[00:00:00] Speaker A: Our platform is we create a restaurant in a box Solution. We provide this 24,7 accessibility of your cuisine and it operates like a quick service restaurant.
[00:00:16] Speaker B: Welcome to the Robot Industry podcast. Thank you for subscribing. I'm Jim Beretta, I'm your host. It's my pleasure to introduce Nipun Sharma from Apptronics to this edition of the podcast. Hi Nupin, welcome to the podcast.
[00:00:28] Speaker A: Hey Jen. Good to see you again.
[00:00:30] Speaker B: So yes, we actually know each other and we've met before on Canadian Food Innovation Project. We made a movie together. Robotics in Action, Seeing is Believing and I'll put the movie link to the show notes because it's on YouTube for people that want to see us in action. So Nupun is a visionary entrepreneur and the co founder and CEO of Apptronics, an innovative company revolutionizing the food service industry with fully autonomous robotic quick serve restaurants. By integrating advanced machine learning and data science, Apptronics creates delicious made to order meals without the need of on site staff, offering a glimpse into the future of dining. With more than 17 years of leadership experience in the restaurant and food manufacturing sector, he has successfully managed more than 10 restaurant brands across multiple cuisines, both quick service and full service concept. And his expertise extends to operating food production facilities, enabling a seamless connection between supply chain and consumer dining experiences. His professional journey began on Wall street where he honed his skills in investment banking and private equity. A graduate of McGill University, he transitioned to food and beverage industry, taking on executive roles and serving as chief operating officer for global public restaurant chains. He is passionate about redefining the dining landscape and advancing the integration of robotics into the restaurant industry, making him a pioneer in the intersection of technology, hospitality and culinary excellence. So that's quite a mouthful.
[00:01:58] Speaker A: Wow. Sounds pretty good coming from you, Jim. I couldn't have done it better.
[00:02:04] Speaker B: So this is very exciting time for you. You've come from Wall street, you've done a lot of work in restaurant brands and and now you've got your own company. So can you tell our listeners how you transition from kind of pasta to pizza and SJW to Apptronics?
[00:02:20] Speaker A: Yes. So, you know, you mentioned that I started my career very far away from food. It was Wall street, it was New York City, all that good stuff. And you know, my contention is that every human being at some point in their lives has thought about starting a restaurant or investing in a restaurant. I was actually dumb enough to follow through on that, so I made that leap over 17 years ago. But I do have some background. I come from four generations of chefs. But I am not a chef and I am colorblind. I know nothing about cooking food. So when I started my journey, I was a bit of an outsider. And I was always trying to build a concept that could enable a guy like me to produce amazing food that a Michelin star chef would be proud of. How do we make our kitchens more of a science than an art? And that's how I started. And it was a tough, tough start as an entrepreneur. I started my first chain in Toronto, and then I moved on to other chains and was CEO of a public company. And I had a weird journey in the sense that I've done multiple cuisine types. I've done quick service restaurants, fine dining restaurants. I've done Chinese, Indian, Middle Eastern steakhouses. You name it, I've done it all. And every time I went and took over a new concept, I always thought about, hey, what are the good best practices from different cuisine types? How do I make a smarter and a better kitchen? How do I make this cheaper to operate? How do I make the food better quality? How do I make it more sustainable? How do I make the unit economics work better? So that became this interesting journey that led us to where we are today. I think about 10 years ago, we started to feel the pressure in the industry from labor. Not just the cost of labor or the quality of the labor, but labor simply not being there. And I'm a big proponent of people needed to make minimum wage, make it higher, make people make more money. The restaurant industry doesn't have a whole lot of margins to play with. So when you put that pressure on that business, it's not coincidence that this business has some of the highest rates of bankruptcy. It's a difficult, difficult business. So I was in the market for robotic solutions for kitchens. There were plenty of companies five, six years ago. They had raised hundreds of millions of dollars for automation and kitchen. I'm like, perfect, we're ready for this. I need fewer gray hair. Let me buy these robots make my kitchens great. Because I'm a big believer in smart kitchens. But when I met these companies, I came to a sad conclusion that a lot of them on a very different journey that makes any sense to me. Companies have raised $100 million to slowly turn a burger patty over.
So looking at some of these companies, I realized that they're not quite there yet. We're not quite there yet.
I came back a bit disappointed until I met my co founders. And my co founders have a tremendous amount of experience in the automation and Robotic business. So when I met them, I'm like, I have this general idea what it means for automation. Do you think it's possible? So it started as just an exercise between three of us to see how we could automate a kitchen. And as we went down the rabbit hole, we came to a conclusion. If it can solve my problems, it can solve many people's problems. So initially our project was named SJW Robotics. It was just the initials of the last name of the co founders. And as we became a company, we decided that we need to come up with a name and that name should reflect what we're trying to do. And I went to a few agencies. They were going to charge us a lot of money. We didn't have any money. So I used AI to come up with a name. At the end of the day, we were finding a solution for food appetite. We're using robotics Tronics. So how do we make something that makes beautiful food uses automation? So Appetronix came into being as our name. And we are not the brand. We are BuyAppetronics, whatever your brand is. Donato's Pizza, what have you. We're biopatronics. So our platform is we create a restaurant in a box solution and we provide this 247 accessibility of your cuisine. And it operates like a quick service restaurant. So that's sort of how we came from an experimental project using some of my experience in the food business and using the experience of our co founders to create a fully autonomous restaurant.
[00:06:36] Speaker B: Let's talk about pizza for a minute. So pizza, you actually kind of started out in pasta, right? But you switched to pizza. Tell our listeners a little bit about the pizza industry.
[00:06:46] Speaker A: So, you know, I didn't, my team and I didn't really initially want to do pizza. And principally because there were many companies trying to do robotic pizzas and we thought they were not doing it correctly. And I didn't want to be the 101st guy trying to do a pizza robot system. So that was never on our radar. We were looking at, as you mentioned, pastas and noodles and Asian food and different things that were more complicated. And we chose complicated because we weren't a company. We're trying to solve a very complex problem. And given my experience, Asian food or anything noodles and pasta based was far more complicated to automate. And we thought if we could automate that, we could automate anything.
And you know, pizza was always on the radar. And one reason everybody wanted to automate pizza, it is the biggest category in the U.S. in North America. In the U.S. alone, we're talking a $50 billion market. There's over a million restaurants in the United States and I think about 30% are pizza focused. Over 90% of Americans eat pizza regularly. 85% eat it at least once a month. So in many ways this is the principal food of North Americans, Americans, Canadians. It'd be foolish not to take it seriously. It'd be foolish not to do it properly. The other big reason we decided to do pizza is our first pizza partner is a company called Donato's Pizza, based in Columbus, Ohio. This is in my opinion and the opinion of some of the best experts in the world. The best pizza chain in the world in the US the company's over 60 years old, do a phenomenal job. We share some of their values, we share their passion for the food. And end of the day, I don't say we're in the robotic business, I say we're in the food business. We are selling the best tasting food in the world. It happens to be made by robots and machinery. So that's how we came up to focusing on pizza focusing. The other interesting thing we liked about this relationship was Columbus. While Ohio has a very special place in the world, it's a very entrepreneurial state. Lots of great companies have come out of there. But the airplane was invented in Ohio, the Wright brothers. The world's first airport was in Ohio in 1905. And we are going to be launching this at an airport in Ohio hopefully the next couple of months. So we thought after 1905, 120 years later, we have the next big thing happening in aviation. So we like the whole story behind it.
[00:09:16] Speaker B: That's kind of great. And you've got some other partners as well, Agape, Alicorp and Grote.
[00:09:22] Speaker A: So we actually have quite a few different partners and that enabled us to do something pretty of big magnitude. So we have companies that help us in some of the automation aspects in technology, in the envision, AI in the oven systems, in the co manufacturing systems. I would say there's a good eight, nine companies that are very solid partners. They're also investors in our company and they've helped us in different components of the machine. So we didn't have to reinvent everything from scratch. We use Avention ovens, some of the best ovens in the world. And this allows us to make a pizza to perfection in a very fast way. So having the team from this company intimately involved in our development, working with the different parts of a machine, makes it easier we have a company called vm, based out of New York, that's helping us with the vision AI it scans the pizza to make sure it's acceptable quality, unless otherwise it rejects it or it lets consumers take it. We have a Hatco locker system that's helped us. We have Booma Manufacturing in London, Ontario. That's our manufacturing partner. So we have a host of different companies that came about, and I like to say that we stand on the shoulders of giants who are experts in different components. This allowed us to narrow our focus down into our R and D. So collectively we could produce something that's pretty magical.
[00:10:41] Speaker B: You know what? I have to say that I'm connected to a boom a little bit. And I actually went for a tour of their facility and saw the machine. And so next time when you're doing a runoff, I'd love to pop over and get a slice of pizza.
[00:10:55] Speaker A: And that's going to amaze you even more than the machine. I promise you. Every time people have come and try the pizza over there, this is completely. Forget that it's made by a robot.
[00:11:03] Speaker B: So what were some of the big challenges in putting a robot and all these other technologies together, which probably hasn't been done before? Right? It's a custom machine.
[00:11:11] Speaker A: Yeah, it hasn't been done before. And, you know, the making a pizza is not. Not impossible using automation and robotics. Right. Conceivably, there's a place to put sauce, apply some cheese, chop some pepperoni, add some onions, bake it, box it. What makes things difficult is how do you narrow the margin of error? Right. So if I'm making Jim Yu a pizza at home, I'm gonna, you know, throw some pepperoni on it and sprinkle some cheese for my hands, and it'll taste reasonably good. But when you start working with the best brands in the world, they're very specific in what they need. So if a medium pizza has 35 slices of pepperoni, for example, you cannot have 34. If I have X number of grams of cheese, I cannot have more or less. So when you do things in an automation perspective, you have a margin of error. So that margin is fairly w. If it's 20%, I have no problems to shrink that to 19. Very difficult to shrink it to 1%. Impossible. So how do you keep shrinking that to a level that a human being can do fairly easily? So that's where you need components of automation. How do you dispense certain things? And even if you do make a mistake, how do you have vision systems that catch that mistake properly. So for example, in a human kitchen, let's say a super experienced operator that does a perfect job every single time. I can eyeball the finished product, I can see I'm missing a couple of slices of pepperoni. I can see the pizzas are slightly undercooked in this area. I can shove it back in the oven for 30 seconds. I can correct it. Now in a computer based, in a robotic based and automation based environment, you cannot make these corrections after the fact. They have to be done before and during the fact. So in the end it's just going out. So you cannot catch it towards the end. How do you catch the process? So you have to think very differently than a human kitchen does. And those little nuances, this narrowness of the margin of error, that's the difficult part, the overall part, no problem, you can put it together in a year. Those little, little components, how do you make them perfect? That's the difficult part.
[00:13:13] Speaker B: So I'm excited to hear that you've got AI working, you've got vision and you're collecting data. So how do those three all merge?
[00:13:20] Speaker A: So for us, AI or data science, it solves two purposes. One is predictive maintenance of the machine. Certain parts are being used X number of times they need to be replaced. Let's make sure we have spare parts on hand. Let's make sure we find the best downtime, let's make sure we have personnel available to go make that change in that downtime. So the machine, if I'm running one alone, I can see it, I can monitor it. And we have thousands of machines in the market that need to monitor themselves. The one part is how do you maintain the machine? The second part is how do you maintain the inventory? You know I have chicken, let's say that's expiring in 24 hours. Instead of wasting it, I'll start self generating a promotion, get my chicken pizza 50% off, I can start predicting demand. Right? It's a Monday morning, the weather is nice, everyone's going to be at the office and everybody keeps ordering pepperoni pizzas. Let's make sure we have adequate amount of pepperoni. There's a storm in Toronto like there was one a couple of days ago. Nobody's coming to the office. Let's not overstock the inventory with food. So how do you make sure you manage the inventory? How do you make sure you minimize waste? As an operator with one location, if I'm really good, I can monitor this. Of thousands of locations, it needs to monitor itself.
[00:14:29] Speaker B: No, that's very exciting. And I'm wondering too, is this data something that you sell to your clients, or is this something that you partner with your clients? Because you're going to have lots of different clients, I assume.
[00:14:41] Speaker A: Well, yes, exactly. So it's more of the one client is the brand, right? It's the brand that has specs in terms of what they have in their menu portfolio. And then you have is where these spaces are. So our business model really calls for working with food facilities management companies, companies like Compass Group and Sodexo and HMS Host. We're talking thousands of universities, hospitals, office towers and airports. And every place has different dynamics. A university could be closed in the summer, hospitals open 24, 7. Airports are really busy at this time of the day because that's when all the flights are coming through. So when you look at each location with very different dynamics in terms of how the customer flows, you need to make sure that you're ready for that. So for us, this data, like I said, predictive maintenance of the machine and also for effective inventory management systems. And it goes beyond just then predicting your sales when you become really good at this. Let's say there's a storm in Florida and the tomato prices go up dramatically. And so can I start pivoting to recipes that use less tomato so I can make a better margin? Can I find a promotion that uses barbecue sauce instead? I don't know. Whatever the case is. So when you start using data, you can start making sure that you're managing your expenses well, you're managing and predicting your sales. Because the only thing worse than having too much food is not having enough. If I've run out and I could have sold 100 more pizzas, I have a problem. So this allows us to match up our demand systems, our cost systems, and good operators do this. Today, there are very few of them, but computers and data analytics and AI is making, I think, the world better for these things. So what we're building is proprietary to our customers only. I don't think at this point we have any desire to sell that to other people. Our goal is to make our current machine work really, really well in the context of our clients.
[00:16:36] Speaker B: Oh, that's very exciting. And so when does the airport happen? Like, when does Columbus happen for you?
[00:16:42] Speaker A: Well, right now we're aiming for the machine to go live in April. The machine, as you saw, is currently working. It's gotten most of its certifications in place with stress testing to make sure that it performs as well over long periods of time. And we've been producing pizzas for the last few months in the machine. We're just fine tuning the final bits so we get all the certific.
I think as of now, our goal is to have this machine transported, sent to Columbus, and hopefully go live at some point in April.
[00:17:11] Speaker B: Well, that's like I say, it's really, really exciting. I'm kind of excited to go to Columbus. I don't get there very often, but I'd love to taste a real pizza in the real environment. So, machine number one, what have been some of the challenges? Because of course, it's all about material handling or maybe there's PLC issues. Have there been some technical challenges? And I want to ask you about the robots too.
Sure.
[00:17:34] Speaker A: So, as I mentioned, the biggest challenge for us was as we had to narrow down the tolerance levels of what's acceptable volume of food products. Cheese, sauce, pepperoni, what have you.
A lot of the things we initially thought were working well, we couldn't lower the variability in those equipments. For example, we've been working for eight, nine months with Cheeser, and it was doing a reasonably good job, but it wouldn't give us that acceptable level of tolerance. And at one point we realized we could keep tinkering with this equipment or start from scratch. And that's what we did. And I can tell you, our team at Booma and our team, it took them just a handful of weeks to come up with a significantly more superior version. So that was the thing at one point. Do we start from scratch and let go all the work you did with something that was kind of working and make something that's that's okay, or start from scratch and make something that's excellent. So we like to make that. Cause in various stages of development, I can tell you, we already have started working on version 2 and version 3 of this machine that you saw the difference between the different versions are going to be. They're going to be smaller, they'll have a higher volume of input, they're going to be faster, they'll be more reliable, and that's the journey we're currently going on. Some things are great that we have something we would love to have in the future, and that's how the different versions are going to get better. Just like your iPhone is much better than it was 10 years ago.
[00:18:55] Speaker B: Oh, great. And I was going to ask you what kind of robots you're using, but that's kind of a moving target, right? Because you probably likely are changing robots too, depending on the machine. Type and the size.
[00:19:06] Speaker A: Well, there's an announcement we're going to be making soon. I can tell you right now that. So in our different machines we've been working a lot with ABB robots, we've worked with fanuc robots and we were exploring who is going to be a long term partner based on the agenda we have for the different versions of our different machines. And we selected Kawasaki Robots. Incredible company. The expertise they have in understanding the food robotics space, the resources they're devoting to help us in our journey. So we made a decision as a company to go with Kawasaki Robots and that's going to be our partner with the robotic component of the machine.
[00:19:43] Speaker B: Oh, that's great. So I did have a question. I was going to ask how many pizzas that you can make in an hour? If you can tell us that or is that confidential?
[00:19:52] Speaker A: No, I can tell you. I can tell you right now. From the moment you place your order to the moment the pizza is delivered in the lockers, it takes about three minutes. The biggest component of that is a little over two minutes is the baking time. So when you have one oven, you're talking about three minutes. That's about 20 pizzas an hour. With two ovens you break that process down. So from the two minutes that it takes in an oven, it becomes two pizzas coming out every two minutes from that system. So then you can go up to about 60. So depending on the number of ingredients, our current machine can do between 50 to 60 an hour.
[00:20:28] Speaker B: Very exciting. So what does the future look like for Optronics? Are there other foods and other form factors that are on your short term radar or is it let's do pizzas first.
[00:20:38] Speaker A: So yes, so we are definitely launching with our pizza brand. We definitely have a version 2 and a version 3 of this machine that's going to come out in the next 12 months. In parallel, we've also worked on a Asian bowl robot. Think about pad Thais and green curry and rice, all that good stuff. We hope to have that one out at some point this year as well. So by the end of this year we will have a couple of versions of our pizza machines out. The first version of our Asian bowl robot out, and that takes us to the next 12 months. Beyond that, we will be adding another cuisine type. I think I would like to add a Mexican bowl robot as our third cuisine type. Between having these three cuisines, between having some pretty solid partners and the landscape to scale up with these food facilities management companies, I think that's going to Be our medium term plan. Three cuisine types, big manufacturing partners and mix scale up with food facilities management companies with the best brands in every category.
[00:21:40] Speaker B: So you must be also thinking the next phase is about setting up like sales and operations and such to help with facilitating installations and that kind of thing.
[00:21:52] Speaker A: Yeah. The good news is that what we're building right now and what we have in terms of our investors and partners, the sales part is the easy part. I think we have enough demand right now. Next hundred years. Our challenge is not finding a customer, it's more about how do we scale up our manufacturing. You know, with a boom and stuff we can do 10 odd robots a month. How do I get to 100amonth? How do we invest in our partners? How do we get more partners? How do we outsource certain components that are easy to outsport, that don't take away from our patents? So our really agenda is all manufacturing focus and manufacturing means R and D as well. Right. So again, not looking for customers. How do I make my machines faster, smaller, higher capacity, more reliable? So we're really R and D driven stuff. I'm pretty much the sole person focusing on the sales and marketing side. For now. All our energies go towards manufacturing and R and D. I'll tell you the kind of stuff we're working on. You mentioned earlier in our conversations that you had laser eye surgery. I would like in a perfect world have lasers cut up pizzas instead of knives. So it makes maintenance a lot easier.
[00:23:05] Speaker B: Right, Right.
Wow, that's such an exciting time and there's so many opportunities. I'm really excited for you. Hey, and I wanted to thank you for coming on the podcast today. If somebody wants to find out more about how they can put one of their units into their facility or hospital or whatever, how do they get a hold of you?
[00:23:23] Speaker A: Hey, they can definitely reach me directly or go through our website. It's a P P E T R O n I x appetronics.com or nipponphiltronics.com.
[00:23:35] Speaker B: And you know what, I was just going to ask you this. Did we miss anything in our conversation this afternoon?
[00:23:40] Speaker A: No, we didn't. I think all I can say is that if anybody hears this podcast that has an interest in robotics and automation, in AI and data science. I'm a firm believer in collaborations. I'm a firm believer in people taking us to next heights and a firm believing believer in not reinventing the wheel. So we'd love to find companies that we can collaborate with that can help us grow and can help us grow quickly.
[00:24:05] Speaker B: Nipin when you're not creating value and inventing new ways to eat food and develop food, what do you like to do? Do you have any hobbies?
[00:24:11] Speaker A: I love reading.
I'm reading Adam Grant's Hidden Potential right now. If you haven't read the book, I'm about 30% way through and it's an incredible book, very inspiring, very intelligent and really gets a few things in my mind working. So I love anything that challenges me intellectually and so yeah, great book I'm reading currently.
[00:24:33] Speaker B: Well, it's great to catch up with you. Next time you're in London, shoot me an email and we'll grab some lunch or grab a pizza.
[00:24:39] Speaker A: Looking forward Jim, Always great chatting with you.
[00:24:41] Speaker B: Our sponsor for this episode is Earhart Automation Systems. Earhart builds and commissions turnkey solutions for their worldwide clients. With over 80 years of precision manufacturing, they understand the complex world of robotics, automated manufacturing and project management, delivering world class custom automation on time and on budget. Contact one of their sales engineers to see what Earhart can build for you and their
[email protected] and Earhart is hard to spell. It's E H R H A R D T. I'd like to acknowledge A three the association for Advancing Automation. They're the leading automation trade association for robotics, vision and imaging, motion control and motors, and the industrial artificial intelligence technologies. Visit automate.org to learn more. And if you'd like to get in touch with us at the Robot Industry podcast, you can find me Jim Beretta on LinkedIn. Today's podcast was produced by Customer Traction Industrial Marketing and I'd like to thank my team, Chris Gray for the music, Jeffrey Bremner for audio production, my business partner Janet, and our sponsor, Earhart Automation Systems.
