Revolutionizing Restaurant Operations with TechRyde's Sandeep

In this episode of the Restaurant Technology Guides podcast, host Jeremy Julian sits down with Sandeep, the founder of TechRyde, to discuss how his innovative middleware solutions are transforming guest engagement and connectivity in the restaurant industry. Sandeep shares his journey from being a technology and POS installer to creating a comprehensive middleware that integrates various apps with POS systems to streamline kitchen operations, reduce labor costs, and ensure on-time deliveries. They delve into the benefits of kitchen automation, real-time data analysis, AI integration, and the impressive ROI achieved by clients using TechRyde's systems. The episode concludes with success stories and insights into the future of restaurant technology.

00:00 Techryde

00:14 Introduction and Guest Introduction

01:33 Sandeep's Background and Journey

03:22 Tech Ride: Concept and Evolution

05:11 Kitchen Automation and Middleware Solutions

08:06 Real-Time Data and Machine Learning in Restaurants

14:37 Importance of Kitchen Automation Systems

24:46 Future of AI in Restaurant Technology

27:07 Contact Information and Closing Remarks

Transcript

0:02

This is the Restaurant Technology Guides podcast, helping you run your restaurant better. In today's episode, we are joined by the founder of TechRyde Sandeep, who is really solving some interesting and unique ways to go about guest engagement and guest connectivity and communication. I was really blown away with how mature his software has gotten and really the amount of problems that he's solving for people as it relates to third party delivery and communicating outside of the four walls. Stay around till the end to hear some of the success stories that Sandeep has had with some of his clients. If you don't know me, my name is Jeremy. Julian, I run the sales group for CBS Northstar. We sell Northstar point of sale for multi-units. Please check us out@cbsnorthstar.com and now onto the episode.

Jeremy Julian: 0:58

welcome back to the Restaurant Technology Guys podcast. I thank everyone out there for joining us. As I say, each and every time. I know you guys have got lots of choices, so thanks for hanging out today. I love these kind of stories'cause we'll talk quite a bit about it, but I love startups that kind of scratch an itch that they see in the market and opportunity to ultimately to make, restaurants better. And so I'm gonna introduce Sandeep Sandeep, why don't you introduce yourself. give a little bit of your background. Got a chance to catch up both before the show and, you guys sent over a little bit of a bio, pretty cool story, and I love kind of hearing these types of stories. And then we'll talk a little bit about how you're solving, solving quite a bit, in the way of restaurant tech and why you went after that.

Sandeep Mahal: 1:32

Yeah. Perfect. First of all, thank you so much, Jeremy, for having me here. So it's, it's my privilege, privilege to talk to you. I'm a techie guy, so I'm a computer, BA graduate. I was studying in India, then I entered into hospitality sector. I worked with, my first company was, POS. I joined as a POS installer, then I moved to Micros, Oracle. Then from there working with Micros. Yum, yum. Brands hired me. the unique problem I found there was like, being a big, enterprise, they had a lot of it people, but still they were struggling. To connect Micros, which is a big POS system to many apps. So that was the idea from where I thought if the big brand is, struggling, although they have deep pockets, all the resources, it and everything, even Oracle has very good relationship by then. Oracle acquired Micros by then. So I thought like, why not create a simple. Easy to use a middleware kind of setup, which can help any restaurant operator to connect to any app to the point of sale system. So that was the idea. In 2016, we created a middleware to connect all the apps. To the point of sale system. Then we added a lot of other point of sale system, a lot of other middlewares, and then I finally moved to North America market. So here I currently am in Canada, so we have now customer, across the globe using our, system integration. To the multiple levels. And not only that, we have taken a lot of pivots after that, we added a lot of, other functionality to our product suite and this is where we stand. So now I am more of, sales and marketing person started with a, like a technology background. I involved most of my time into marketing. That's why I thought like podcast is a great opportunity. So Jeremy, thank you again for. Having me here and giving me the opportunity to present Decorat right, and myself and the company and our vision.

Jeremy Julian: 3:22

Yeah, talk to me real quick about tech, right?'cause I do want to talk a little bit about the background because it's always funny when I get tech people, they're like, oh, it should be so simple. they're used to open APIs, they're used to some of these same kind of things that happen in the regular. I guess outside of the POS world, but what is TechRyde Before we jump into kind of how and why you thought that this was a great place to start and to go solve these problems, but what is it that middleware, I know you guys do a lot of different cool things within the space, and so kinda what's your main focus and where did it start and where is it going?

Sandeep Mahal: 3:52

Exactly. So this is the common question. I've been asked so many times when we exhibit at the trade show and do a lot of other stuff because we are not only just the middleware aggregator, so in simple words, which anybody could understand. Or I would say restaurant operator would understand. So we are backbone of the kitchen. So we see kitchen as a soul of the restaurant from where we, we do everything. which means we can handle the online ordering websites. Suppose there is a lot of load in the kitchen, you cannot take the new order. So what we can do is we update all the online ordering platform. We have kitchen display system, which can automate the entire kitchen flow. So chef's, life is, we made his life very easy because they can now see what they can cook, unlike any other kitchen display system, which can just simply send the orders and in a sequence, and then chefs start to cook it. our system is built for saving the labor cost, saving the food cost, and making the customer happy. We also take care of the consumer so they leave satisfied because they're getting hot and warm food, and in the end, make staff more organized. So that's the wholesale idea. So we want everybody to be happy and not the least. We also take care of the environment with our technology, with our automation and batching of the orders together so there's less carbon footprint when the driver goes out to deliver the food. So if it is still sounds very confusing. So all the pieces tied together is within the kitchen, so we ensure the. Food is cooked right on time, hot and fresh every time when it is delivered to the customer.

Jeremy Julian: 5:24

and as I talked to different restaurant operators, I dunno, 20 years ago they had channels and the, there was really mostly one channel, and they might have seven or 8% of your sales that were going in out another channel. If I'm a delivery pizza place, almost everything is delivery. But nowadays, the consumers expect to be able to order on a third party marketplace on your website directly. They wanna be able to call the store and get an order. They wanna be able to sit down in the dining room. So the complexity of how they produce food continues to be a challenge. Is that what you guys saw as you guys have been going to this kitchen automation path that says, how do we solve both for the consumer and quite frankly, for the staff member? Because the staff member is also struggling to figure out how to manage that. is that where you guys are trying to solve those problems for both sides of the equation?

Sandeep Mahal: 6:08

Yeah, a hundred percent. Jeremy, you're on. Spot on. So what happens is, imagine a kitchen at a busy time, even at a normal times, a lot of orders coming, people are ordering from online orderings. There are a few people ordering from Uber, DoorDash, GrubHub, skip the dishes and their own website too. Somebody calling somebody in the restaurant in front of kiosk system using the phone app. Or there are people seated on a table. There are cars in the drive through and the chef, who is just very expert in cooking the order. Doesn't know. Doesn't know what's going on out in outside the four walls of the kitchen. Where are my drivers? How's the weather outside? How's the traffic look like? How many orders in queue? What is the promise time? So there are so many moving parts which human brain cannot, analyze on the real time basis. So this is where technology is taking care of it so that the business can focus on their business side and where technology can help them assist smoothen the operation, help the customers, their consumers, and their staff, everybody. So that's what we guarantee 2500% ROI when somebody uses our system. So ROI is driven from, increase in profit. Reduction in labor and reduction in food cost. So that's, which is very huge. And nowadays

Jeremy Julian: 7:20

the only two prime costs you can control are labor costs and food costs. So if you're not controlling them and finding solutions that can help with that is huge.

Sandeep Mahal: 7:27

Yeah, exactly. And we have data to validate it. The people who have started using such kind of technology, they have seen a hundred percent on time delivery. Like for example, PI Hut Australia, they use our integration, with all the locations like 13, 13 countries across the globe. Pi Hut using our system a hundred percent on time delivery is huge for, so for quick service brand like Pizza Hut. So that's huge. And they also have seen growth in number of orders. We process two 25 million transaction every day, every month, for Pizza Hut. So labor costs all that contribute to$4,000 in saving after spending a little money for our subscription. I.

Jeremy Julian: 8:06

Yeah, so tell me how is it that you guys are calculating a hundred percent, ROI on these things, or a hundred percent delivery time? that's unheard of. I don't know how you guys are able to one, track that. Number two, accomplish that because, I mean at the end of the day there's so many moving parts and they know for any of our restaurant operators out there, they're like, yeah, this guy's crazy. help them understand how and where you guys are getting that data and ensuring that we, that by implementing a solution like this, they can get there themselves as well.

Sandeep Mahal: 8:32

That's a valid question because this is the common question again asked when we say, oh, we can achieve a hundred percent ROI and your deliveries. And not only that is part of our agreement. So when we sign an agreement with any customer, we say, these are your KPIs. Your customer satisfaction score has to go up by say 20%. If you are right now at 70, we can take it up to 90% and your on-time deliveries will be a hundred percent. We guarantee it. That's part of our agreement. If you're not happy, there's no ation. So how do we calculate? That's the beauty of technology. So that's why where we say kitchen is soul of the restaurant. We, we are. Entire calculation is based on machine learning. We have written algorithms which can optimize the flow of the kitchen order. So for example, we will only show up those orders in front of Chef, which they can deliver easily on time. Suppose you and I live on same street, there is another person live on different street. We can batch our system, can batch our orders together. Not only like on time delivery, even efficient delivery. So when driver take goes out, he can take both of our orders together instead of going back and forth each time. So imagine there are so many orders in the restaurant, busy time. So system is making it more easier, more effective, and that's why it's a hundred percent on time delivery. Batching is one thing. Also route deciding best route based upon traffic so we know when the traffic is going to increase, what is the best route to deliver the food. If restaurant operator has multiple, third party delivery channels available, we even find who can be the best, best efficient, economical resource for the restaurant. So if it is close, delivery near to the restaurant, we assign it to the restaurant driver because he'll come back quickly pick up the another order. So there are many things I can talk a lot about it. Suppose there are two Pizza Hut chains or maybe any two restaurant just close by in two kilometers distance and there's another driver sitting idle and we have a lot of orders in the another branch we can do driver sharing too. We can ping that driver come and pick up the order so that way it'll be more productive. That's how we save the labor cost too.

Jeremy Julian: 10:31

That's amazing. and again, statistics showing, a hundred percent, delivery on time is also remarkable. I was actually watching the History Channel had, the history of Pizza Hut and, they had that thing, or Domino's maybe, 30 minutes or your pizza's free, and they were struggling, but it was a huge marketing ploy that they ended up doing. Cindy, let's take a step back. You talked about your early career and. Getting the source data. The source data, whether that be coming from an online order, the third party delivery point of sale. How has that translation been? Because it's hard. It's hard doing, all of these different modalities because the way that DoorDash reads an order and publishes it, and the way online ordering. Platforms do. Are you guys taking all of that and putting point of sale at the core of it, or are you guys still at a place where you guys are integrating? Because doing that translation layer has to be hard to be able to see what is micro saying? What is toast saying? What is Square saying? What is Clover saying? Whatever the system is of record. And then you guys have to translate it into business terms to be able to make a decision on it while also looking at some of the newer systems that are out there that are digital native.

Sandeep Mahal: 11:34

Yeah, so that's, the good thing about it's, we got that patented also. So this, the layer is so complex. Many system talk in different language. They have different data set. We made it generic. So we we got our patent, in. One 50 countries, including us is patent pending. So because we do a lot many things also the data processing, updating the website on real time promise, time expectation. along with that, we take care of dine-in orders too. There are a lot of data within the restaurant as well. People are coming, going, there are allergic information and, and their past history, their complaints. We have integration to, even hotel, property management system. There's a lot of data sitting with the Oracle database, let's say in the property. The guests are visiting Merris, star Wars and all their preferences are noted. So all that complexity of data. So we have collaborated together into our data source. So we collect data from different data sources, point of sale, third party websites, online ordering, and even APIs. Third party APIs collect all the data source into the point of sale and then run the machine learning. and decide which order is more important in for the kitchen. I. So that's one part. This which we have patented and put our fencing along the wall so that it gets secured and all that. So we take care of data security as well. When data is in transmit or being published to cloud, all that is being taken care. So we are an enterprise app. So for any enterprise build.

Jeremy Julian: 13:01

I love it. how has the, let me flip and I'm ask a more personal question. How has it been going from the tech side to, the sales and marketing side? Is it, has it been a fun journey for you?'cause I know a lot of, tech founders struggle with kinda, how do I translate this into plain English for somebody?'cause you just, even based on some of the responses, I can tell that you think. Technically, and then you translate it to'cause you've just been doing this for a long time. I'd love to have you share a little bit of, how has that journey been for you personally, getting into kind of the, let me paint the vision for what it is that we're creating.

Sandeep Mahal: 13:29

Yeah, so interesting. So life, so life is a learning curve for me. it's just not the sales or, the marketing part, which is really fascinating, right? Nowadays to me, I learn from my staff, I learn from people we hire from marketing. So we have hired people who have spent like years and years in doing marketing. So every day or every other day. Weekly calls and all I take their input, also do my study. I read a lot recently. not, I was not reading earlier, but recently I started reading a lot. Blue Ocean Strategy is a good book, which I follow for marketing. Yeah, so a lot of things I have improved in myself, my lifestyle, my eating habits, my sales and energy passion. So I know business is all about sales, right? So if you want to run a business, you, if you are only tech focused, you won't be able to survive. So I learned it. Hard way and all. And like I said, it's a learning curve every day. Even I know in future I'll be learning a lot. Still I'm not expert. So this podcasting also, I never imagined, I would do podcasts one day, so this's part of marketing, so I'm learning. Maybe I'll improve more in podcast too. And part of, join you in further podcasts and all that. So let's see how it goes.

Jeremy Julian: 14:35

I love it. I love it. Talk to me a little bit about the kitchen automation side of things. You talked a lot about, guest, and staff, behaviors. And again, I just was on the phone with a, with another person that produces KDS software and hardware last week, and we were talking about how consumers really are looking, whether it's Amazon, they tell you your delivery drivers 10 stops away, your delivery driver's eight stops away in the restaurant. It's like this. Pray that the food is gonna get to you. Domino's has had this pizza delivery tracker. How is it that you're helping automate the kitchen tasks? You talked about it a little bit earlier. the chef needs to know what he needs to know. When he needs to know it. Any extraneous data is gonna hurt. His ability to produce the food, whether it's the, line cook, the prep cook, whomever. Help me understand how you guys have built out something to automate that heart of the house that, the engine that produces those items. Because that part is tough. That part is tough to be able to know that I have a New York strip and I have a Caesar salad, and how do they come together? Especially with so many different inputs and we talked about them just a few minutes ago.

Sandeep Mahal: 15:35

Yeah. So yeah. So there are so many moving parts within the kitchen. like I said earlier, the traffic. how many chefs available, how many drivers available, how many equipment's available, promise, time, number of items in order, how many order channels are there? How many orders are in queue? What is the historical data? So a lot of data is being feeded into the system, and system is running algorithm. So the best part of the machine learning is system is learning every day the more data you feed system is becoming more efficient, more perfect, day by day, more learning, more. Everything is there. So that's how all this parameters we have added into the system. First time we input the system, train it. okay. So system has to behave in such a way, ensure every order is fulfilled on time. The whole idea. The automation is the consumer should get hot and warm food every time, even if it is a delivery order or a din order. If the customer is seated on a table, he should get his rounds every round on time. So that table turn time also improve. So we take care of all the different segments. It's not just the delivery part of it. Now, talking about the automation for consumers. There was an article in SD technology recently. They have done research on Canadian, all the people who are ordering food. So 70% of the consumer were, said that they want to see the real time order status from the kitchen. There are many apps, many restaurants. They provide real time, status in terms of where is the driver. Even like I, I know Uber Eats DoorDash. They provide the driver tracking ability. Driver is out of the store, order is now submitted to the store. It is approved. So what we do further to that is we provide level of, real time status from the kitchen when your order is being. prepared. When is it, is put in oven Right now it is cooking or it is cooked or it's packed or it's dispatched or delivered. This kind of gran updates is very helpful in automation and it reduces the stress on the staff. Also, somebody calling back on the status, no need is already updated. Consumer is happy. He will obviously come back again and again, and that's how your satisfaction score for consumer goes up.

Jeremy Julian: 17:42

and the thing that I love that you said earlier, Sandeep, is the whole idea that, tech ride is looking at not what ideal is, but what is actually taking to cook this steak. when you have tickets, you know nothing, you know almost nothing. Nothing is digitized. So let's start with. Those restaurants that don't even have a KDS solution, take all of the automation and all of the machine learning out of the equation. Why is it so critical to ensure in 2025 that you have a kitchen automation system versus the old, I'm gonna hang a ticket in the window and hopefully somebody makes the food. my wife and I went out to, to, from Mother's Day, this weekend. we're having to be recording this in mid-May in 2025. We were out for Mother's Day and you watch. Just how buried the kitchen and the bar is getting, trying to produce drinks during these times, but they were doing it on tickets, the place that we went to. And I'm like, they have no data to know. Do they need another bartender? Do they not need another bartender? Do they need another line cook? Do they not need another line cook? So for those in our audience that are still. On legacy tickets, talk them through what pieces of data can you get and how can you automate that part of the house. And then let's take that next step after and talk a little bit about why using that data to make better business decisions is so critical. But let's start with going from tickets to KDS. Why is it so critical in today's day and age that you have that?

Sandeep Mahal: 18:58

So in terms of restaurant operator language, I would say. most of the restaurants today are struggling, especially struggling to keep up the operations, to get the profits. And with the trade war happening with every country, the cost, price for the ingredients are going up and up. So this is why technology is really helpful. Let's go back few years. So they were at a time when only the printers were there, so they, everybody was happy with, just taking the order on a printer receipt and just, stuck, stick that, receipt into the wall. And then there was a time when. People say, oh, no, you need, you don't need a dump device. Like printer. Let's get onto the kitchen display system, which can make the sys more efficient. Like you can see the real time display of all the orders. You can see number of items as a consolidated view that you have to cook it much better. But there were still people who were just simply relying on the older way because the staff was used to it and their staff was happy managing the chats. Now at this critical time, when. Data is very important when you're already struggling to keep up the margins and increase the profit. Even if you're not struggling with the God grace, your revenue is really good, but you need to grow, right? You need more revenue and even you want to make your customer happy and your staff to be retained. When staff is organized, they will stay with you. You don't need to retrain them. So considering everything in mind, what we have designed is this kitchen automation system. So data for now, I'll give you another example. There is a order which has chicken wings and fries. Let's say chicken wings takes six minute to cook and fries take two minutes. Just an assumption. So our system knows both item has to be prepared hot and fresh. By the fourth minute, the fries will remain deactivated until fourth minute because it takes two minute. By the fifth minute start, our chef will get alert now start cook fries or maybe pack the fries. So by the end of sixth minute, both item will be hot and fresh. So that matters when you have a lot of customers waiting and there is a delivery also that will go like maybe 20 minutes away. And when the hot food is delivered, customer is eventually going to love it. I know I placed many times orders sometime I even, I know the incident when they, the order was cold, not even warm, lukewarm, it was cold. So I would never go back to similar restaurant again. And this is where technology is helping.

Jeremy Julian: 21:15

And so once you have that data, one of the things Sandeep that I, we've been seeing,'cause we've had a lot of people, they're looking to figure out how quickly on a Tuesday night at 6:00 PM versus a Friday night at 6:00 PM the cook time on that steak or that hamburger is not the same Friday night. They might be more efficient, they might be less efficient, they might have more grill space, they might have less grill space. So being able to give real time feedback for what estimated cook time is based on who's on the line and all of that. Is also really critical. Most systems out there, and I'd love your thoughts on this, just give you a linear path. If I have 10 orders in the kitchen, it's a 60 minute wait time. If I have an 11th order, it's 65 minutes. If I have a 12th order, it's 70 minutes. It takes nothing into account. The restaurant, the pace of service, the items that are on that order is that, are those components that you guys are looking at when you put your machine learning logarithm on top of the data to be able to give estimated wait times back to the guest.

Sandeep Mahal: 22:11

We, yes, those are some of the items, not only items. So other than that, we have preparation time also feeded into the system. When we make any system live, we feed in based upon the customer or restaurant operator input. I. So gradually systems start to learn how quickly they bump it. Even if we have put five minutes for a certain item, if they take five and a half minute, system learning. So how efficient the staff is, so according to that estimation and all the other attribute environmental factor, we control all the websites. So we have direct integration with Uber, DoorDash, GrubHub. What we do is we update all the websites. Next promise time is 65 minutes. Next is 75 minutes if there's a lot of orders already in the restaurant. Not only that, we also know the historical data, so we know at 6:00 PM on Friday evening, maybe busier than 6:00 PM on Tuesday evening. So looking at that historical data, we can update all the website. The less the next lot available is after 45 minutes. So that way even kitchen is not overwhelmed and the consumer who is ordering the food knows when he is really getting the food hot and fresh. Everybody is happy.

Jeremy Julian: 23:18

Yeah, I love that. where, I guess where is it going? Like you guys have built stuff for the last, close to 10 years and it feels really cool from the perspective of you started as this middleware saw a huge problem and part of why I love having founders on that have, solved problems. And again, this is very common, saying that I have tech for technology's sake is worthless. But technology that solves real business problems, you guys truly are, where are you guys going with this tech like. how much farther are you gonna try and extend this kitchen automation on the heart of the house, out to, consumers and out to staff members?

Sandeep Mahal: 23:50

Yeah, so like I said, I started this company with a vision that I want to help restaurant operator, especially when I was with the Yum Brands, I saw a lot of struggle. So my and company vision is to help everybody out there. Not only the biggest chains like Yum Brands or other brands who are already using the similar technology. There are a lot of restaurant. Folks out there who are struggling, we want this, technology to be affordable to everybody and accessible to everybody. Scalable, easily scalable for everybody, and simple and easy to use. That's why, we are spending a lot of our effort and time and energy, even money into the marketing initiatives we exhibit at the trade shows so that people know there's something like that exists in the bucket. Which I see is the challenge right now with us, that we want to get this brand awareness done, everywhere so that more people, more restaurant operators are aware and they can take benefit out of it.

Jeremy Julian: 24:44

Love that. last question. You guys have already used quite a bit of ai. Where do you think AI is gonna continue to enhance the restaurant operation From a technology perspective, because again, I've been doing this for close to 30 years in the restaurant tech space, so I've watched lots of changes, but I, over the last, really since COVID, I. More changes to restaurant tech have happened. How is AI gonna impact both the consumer and the restaurant owners as it relates to things and I guess go out on a limb'cause you guys are already using it. AI has been around forever, but it's been so commercialized now that everybody knows about it. Whereas you and I have probably known a lot of these same machine learning algorithms have been around for such time.

Sandeep Mahal: 25:22

Yeah. Wonderful. I think everybody asking this question and also our restaurant operator. are hesitant. Like they, not everybody wants to use the AI as on today. there are a few people who really understand, if I go back with my example, personal example, back in days, even everybody's example, right? Back in day we were relying on say, newspapers or maybe our friends or some kind of knowledge transfer. Then Google came into the picture. So we started searching everything on Google, right? So what was our answers? Now, you ask any kind of random question, whatever running in your mind. Chad, GPT stuff, right? So this AI is here. So giving you more precise, more accurate answers. Even well researched there, a lot of r and d is going on. A deep seek, like for example, is there, there a lot of other algorithms are being written. Robotics, were tested and tried to deliver food, even in us. so AI IIC has a lot of potential. so because it is, it's not taking away the human jobs, I would say, but it's really helping the. Struggling, the people who are struggling with something to get the job done in more efficient way. AI is really helping mitigate those problems. So not only 2025, if you go down the line, there's a lot of progress already happening. When we exhibit in any ratios, we see most of the technology booths nowadays as like really AI wise based ordering, like there so many examples. So all these pieces, if they are tied together. And able to help restaurant, industry specifically, I'm talking about restaurant only, but overall, overall AI is helping everybody. So there's a lot of potential I would say, and there's a lot of work has to be done here to make everybody succeed and grow.

Jeremy Julian: 26:55

Yeah, no, I love that. And I think, the ability to get to that data faster, easier, and consolidate it, even eight, nine years ago when you guys started looking at this data, it was really hard to normalize. I'm sure it's gonna only get easier and easier. How do people learn more? How do people get in touch? How do people, what platforms are you guys integrated with? And again, I think as I'm sitting here listening, going, oh, I can get that large of an ROI, why wouldn't I at least pick up the phone and call? how Sandeep do they get in charge? get in touch with your team and figure out how, how to learn more.

Sandeep Mahal: 27:24

Yeah, we, we have, our Contact us page. I'll share after this conversation. so they can reach out to us on tech ride.com or our social media handles like LinkedIn, Instagram, or x. we are everywhere so they can contact us. Anybody wants to reach out. It is easy to reach out and, what, like we are integrated to many point of sale system. Let's start with Micros because I worked with Micros, even legacy versions of Micros Symphony 3,700 Square, Lightspeed. There are a few more shift four, infra Gigi that's mostly used in hotels industry. We have payment integration to stripe, azure.net shift four again, Elon, Clover point of sale too. We are integrated so, there are many apps which we are integrated and we don't use third party plugin. Everything is. Owned by us in terms of integration. so it's, yeah. It's one company providing you online ordering integration to food apps like, DoorDash, GrubHub, Uber Eats and everything. Integration to your point of sale system is one company. In back office, you have one company using, kitchen Display System, providing you the automation. We have dispatch app, which can. batch the order, route the order together to deliver. And we have delivery apps too, and we have delivery integration to Uber delivery, and DoorDash delivery as well. yeah, so and so there are a few more things like chaos system and order taker. Like you can pay and, add, your order on a table. So that's all part of our entire package. We charge one$50. And ROI is huge. For one$50 is per month per location. And ROI is, I already told you, 2500% ROI, which is part of our agreement as KPI.

Jeremy Julian: 29:03

Yeah, no, that's amazing. and you very seldom see tech companies that put that out on the line that say, you know what, this is what our ROI is, truthfully, Sandeep. And the thing that I love about yours and my ongoing conversation is one, I. You're making some pretty bold claims, but that you're backing it up. Can you tell our listeners before we sign off, just one quick success story of somebody that you've saved 2500%, you don't have to mention a brand name, but what were they doing prior to you guys getting on board with them? And then how did you show such a large ROI back for their, for their brand?

Sandeep Mahal: 29:34

Yes. So that's a funny story. not really. So one of our customers in one big country, they made biggest ever sales because of using the entire system, part of our integration and their kitchen display and everything. So millions of transaction and everything worked very well. In one day, their sales skyrocketed and they achieved 56 months of positive month on month growth after implementation of

Jeremy Julian: 30:01

Hang on. Say that again.

Sandeep Mahal: 30:03

50 56 months of consecutive. Yeah.

Jeremy Julian: 30:07

That's insane. Sorry, I don't mean to, I don't mean to stop you, but it's I was like, no. There's no way. You just did 56 months of consecutive sales growth.

Sandeep Mahal: 30:14

That's published on LinkedIn and it's a big brand global brand. Yeah, so come reach out to me. We can get this data validated. And, not only that, so the how the 2,500, percent ROI is translated, one is no chargebacks, like you mentioned, Domino's. If 30 minutes, delivery is late, they have to give chargebacks. So no chargebacks, a hundred percent on time deliveries, customer satisfaction score, we have seen increase, which brings more profit. Labor cost reduction around 10%, 8%. That also huge saving. So that's contributed to$4,000 in saving and this further this system when in used in conjunction with online ordering and all that. There's more and more. ROI. We are just talking about kitchen, ROI at the moment. No order, no other

Jeremy Julian: 30:58

the chargebacks, the, all of it. It's amazing. you, you said, so tech ride, is RYDE by the way? Not RIDE. So just, I don't know what, I don't know where I'm gonna wind up if I show up at T Tech ride, T-C-H-R-I-D-E, but, but, RYDE. And Sandeep, is there anything else that we missed that, that you would want our listeners to know other than, Other than to get in touch with you guys to get some guaranteed, guaranteed ROI if they, implement the solution.

Sandeep Mahal: 31:21

Yeah, thank you. in case you, your listeners or our listeners have any other question, reach out to me, maybe related to their point of sale, maybe there's some struggles going on. We can definitely have a chat about it. And we also exhibit in trade shows, like NRA. Or other trade shows as well. So perhaps you would see us in one of those exhibitions as well. Come see our products and you will see the real time ai and then somebody want to see a demo of artificial intelligence and all. We have entire demo system available where we can put some scenarios out. Somebody ordering fruit from this distance to another distance, how system is sequencing the order. We can show it on the real time.

Jeremy Julian: 31:58

Love that. I, I would love to, stop by next week. I'll be at the show, in Chicago. I know this episode won't get out till after that show, so hopefully I'll have had, had a chance to meet in person. Sandeep. good luck, continuing to grow. To our listeners, guys, thank you guys for, for continuing to tune in. If you haven't already subscribed, please do so on your favorite player. We're also on YouTube. share the show with somebody that you think might benefit from it. And Cindy, thank you so much and to our listeners, make it a great day.

Sandeep Mahal: 32:23

Thank.

32:26

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