Full transcript of How AI Is Transforming Drive-Thru Performance — Eric Lam — episode 343 of the Restaurant Technology Guys podcast, with Eric Lam, CEO of Berry AI. Speaker labels preserved; lightly edited for readability.
What this episode covers. Eric Lam, CEO of Berry AI, on using computer vision to measure and improve drive-thru performance. The number that frames the conversation: for every seven seconds of improvement in drive-thru speed, restaurants can see up to a 1% increase in revenue. Eric covers why loop timers embedded in the pavement fall short, how speed numbers get manipulated, when a second lane helps and when it does not, the operational decisions the data actually drives, and the three chapters he sees for computer vision in restaurants.
- In this transcript:
- Eric Lam and the Road to Berry AI
- Two Ways to Look at AI in the Drive-Thru
- Where the Revenue Actually Comes From
- Why Loop Timers Fall Short
- How Speed Numbers Get Manipulated
- One Lane or Two, and Why It Depends
- The Operational Decisions the Data Drives
- Eric’s Biggest Takeaway for Operators
- The Big Announcement
- Three Chapters of Computer Vision in Restaurants
- Who Berry AI Works With
Jeremy Julian (0:01): Welcome back to the Restaurant Technology Guys podcast. I thank everyone out there for joining us. As I say every single episode, feels like thank you guys, because I you guys have got lots of choices out on the internet to go spend your time and energy. So thanks for hanging with us. Today, I am joined by a founder and a very, very bright guy who I got the chance to hear last fall. And I’m excited to have Eric jump on the. on the episode. Really, we’re going to talk quite a bit about where things are going and some of the old ways that people used to solve the problem that he and his team have been solving for and then kind of where it’s going. But Eric, before we jump into kind of what you’re doing with Barry, who is Eric? Give me a little bit of background. Where did Eric come from? And then we can talk a little bit about what you guys have been solving for because I’m pretty excited to dig in.
Eric Lam and the Road to Berry AI
Eric Lam (0:47): Great, well, thanks for having me on, Jeremy. My name is Eric, CEO of Barry AI. uh I come from a really unique background where it is a family business that manufactures POS devices. And so I did not have a normal childhood like my peers did. I grew up playing around with POS terminals, taking them apart and putting them back together. So that was from an early age how… I got involved and fascinated into the world of restaurant technology. And really that kind of paved the way for the rest of my career after I went to business school, returned to family business and had the chance to kind of invent a new chapter for the business. And that’s what became Berry AI. So I’m an engineer by school training and then a entrepreneur at heart. It’s very exciting to me to be chatting about the future of QSR here.
Jeremy Julian (1:48): I love it. And you didn’t go to just some basic business school. Come on, give our listeners a little bit of a heads up that you went to one of the top business schools in America, I think.
Eric Lam (1:53): you I had a chance to go to Harvard Business School. Great two years there, learned a lot. they teach you a lot of things there. They also don’t teach you a lot of things there. So that’s the biggest takeaway that I learned. The many things you cannot learn in business school.
Jeremy Julian (2:13): Well, it’s the entrepreneurial journey, right? The figure out ability. tell my, I just had a kid graduate undergrad this weekend and I was like, dude, half of it’s about how to figure it out as much as is what they taught you in those books and in those lecture halls, right?
Eric Lam (2:27): Yep, that’s exactly right.
Jeremy Julian (2:29): I love it. So talk to me a little bit about Barry. I give us an overview? What is Barry AI? And again, we’re what? Two minutes into the episode, we’ve already used the word AI. we’ve got our quote. We started our quota off for the day. But what is Barry AI at a macro level before we start to dig into the technology and why you think it’s so unique and different?
Two Ways to Look at AI in the Drive-Thru
Eric Lam (2:50): Yeah, that’s a great question because AI is the buzzword these days. So it’s really important to really dig deep. What is the AI? What are we doing? uh From a high level, Barry AI is the vision AI solution provider for QSR. So we specialize in computer vision, which is analyzing what cameras see through the videos, detecting people, detecting behaviors, detecting vehicles. um In the QSR world, there’s a lot of things that we can do ah with these vision analytics. A good example is we would be able to track speed of service really well and drive service for a QSR restaurant. So macro level, um think about Barry AI as trying to use cameras to obtain more analytics and intelligence from the way restaurants are operating today.
Jeremy Julian (3:49): So, Eric, real quick, just for those that are not QSR experts or have never really understood the value of a drive-through, uh I’ve got somebody on my team that used to work at HME, and he and I will talk about kind of the value of what he was selling headsets for that. And we kind of talked about some of the numbers in drive-through and… for those listeners that either have a drive-through but have never really dug into the analytics of how much more valuable speed of service timing is and all of those kinds of things, as well as just in general, the exponential growth you can have with a successful um drive-through. guess talk to our listeners a little bit about kind of why that’s such a value proposition to ensure that you’re driving the volume that you need to through analytics and really just in general, how you can increase your top line sales if you do it properly.
Where the Revenue Actually Comes From
Eric Lam (4:38): Yeah, I think there’s two angles to look at this. So first is to look at it from the restaurant angle. So, you know, quick service restaurants is what QSR stands for. It literally has the word quick in it. And people go to these brands, these establishments with an expectation that they can get in and get out and get their food really quick. um So there’s a lot of well-known studies um into how long waiting will impact your sales. Um, you know, the, number in the industry is every seven seconds that you can improve in your spirit of service equates to one extra percent of revenue that you can generate for your restaurant. So there’s, there’s, yeah, that’s, mean, people have an actual translation and my very brand to brand, uh, but every brand, um, their ops leaders will have some way of translating. If we can just shave a few extra seconds off of our drive through.
Jeremy Julian (5:21): That’s insane.
Eric Lam (5:37): this is how much more revenue we can generate. And the other angle, think, is probably even more intuitive to listeners who may not be as familiar with the QSR space, is just to think about it from a consumer angle. We are all consumers um in the market. We uh live in an era where we are not accustomed. We’ve been spoiled to not wait for things. YouTube can save a YouTube premium plan because people don’t want to spend 15 seconds watching an ad.
Jeremy Julian (6:00): Mm-hmm.
Eric Lam (6:06): And so if people are accustomed to this, um everything’s on demand. can get anything you want within seconds. um Just think about your own personal journey. If you’ve ever been stuck in a drive-through and the difference between a four minute wait and a five minute wait might mean next time you’re hungry, you don’t go to that establishment. And so I think that’s the easiest, most intuitive way to think about it. Hey, if any restaurant can just convince people that they’re moving along a little faster, it. it convinces consumers to come back frankly at a higher frequency.
Jeremy Julian (6:42): Yeah. Well, and, and this isn’t, mean, your guys’ technology is not, it’s new the way you guys are solving the problem, but drive-throughs have been measured forever. We’ve all been to a drive-through, again, I’m older than you for sure, but I’m older that I remember the drive-through window with the stupid clock and, these guys are rushing around. And so it’s not new what it is that you guys are doing from a drive-through timing perspective. But I guess talk to me a little bit about traditionally how, how people historically had done it.
Eric Lam (6:48): Thank you.
Jeremy Julian (7:10): loop timers and some of those kind of things. And why do we think that computer vision AI is such a huge leap forward? I got the privilege to listen to you and the team talk, you know, last fall and I was blown away. I literally walked out of there and I got so educated. when you guys reached out to be on the show, was like, dude, I need the world to hear this because I was so enamored with the different things that you guys consult for that the traditional systems hadn’t or can’t solve for because of the way the technology was built. know, 20, 30 years ago when it all got started. So I’d love for you to talk through what is the traditional method and why do we think the computer vision AI is such a leap forward for things.
Why Loop Timers Fall Short
Eric Lam (7:48): Absolutely. So the way things have always been done is QSRs use a technology called loop timers. They are magnetic inductive loops. Imagine just a metal wire that is buried under the pavement of the drive-through. And so anytime you’re in a QSR drive-through, if you’re at the pickup window, you look down, you might see a rectangle um on the pavement, and that’s a loop timer. It basically is a metal detector and it detects when cars are stopped over it. This technology is quite mature. It’s been around for a long time. And it’s basically the way that QSRs have measured drive-throughs for the last 30 years. Now, loop timers have always had a few critical flaws. Number one, they only measure two points in the drive-through because of the way they’re designed and the height. kind of labor costs, installation costs, to dig up the concrete and put down the loop timers. You can really only put it at the pickup window and at the menu board. And so that’s only measuring part of the customer journey. So that’s the first challenge. The second challenge, and one that we ran into with a lot of our customers, is that loop timers are very easy to manipulate. ah What’s most fascinating is that when these brands, QSRs, try to actually improve some of the service, many times they’ll start setting goals or bonuses for their stores that if they can achieve certain speeds on a loop timer, then they get certain bonuses. That actually incentivizes the behavior to manipulate these sensors because they’re not really intelligent sensors. They’re really metal sensors. So we’ve heard of instances of staff taking metal trays and waving it at the window and above the loop timer to trigger faster reads on the car. Or another frequent uh issue that happens is oftentimes the food will not be ready when the customer arrives at the window. And they’ll ask the car to drive forward and wait at a parking spot. And we call that pull forwards. ah Sometimes that’s a good operational decision.
Jeremy Julian (10:04): Mm-hmm.
How Speed Numbers Get Manipulated
Eric Lam (10:08): Many times it’s a way of manipulating the speed of service as well to get your car off of the loop timer. um Finally, the biggest challenge with loop timers as well is that it’s very difficult to maintain and it’s very difficult to install. So sometimes snow or increment weather will damage the loop timers. You need to shut down your drive-through. You need to dig up the loops and then reinstall new loops. um Those are kind of the high level challenges of loop timers. uh As you expect, computer vision or cameras in the drive-through naturally solve each one of those problems. So when we put in cameras in the drive-through, we’re able to stitch together the view across multiple cameras so that we know the moment a car arrives in the parking lot, joins the drive-through queue, the timer starts. We can track that all the way through the ordering process, through the pickup window process. Even if they get pulled forward, we can keep tracking that journey. And so it’s really for the first time that the industry can measure the end-to-end guest wait time, which many customers, many QSRs believe you really got to measure what the customer is experiencing to know what you’re optimizing for. So that’s number one. Number two. um
Jeremy Julian (11:33): No, no, no. was just going to say, I was just going to say one of the things that I, that I also heard you guys talk about is just the flexibility with loop timers because you lack flexibility. You can’t go to two lanes. can’t do anything else in the drive through other than kind of force the one path. And so if there’s something going on in the parking lot, you get stuck without the speed of service data. So sorry, I didn’t mean to cut you off, but it’s like, there’s a lack of flexibility because it’s extremely expensive. It’s a point to point and there’s not a whole lot of, you know, there’s not a whole lot of variability. So if they’re doing something.
Eric Lam (11:44): Yep. Exactly.
Jeremy Julian (12:02): in the drive through, you know, they’re changing out the oil and the trucks got to be parked kind of somewhere where they might be triggering timing, you’re going to miss some of those people if you’ve got the line that’s too long before it you know, you lose out on some of those capabilities. And so, you know, until they hit the menu board, so sorry, I’ll let you keep going. But but I just I wanted everybody to listen to hear that says is not just those couple of points. It is also this whole flexibility because we’re getting more and more creative with the ways that people are doing drive-throughs because it does drive, get satisfaction. So, sorry, I’ll let you keep going.
One Lane or Two, and Why It Depends
Eric Lam (12:26): Exactly. Yeah, and I’ll add one more point to that as well, is that drive-through is becoming an increasing part of QSR’s business. uh Many brands are now expanding from single-lane to dual-lane drive-throughs, and the drive-through operation is getting more complex. And the biggest challenge is that oftentimes there’s no standardized playbook at the store level. uh stores are kind of tasked with the mission of, you should adapt how you operate the drive-through.
Jeremy Julian (12:41): Mm-hmm.
Eric Lam (13:01): based on the situation. So sometimes it’s two lanes, sometimes it’s one lane. They need to react very dynamically. And that’s one of the challenges loop timers will run into is, pick one or the other one lane or two lanes. can’t just switch between the two that the cameras also address. But back to the original point of some of the advantages of computer vision for speed of service, the other big advantage that we talked about is the the ability to actually provide accurate data that cannot be manipulated. So traditionally, if staff were pulling cars ahead, um that timer doesn’t stop in a computer vision world. If a staff is waving a metal bin above the loop timer, uh the camera’s not fooled by that. And finally, the installation and the maintenance of the cameras is a lot simpler and easier than loop timers. You don’t have to shut down your drive through.
Jeremy Julian (13:57): Mm-hmm.
Eric Lam (13:59): If one of the cameras goes down, rest of the uh cameras and the timer still works. So overall, I would say it’s easier to maintain a more robust system.
Jeremy Julian (14:10): Well, Eric, you said that there are certain operational reasons why you would want to do pull through. And again, I got to hear you talk on stage about why you would want somebody to pull through versus not. And I guess I’d love to listen to your thoughts on what makes sense operationally to pull somebody through versus not and how really computer vision can help tie that transaction to that car that got pulled through versus the one that shouldn’t have gotten pulled through. So I guess why don’t you educate everybody? What is a good
Eric Lam (14:17): Thank
Jeremy Julian (14:39): good sense of pulling somebody through. Why would you tell them to go pull into a parking spot or pull through to the front of the drive through so that you can run food out to them? And how can you with computer vision, you know, like really even tell whether that was the right thing to do versus not, whereas you can’t with the loop timers.
The Operational Decisions the Data Drives
Eric Lam (14:56): Yeah, so the answer is that it depends on the concept. And I can kind of give a few examples. So generally from a high level, you want to pull the car forward when that car is going to have a long wait time. Now, depending on the concept, that long wait time might be triggered by different things. For example, if they’re ordering a very large basket, very large meal, uh the equivalent of five or six people’s worth of meals. um in order to keep the line moving, that specific item, chicken fingers, fries might not be ready right away. so in scenarios like this, it’s often best practice by these brands to pull the car forward so that they can actually start serving the cars behind that big order cards. For some other customers, many brands today are cooked to order. So they kind of promote that they are the most fresh, best food quality. They don’t prep things ahead of time. so in instances like that, um when cars order, you are actually pulling most of the cars forward because the food is being prepped only after you order. And so in scenarios like this, you want to make sure that um your staff are incentivized and they’re trained that they’re also pulling these cars forward. Now, where you want to draw a fine line is when there’s not scenarios like this and it’s just someone, a staff kind of manipulating the speed of service times. And the way we tackle that from a computer vision standpoint is that we’re actually able to look at both the POS data as well as the um drive-through uh information in real time to know, hey, was this car being pulled because there was a long line behind them and the staff probably wanted to get the line moving. Or was the car being pulled when there was actually no car? There was no car in the drive-thru. It’s 10 PM at night, and there’s really no excuse for why this car should be waiting. There’s no car waiting behind them. And so we’re able to differentiate between those and also look at the POS data and the receipt data to help these brands look at, these pull forwards kind of by the book, or were they more likely to be something we call like a flag pull forward that’s potentially a manipulation of the timers.
Jeremy Julian (17:24): Yeah. Eric, one last thing on this whole kind of thread before I jump into some of the things that people say from a downside on it is data is amazing. Data is amazing if you can do something with it. But the thing that I continue to talk to operators and executives that are building technology is how do we make this data actionable? How do we make it so that we can solve true guest problems as well as restaurant problems with this? because just throwing a whole bunch of data at it and changing it from loop timers to this, they’re humans, they’re either gonna find a way to go manipulate these systems and or they’re gonna be inundated with data without the ability to execute against it. So I guess I’d love for you to talk because again, I heard you and the team tell some stories last fall that I was blown away with that it’s not just data for data sake, but it’s truly helping make the business better. both from a guest perspective as well as from a staff perspective so that it’s not this like, ah, know, they just want, want, want, want what, you know, they need to be able to solve that problem. So I’d love for you to share a little bit about how you guys are using the data that you have to create a better guest experience as well as a staff experience.
Eric’s Biggest Takeaway for Operators
Eric Lam (18:37): Absolutely. um My biggest take, and this is a big learning for myself as well, as I’ve been in this industry, is that less is more. And this is really something that took years to, I think, internalize what that meant. And I think it manifests in two ways. So I think when we think about how do we make this simpler and more actionable for restaurant staff, the first approach we take is
Jeremy Julian (18:47): Mm-hmm.
Eric Lam (19:04): We want to make sure we can deliver information in real time because when you deliver in real time, then there’s actually enough time to fix it right away. Um, and so, you know, part of our product is a real time dashboard in the restaurant itself. And we iterate on the design a lot. We want to make sure even for someone who doesn’t fully doesn’t have, for example, great command of English, they can understand this dashboard. should be so simple. sort of like your IKEA furniture assembly instruction guide, that someone with no training can look at your dashboard and say, oh, you know what? I can see eight cars in the drive-through. So I don’t need training. I know that means I’m going to run out of fries in five minutes. And so it took us a lot of iteration in terms of what are the best metrics? What are the easiest ways to present this information so that it doesn’t take much training for staff? especially in poor and QSR when there’s such high turnover. So that’s the first angle we looked at things. The second is from the detailed reporting that restaurants get from our system at the end of every day and at end of every week. And that’s really where, you know, initially without being careful, we inundated our customers with too much data. We gave them every single metric they could have possibly ever wanted for. ah And it took a deep collaboration with our customers to start cutting down from that and saying, Hey, we only need to focus on these three or four metrics. And for all the rest, that’s great. Let’s put that somewhere else. If someone’s really passionate about it, they can go look at it. But for the most standard reporting, um let’s really boil it down to, customize metrics. And, know, at Barry, we really take it even one step further to reduce that training burden. Everyone has different terminology. for the same thing. Some people call this experience time. Some people call it total journey time. Some people call it customer time, et cetera. So we also make sure that in our system, it’s designed to be flexible enough so that we can customize it to the customer’s vernacular or their terminology that they’re very used to. And all these little things are what we pay attention to so that when people look at our system, it’s very obvious where the bottleneck is. um
Jeremy Julian (21:02): Yeah.
Eric Lam (21:30): We like to say that our system, our goal is not to get you to spend more time on our dashboard. Our goal is to make it so that you can get a quick glance and know what you need right away. ah We’re not optimizing for how much time you need to spend on our website.
Jeremy Julian (21:46): I love that. Eric, I’m going to take a pivot real quick and ask about, you hear a lot about computer vision and the part that I guess I hear from consumers or customers is how do you deal with the privacy factor? How do you deal with the fact that you’ve now got cameras? The loop timers was fine because you knew it was just measuring the bottom of the car. You’ve got certain states, certain legislation that says, we can’t capture images and certain you know, things. for those listeners out there going, it would never work in my state, in my store because of that. I’d love for you to kind of, guess, dispel that myth that says we’re not capturing PII. We’re not doing any of that, that kind of stuff. So love for you to talk through a little bit of that, cause I’m sure you get that, uh, get that question from time to time.
Eric Lam (22:36): Yeah, that’s great question. I would have a few advice for anyone out there who’s thinking about this privacy question. The first is really be very specific and be very narrow in terms of what the use case is. What are you using cameras for? It’s not that any form of camera recognition is treated as same. so PII, personal identifying information, is really the key here. When camera systems identify any identifying information, whether that’s who you are, like facial recognition or vehicle license plate on a car, um those are all kinds of um identifying information. So that’s not illegal. That’s just you have more disclosure notices if you’re doing that kind of use case. where we’ve kind of been very intentional about spending our time is making sure we don’t kind of take that step into identifying information because quite frankly, there’s so much value that we can do even just from an anonymized uh fashion. So we don’t need to know, hey, this car is Eric, this car is Jeremy, or this car is license plate ABCD. We just need to know this red car spent five minutes and 30 seconds in the drive-through. And so when it’s use cases like this that are anonymized, not identifying. Your cameras, at least as of today, are regulated in the same way that security cameras are. The other piece of advice I would have is obviously, AI is evolving quickly. Different states are having different approaches to it. As of today, no state is going to say, you are Just because you have cameras means you’re capturing personal identifying information. And so my other advice is if you’re engaging with a camera vendor or a computer vision vendor, ah it’s worth asking them about what’s their understanding of the latest regulation this space, because it is um changing. The last thing I’ll mention that we take an extra step of precaution at Barry AI is that we actually don’t keep any of the videos. all of the videos that we process from the cameras, they’re processed on the server inside the restaurant. And once the AI analyzes all the cars, all the journeys, that video is no longer stored. And so from a regulation or compliance standpoint, that’s actually the easiest uh for ease of mind for restaurants.
Jeremy Julian (25:19): Yeah, no, and I love that. Eric, I know we’re recording this the first week of May in 2026. I knew that we were waiting till earlier this week to have this recording, partially because you guys had some pretty exciting news. Funny enough, I got the email from one of the QSR magazine, I think, uh hit my inbox, and I knew that we were getting started to record. I guess I’d love for you to share a little bit about your guys’ huge win, because uh it’s a pretty big one. It’s a pretty big one and I guess I’d love, you know, it’s already kind of in the press release, but for the listeners that may have missed this, who did you guys just land and kind of what drove that decision for their brand forward with Barry?
The Big Announcement
Eric Lam (26:00): Yeah, so the big announcement that we had a few days ago was that Culver’s restaurants has announced that they’re going to deploy Berry Eye to their stores nationwide this year. And so that’s over a thousand locations and it’s uh another brand in the QSR space we have chosen to lean kind of completely into the computer vision technology and uh really use this as an opportunity to leapfrog uh some of their peers. um The Culver’s journey, know, we’re very grateful to be working with them. They’re very forward thinking leaders on the team. They’re actually coming from a space where previously they were not using loop timers. They were not using these types of um systems. uh So to them, they did not have the visibility. to really think about and measure speed of service. Culver’s has always been a restaurant that prides itself in everything cooked fresh, made to order. But as it’s expanding kind of nationwide and expanding into new markets, it’s entering new markets where customers may not be as familiar with the Culver’s brand. And they don’t know that it’s cooked to order. They don’t know that. When you go to Culver’s, you need to wait, you know, five minutes, 10 minutes for your burger, even though it’s delicious, it’s a bit of a longer wait than some of the other brands. And so when we chatted with the Culver’s team, that was some of the thinking behind, why, why invest in new technology like this? Why really focus on speed of service is because, you know, in new markets, when no one understands your brand, that’s a basic expectation that folks have. And so it’s a, it’s a It’s a long journey that we worked with Culver’s. It started with small scale pilots, and then we expanded to larger pilots. And throughout that journey, incorporated feedback from pilot franchisees and tweaked the product, adjusted our terminology, et cetera, and really made sure to find a way to make it work for their needs. So that was a big announcement a few days ago.
Jeremy Julian (28:16): Well, congratulations. And uh I’ve got a butter burger on the way. I got to go figure out how to go find me one. There’s a call where it’s maybe 20 minutes from me. I got to head up that way later this week. So have to check it out.
Eric Lam (28:22): There we go. Yeah, my favorite’s the mushroom Swiss.
Jeremy Julian (28:31): Yes, that is a pretty darn good burger. A um Culver’s meal for me is not complete without some cheese curds, so there’s that. I love it. Eric, we’ve talked a lot about DriveThru. guess, where is the computer vision technology going? We’ve had a guest on the show who was really doing inside the store stuff. Are you guys trying to stay outside the store on DriveThru? Are you guys trying to work on some of the speed of service and some of the other areas that you guys can improve the guest experience?
Eric Lam (28:34): Yeah.
Jeremy Julian (29:00): within the four walls or even in the drive-through, guess I’d love to listen to you kind of riff a little bit about kind of the product roadmap and where do you see not only this technology but very AI going to help you really create better guest experience, better staff experience for restaurants out there.
Three Chapters of Computer Vision in Restaurants
Eric Lam (29:17): Absolutely. So we kind of described three chapters to the Berry Eye journey. And we really just finished chapter one and now entering chapter two. So when we started off, we really wanted to find one specific use case that cameras could solve and that restaurants would care about and really adopt. And that to us was the drive-through timer. And so We’ve been very focused on that. We’re very grateful to be working with customers, Culver’s, Zaxby’s, other brands who have fully adopted this type of drive-through camera timer. Chapter two is really where a lot of our customers are now kind of guiding us or pulling us towards is they say, hey, you know what? I don’t want two camera systems in my restaurants. I have one set for the drive-through, for the AI, and then I have another set of security cameras inside the restaurant. ah They’re two camera systems. They don’t talk to each other. And so, you know, Barry, can you guys figure something out and really unify this experience? so chapter two for us is really becoming that security camera provider as well inside the store for the restaurant so that these restaurants can have a unified experience with their cameras. And if they see a very slow um speed of service, a certain day part, then they can pull up the video right away of what was happening in the kitchen. or they can pull up the video right away of what certain item was not prepped ahead of time, et cetera. So that’s really chapter two of our journey. And what excites us the most, I think, is chapter three, which is when you have a lot of cameras, there’s really a lot of things that cameras can be detecting and analyzing. what’s cool about this is that at this point, the restaurants really become your thought partner because they’ll become…
Jeremy Julian (31:08): Mm-hmm.
Eric Lam (31:09): very inspired, hey, can you also track how many boxes of inventory that were just drop shipped? Hey, can you also check how long the fries were sitting out? Can you check if the tables are clean, if we’re taking the trash out on time? From a highest level, the way to think about it is, from a brand standards, operation standards perspective, there’s hundreds of tasks that you need to be checking manually and auditing. um Any of those tasks that you can do with a human eye, is something we eventually want to be able to help with with cameras because no one signs up to do this job to be auditing how long the fries have been sitting, you know, in the holding bid. Those are things that we believe cameras are really well suited for. They can work in the background. They can work quietly. They can audit things based on defined SOPs and that will allow brands to let their staff go focus on what brings better experience to the consumer. and actually spend their time interacting with guests and taking care of the employees and not having to check how many boxes were dropped off. ah So that’s high level how we think about how we evolve as a company. think really, we’re only just getting started with what cameras can be helping folks do operationally and inside of restaurants.
Jeremy Julian (32:30): Yeah, no, and I think it’s, uh I think for me, it’s one of those amazing things that the cameras have gotten inexpensive enough. They’ve gotten, you know, easy enough to install and the data is so good and so rich. And it’s not essentially, I’ve heard people go, oh, you’re just going to slap people on the wrist. But at the end of the day, you’re getting them to get to the right place to be able to do the job that they need to do and not. get stuck, you know, managing checklists and doing those kinds of things. So it’s automating so much of that so that they can truly be in the hospitality industry because that’s why we go out to eat is to have an experience to enjoy the food and to, create that guest experience. so if the, if the computers can help train them on how to do those things better, I think all of us will be better off with the team members in the store as well as the guest experience. So I love that you guys are leaning into that Eric.
Eric Lam (33:18): Yeah, absolutely.
Jeremy Julian (33:20): Awesome. So talk to us a little bit. How do people get in touch? How do people learn more? What can they expect the experience to look like if they choose to, you know, they got to the end of this and they’re like, okay, I’m 30 minutes into this. I need this for my drive-through. I need to figure out how I can get what Barry’s doing. Who are your guys’s, you know, kind of clients that you guys are looking at? How did they engage and what would the experience look like if they were to reach out?
Who Berry AI Works With
Eric Lam (33:44): Yeah. So we’re open to working with any QSR, um whether it’s drive-through or you don’t want to measure speed of service inside your restaurant, we can also do that. um The best way to get in touch with us is to come to our website, Barry-AI.com. You’ll be able to see different case studies, different product information. And if you get in touch with us, we’ll jump on a demo and give you of in-depth. understanding of this is what the product will deliver inside the store. This is what your reporting will look like from an email standpoint. This is what you’ll be able to see on our web dashboard. um So you can really get a feel of what the product is and what you can get out of it. And then we’ll talk about letting you try it out. Most folks try it out at one or two locations to start and really that’s when they can see, what do we get from this? How do we act differently if we have this information? That’s the best way I encourage folks to get started is to try it out. It’s pretty low cost to try out and chances are there’ll be a good way for you to think differently about how you’re in your drive-throughs and how you identify bottlenecks.
Jeremy Julian (34:58): Yeah, and I say it all the time. If you’re not doing it, likely your neighbor is to your right or to your left. And so I would encourage anybody that’s got a drive through, anybody that’s trying to measure speed of service in a different way. Eric has talked a lot about kind of what they did and why Barry exists and how they’re really solving these problems. So for listeners guys, we know that you guys have got lots of choices. Thank you guys for hanging out. Eric, thank you so much for jumping on and to our listeners, make it a great day.
Eric Lam (35:25): Absolutely. Thank you, Jeremy, for having me.
