retail, manufacturing and more 3 PL companies like Barrett Songkulp. Thank you for taking us to the top.

Sankalp Arora: Thank you for having me on this and letting me share.

Nathan Latka: You won't believe this CEO's revenue. Click here to watch the next episode. Right now.