Here’s What Innovation Looks Like - Amir Hudda, CEO of Qu (Part 2 of 3)

In this 2nd part of our 3-part mini-series with Amir Hudda, the CEO of Qu, we discuss technology innovations that improve transaction speed, in-store redundancy, and uptime.  

We dive into innovation areas like Edge Computing and how it helps reduce data loads (speed) when implemented on modern cloud architecture. We also discuss Voice Ordering and how Qu was "accidentally" led to integrate voice into our mobile reporting App, Notify.

Bonus content: Learn how real-time POS data can drive pricing, promotion, and production optimization. 

Key Insights

🎙️Removing technology as a burden
You cannot afford downtime as a restaurant. You need technology with real-time capabilities and no lag times for optimal guest experiences and team member efficiencies. 

🎙️The Definition of Edge Computing
Edge Computing brings data  closer to the point of purchase, with deeper computing power to the store level. Results: Improved speed and redundancy.

🎙️Experiments with Voice Ordering
Voice ordering had multiple hops to the cloud and created lags for guests. We needed redundancy and speed. This was the genesis for looking into edge computing.

🎙️Doubling Down on Innovation During the Pandemic
While many companies had layoff and less innovation, our investors asked us to innovate in a way that would help the restaurants come out stronger. Edge computing was integrated into our platform during this time.

Related Resources
Making Technology Invisible in the Restaurant – Amir Hudda CEO of Qu (part 1)
Notify: App that Saves Franchisees Time & Money
How Edge Computing is Revolutionizing Restaurant Tech

Connect with Amir on LinkedIn

Check out Qu's Annual State of Digital for Enterprise QSR & Fast Casual Brands

Transcript

Restaurants Reinvented - Amir Hudda

[00:00:00] Amir Hudda: So, part of being innovative is how do we take technology into areas where there may not be an immediate need for it, but there could be an immediate value for it. 

[00:00:41] Jen Kern: So, one of the things that we've been talking about here recently at Qu is edge computing. 

[00:00:47] Amir Hudda: Yeah.

[00:00:48] Jen Kern: And so, I'd like for you to define for us what is edge computing and why is it so critical to the future of restaurants, and particularly for the restaurant tech stocks.

[00:00:57] Amir Hudda: Yeah. So, edge computing has been around and, and has been used very effectively in several verticals; it's essentially, in its simplistic form, bringing technology closer to where the computing needs to happen for a variety of reasons you might want to do that. We at Qu didn't start about saying, "You know what, let's build edge computing for the restaurant industry."

[00:01:26] Yep. That was not the goal. That was not even what we set out to do, but this is actually something that happened just at the start of the pandemic. Fortunately for us, we have investors that have deep conviction in the restaurant technology industry and in what Qu is, has set out to do. So, when the pandemic happened, you know, we, I remember so many of our partners in the technology space, some of our competitors in the restaurant technology space, you know, there was this, this shock, right?

[00:02:00] And instantly, people went into cost-cutting mode, and I remember how many companies were laying off employees and really making sure that they could survive the next 3, 6, 12 months, nobody knew how long this was gonna be, and fortunately for us, our investors came and said, "Look, this is the time to take the extra bandwidth that we might have while we're waiting for our customers to be ready to start deploying again, to start evaluating again, let's think of what we can do to invest,

[00:02:33] and build stuff that might be important for the future." And so, not only did we not lay off anyone, we actually doubled down on our technology investments. And I remember we started out a little project, it was almost like a, a skunk quotes project to see where you could use voice because voice ordering was sort of, kind of on the horizon,

[00:02:53] some people were experimenting with it. And so, we said, "All right, let's see where the technology is today to do voice ordering." And in about three months, we realized that you can get to 90% accuracy very quickly using off-the-shelf technology. The remaining 10% was extremely difficult, and when you think about voice ordering, let's take, for example, a drive-through, that guest experience is critical.

[00:03:18] You can't expect somebody sitting in a car that's talking to a voice bot somewhere where there is a lag, or a delay, or, you know, the translation is not very accurate, and what we learned was when you're doing voice ordering, there are multiple hops to the cloud because you first have to translate, do your speech-to-text, that takes a hop.

[00:03:42] Then you get the text back, then you go, have to go back to the cloud, to the POS system to figure out what it is that, that is being ordered. That lag, even if it's half a second, or a second, which is what it was at the time, is not acceptable, right, from a guest experience perspective, we as consumers then think we would use that technology. So, we realize that not just voice ordering, but there are so many other things that need deeper computing power in the store. Think about, uh, what Amazon does really well, right, or any other company that has had huge impact on our purchasing behaviors, whether it's Amazon, or even what Walmart has done for retail, that ability to give meaningful alternatives,

[00:04:29] build real-time capabilities to do promotions and pricing optimizations. Think about in the restaurant industry, being able to do production optimization, how much do I have to build given what orders I'm seeing in all my digital channels, whether it's my native channel or a third-party channel. All of those things require pretty heavy computing, some things you just can't do when you're going back and forth to the cloud every time.

[00:04:55] And sometimes you just need data at the point of purchase that you just don't have. And so, that was the genesis for us to start looking into an edge computing device that can offer speed and increase the redundancy of your cloud operations. I mean, for us, we were fortunate that our cloud architecture was already second generation before our, our partners and our competitors were still, you know, working on first generation.

[00:05:25] So, we had the luxury to take a step back and really think about if you're gonna do edge computing, what are the problems we're looking to solve? And for us, that redundancy was critical, right? In a restaurant, you just cannot be down ever, right? You have to be able to take that transaction process, that transaction, no matter what.

[00:05:46] So, we already had couple of layers of redundancy in our architecture. We realized that with edge computing we could add a third layer of redundancy but also add the speed that is required for some of these types of applications that are going to happen, they may not be here today, but we know it's gonna happen.

[00:06:05] So that was the genesis of, of why we ended up building our edge computing device.

[00:06:11] Jen Kern: Mm-hmm. So, it was originally around the voice. We were looking into voice and looking at some voice-activated systems, which was hot, and still is hot, 

[00:06:19] Amir Hudda: Yeah.

[00:06:20] Jen Kern: but it turned out to be something that is really, I believe, helping the in-store services, the uptime, one of the ways we describe it is bringing the public cloud down to the store level.

[00:06:33] Amir Hudda: Yeah. Can you talk a little bit more about how the in-store services have increased uptime by using this edge computing problem?

[00:06:41] I'm, I'm glad you brought that up because that's another huge differentiator and value add, right? When you think about, if you're using public cloud architecture, which most companies are still not doing, right? Taking your old legacy client-server, and taking the server and moving it into a data center in the cloud somewhere, is not a true cloud architecture, right?

[00:07:03] It's not multi-tenant, it doesn't offer all of the benefits from whatever, whether you are using AWS or Azure or Google, Google cloud, you're not really taking advantage of those technologies. We already were doing that. So, when we built our edge computing device, what we did is we took an instance of our public cloud architecture where all the processing was happening and created a local version of it.

[00:07:29] And that's why we call it the in-store cloud. And we architected in a way where it's completely optional in that whether, I should say flexible, so your existing POS devices, your kitchen devices, your handheld devices in the store, they can continue to talk to the public cloud, or to the in-store cloud, and, and they can be smart enough to just switch back and forth.

[00:07:53] So, it gives you not only this additional layer of redundancy, but it's not a requirement to have that, it doesn't add more complexity to your operation. If anything, it brings your existing cloud architecture that you're benefiting from closer to where the computing needs to happen, so you get the speed, and you get the redundancy.

[00:08:12] So, even if you never think about voice ordering, or you never think about real-time promotions, or pricing optimization, or production optimization, or IoT sensors in the store to keep track of all of your different operations, even if you didn't do any of that, having an edge computing device can give you better speed and better redundancy for everything you're doing today.

[00:08:32] And what do you say to the restaurant operator out there? Uh, maybe even just, you know, a small mom-and-pop, one or two locations, they're like cloud this, cloud that, edge computing, I have no idea what you're talking about, like, I just wanna run my restaurant, have it work, and not have to worry about my technology.

[00:08:49] Jen Kern: What do you say to those folks?

[00:08:50] Amir Hudda: Well, it's interesting, uh, you bring that up because, you know, we focus on enterprise chains, but guess what? Those chains that have hundreds, thousands of locations, 90, 95 in some cases, 99% of those are owned and operated by franchisees, and a franchisee might have one location, two locations, five locations.

[00:09:12] Yeah. There are some that are already large, but at the end of the day, most of these are people that have built really successful small businesses, family-owned businesses. These are not people that have a, a legacy understanding of technology and evolution, they're really good at running a restaurant, which is hard to do as I mentioned,

[00:09:34] right? So, one of the things we started thinking, going back to when we, when we were experimenting with voice, is if we've learned a lot in this process, we don't think the market or the technology is ready for voice ordering, but what can we do with this technology that we've experimented with? So, we took our application that we had built called Notify, which is an alerting and dashboarding app, and we put voice technology into that. Not because we think everybody wants to have a Siri or an Alexa-like voice bot, but because it was our way of continuing to evolve the technology and test the technology boundaries and see where we needed to continue to push the envelope.

[00:10:17] So, our Notify application, it was built for the franchisees, and it has some unique capabilities, primarily because think about their life, right, from 5:00 AM in the morning, or 6:00 AM in the morning to midnight, they're running, trying to keep their business profitable and operational. They don't have the time.

[00:10:38] And in many cases, the, the skill sets to do analytics, and, and understand what levers to, to pull, to increase their revenues or to optimize their cost, but we see all that data, why can't we be their eyes and ears, and sort of tap them on the shoulder when we see something that doesn't make sense. So, that was the genesis for building Notify, which is really an alerting app.

[00:11:02] It notifies you on your mobile phone, Android, iOS, whatever. When we see data that is not making sense, it could be your labor costs are running higher than they should be for a given shift compared to yesterday, or the week before, or the month before. It could be some positive information, your revenues are running higher, whatever, you know, your inventory's not aligned with where we are seeing your, your revenues, or any of those things.

[00:11:26] We can notify you and let you know, so you can take action if need be. So, in that process, we put in a, a voice bot, and a text bot, so that if you're running around, you are in your car, driving from one place to another, you can just, you know, talk to our system and find out how sales are going, how costs are running.

[00:11:46] And it's a, it's a nice little add-on, it's a nice little touch, nobody's asked for it, nobody said, "Oh, you'd be great if I could just talk to my POS system, and find out how my sales are doing, just like I talk to Siri or Alexa for weather, or songs, or whatever, right?" So, part of being innovative is how do we take technology into areas where there may not be an immediate need for it, but there could be an immediate value for it.

[00:12:15] And that's how we always evaluate when we are building new, new technologies, new products, we're always thinking about who are we building it for, and what kind of value would it bring if we build something.

[00:12:27] Yeah, it's so true, and, I mean, I, that's one of the reasons I love working here is 'cause we are super innovative, and you are super innovative, and I think it's important to stay ahead of where the market is and where the guest is. And what you're talking about is really removing the burden of technology from restaurants and making it easier for restauranters to do their business, their restaurant, their food without technology being a hindrance.