This is a recap of a webinar that took place on the 19th of August 2021. Brought to you by meldCX, Ingram Micro and Schneider Electric.

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While the cloud has quickly become indispensable technology, challenges around latency, cost and complexity clear the way for something else to drive connected technology innovation: edge computing.

Ingram Micro, Schneider Electric and meldCX are jointly bringing forth Edge AI solutions that are secure, accurate, cost efficient, and easy-to-deploy. Discover how AI can safely and securely monitor and analyse how people work, shop, learn and play in physical and digital spaces, without the need for complex development.

Featuring:

• Luke Neofytou, Head of IoT at Ingram Micro
• Rajesh Thangaraj, Edge Solutions Director at Schneider Electric
• Thor Turrecha, EVP of Global SaaS at meldCX

Transcript:

Luke Neofytou  0:00  
Hi all thanks for joining in today. We just give everybody a few minutes as they're starting to join in. And we'll probably get started in about two minutes or so.

Luke Neofytou  0:36  
Thanks, guys for joining, give us a minute or two and then we'll get started just waiting for a few more people to jump in.

Luke Neofytou  0:51  
Just to note, as well, for any of the questions, we'll be answering them at the end of the webinar. Please include those into the Q&A section. I know there are both the chat and the Q&A. So if you could please put them in the Q&A, once we finish up with the slide deck will go through and answer any of the questions that we've got. Also, for today's session, as well, we have the first 20 partners to complete the entirety of the webinar will win an Uber voucher, and also for the most creative question throughout this law. So when a Jabra Elite headset. Alright, awesome. I think we've got quite a few attendees jumping in now. So we'll get started. Thank you very much, everybody for joining today's session.

Luke Neofytou  1:54  
Today we'll be running through artificial intelligence, and how we've been able to help support and create this into the market. Before we get started into the webinar itself. And the contents. Just wanted to kind of mention that a lot of what we're seeing today, from a partner perspective is that we're getting questions from partners saying we're seeing emerging technologies start to be leveraged in the market today. But it's quite difficult and hard for partners to start their journey without the necessary headcount resources, or experience to be able to leverage this type of technology and where do they get started. And really the goal for us today, from Ingram, Schneider Electric, meldCX and our associated vendors and partners, is to provide you with a stepping stone of how to be able to leverage artificial intelligence and the end to end solutions that we've built in market to make it easier for you to scale and really start that journey into emerging technology. And today, we'll be running through a variety of different use cases, again, around artificial intelligence and vision, artificial intelligence, specifically, just to give you an understanding of what can be completed, what has been completed, and how you'll be able to start to be able to leverage this in market today.

Luke Neofytou  3:30  
Now, just to begin with a bit of an introduction to our speakers today, myself, for those of you haven't spoken to, I lead the IoT division here for Ingram micro 11 years experience within the distribution sector, within a variety of different roles from technical to programme management and management of technology as well, and really quite quite honoured today to be able to run through some of this technology with you. Also, we have Thor, which I'll pass it over to a quick introduction from Thor side as well.

Thor Turrecha  4:15  
They go. Thank you look. Hi, everyone. Thank you for having me today. My name is Thor. I'm from meldCX. Now meldCX is short for melding customer experience. And we particularly specialised on solving problems that requires a lot of stitching from different solutions, different different software hardware, on prem on Cloud kind of components, in most businesses that are aimed to solve epic business problems. Now the way we solve it is we make it simple for our customers to use, simple for our partners like you to deploy and we make it secure to make sure that we protect your data Yeah, and customer data. And today we're going to talk more about that and how we do it. But before we deep dive into it, I want to pass you to redress for his intro.

Rajesh Thangaraj  5:10  
Thanks. Hi, everyone. My name is Rajesh Thangaraj, I'm responsible for the edge solutions enablement in the Pacific region. So I covered the business segments, including channels, commercial, enterprise, and industrial. And I would like to today start with these questions. For retailer, how bad is to lose a customer at the checkout, and because the boss machine lost the power maybe for a healthcare provider, how bad is not have the right information. When treating a patient in ICU because of the power failure, or a security breach in the ICT infrastructure? Schneider Electric's we offer human centric energy automation, and infrastructure solutions to address these challenges.

Rajesh Thangaraj  6:12  
Sorry for the delay. Luke will be sharing some of the great content and AI and applications later in the presentation. But before we get into this topic, I'd like to share some thoughts on current industry trends, and how technologies like edge computing or hybrid cloud is empowering different industries and bringing this new opportunities and how the new outcome based business models and transforming the IT industry and accelerating this digital transformation. So I thought it is really important for us to address some of the common questions like you have our most of our customer ask the such as what is happening in industry? What are we talking about? Why are we talking about AI today? Why are we talking about the hybrid by design today that we focus as a business, how to prioritise in that list, know whether we choose a CapEx Model X model.

Rajesh Thangaraj  7:04  
So I believe like we could share some insights in the next five minutes to address some of these genuine questions to start with industry trends. So you may familiar with this industry for that, or the fourth industrial revolution, which is convergence of physical, digital and biological worlds, which leverage the emerging disruptive technologies such as IoT AI, automation, Blockchain, VR, ar, ar mixed reality. In industry terms are enterprise terms, you know, generally this is called as digital transformation or digital trends. So I'd like to share the current digital trends we see around this digital transformation ecosystem.

Rajesh Thangaraj  7:44  
To start with number one is the digital business. So the new business design during this digital and the physical world, which bring this convergence. So the business moment is that transient opportunity is rich people, data, business things all work together dynamically to creating new value. So which leads the business to make informed decision making are bringing value out of the data. Number two, what we say is IoT and AI. So there is an exponential growth in the connected devices. Today we're talking about 1 million new devices will be sold every hour by industry. So I was working in IoT for more than 10 years, and in 2018, started focusing on AI. So I'm really fascinated to see how these technologies work to complement each other really brings us new value outcomes to the business. So when it comes to IoT, we see all kinds of things simple, complex, dumb, intelligent, personal sensor, customer centric, static, mobile wearables, and doing all kinds of things, measurement and recording videos in the people, data producer actuators. Of course, it all comes with a new challenges such as security, privacy, bandwidth, automation and management, and comes to AI. AI is a concept in the industry over 15 More 50 years and the potential of VR is realised today is because of the technological advancement. So 10 years ago, we have to write 1000 1000 lines of coding to define the visual models. But today we have systems and computing power to process visual images much faster. This accelerates bi deployment. The third trend we see is hyper interactivity. So all these smart things interact and predictably, but demands real time response. So for example, in in the slighlty list of three millisecond delay in processing in the premises could cause a fatal accident.

Rajesh Thangaraj  9:47  
And in the VR AR applications less than seven millisecond delay would cause motion sickness and give very unpleasant experience to the user and in autonomous driving. So very critical to 300 milliseconds off delay would cause the autonomous vehicle to stop 30 feet away. That's really fatal. So the technology we have today such as 5G, and the age and this hybrid model can deliver these outcomes. And forth, we see the emotion. The human mission gap is shrinking today. And in the enterprise, for example, there's a demand for real customer engagement, employee productivity, location experience, and then the consumer point of view that the demand for personalised experience real time interactions, and workforce, like the demand and flexible work environments, or training, which will take collaboration, but what we are doing really, the fandom really asked for it or this, you know, demand and you know, really helping too many organisations take this digital transformation much, much more seriously. In a nutshell, all this digital stress demands are realised by computing, not just computing, it demands a distributed computing, which is edge. So some of the key drivers in the digital transformation compared to the edge is latency, bandwidth, security, and system autonomy. So, as a business, the next question is,  you know, what is informed me? You know, where do I focus? You know, we understand that, as you know, like, there's all this digital stuff happening, and, you know, specifically get into this edge computing. So where do we really focus?

Rajesh Thangaraj  11:19  
So, I've really liked this graph, a copy completed graph. So that was three waves of edge opportunities in the market ID to really, you know, put through a bit of a lot of research and bring this chart. So the the, the way once a transformational, so some of those new technology emerging technologies, such as local cloud, service, Coronavirus, it container micro services, and other ones incremental such as like 4g to 5g and Wi Fi five to Wi Fi six and some of the advancement of the SDRAM. It's an opportunity to third wave, which is more of a customer solutions like micro data centres are B or E or H collocations. So, the interesting part of this graph is, for example, if a customer is deploying AI, so you need a cameras to connect cameras in a network. If the data is processed locally, you need an edge computing the edge computing needed info like power and cooling.

Rajesh Thangaraj  12:20  
And if it's a multiple site deployment, which requires backbone network like SD, wham. So you still need a cloud and data lake to still publish into our enterprise wide information. So even this is one application like missing, or IoT, which brings like a multiple in a diamond shop opportunities to you a mixture of like maybe a transformational incremental, now opportunistic waves. The question is, what are the opportunities I should prioritise? And what is the market adaptability of these technologies? It's very difficult to answer these questions. If you see the chart like you know, some of the transformational technologies were very fast and adoption likes talking about three to five years.

Rajesh Thangaraj  13:04  
And the incremental is like you have five to seven years and opportunistic like that go up to 10 years, it doesn't mean that you have to wait it has to be a mixture, what we see is a mixture of all these three ways. And so, basically, it depends on your business and what kind of applications you are delivering your customer, your partner you are delivering those to the customer premises, so you can make a decision accordingly. The third question is, so what about the business model? Whether I could offer a adopt a CapEx model or OPEX model. So the traditional business model is very product centric and channel centric, supplying customers with fixed products and services by business departments. So for example, edge computing is moving that collection and the compute of the data closer to the end user, which is enabling players in the space to provide outcome instead of product and services. So we see more and more, you know, DC of larger deals signed with the enterprises, between enterprises and the IT vendors. So the outcome oriented thinking is not new.

Rajesh Thangaraj  14:16  
So with many business models, adapting to provide choice to the customers ever the Dubai owner operator paper, for example, and adilyn industry, if you take an outline, the primary business is to safely transport people from one place to another, but they're managing to start their primary business in their secondary business and it's not the business even like it's actually they have to do that for operational purposes. So the outcome they have an industry today expecting use the jet engine as a service. So which is the aircraft propulsion system offered on power by Hubbard basis, you know, like how much time they using the jet engine, they're going to pay for it. Thank you You know, from the gentleman provided to me, it may be a retailer, for example, their primary businesses to you know, focus on their stores and you know, selling products, their primary businesses not actually and it or managing it. So they actually outsource these secondary tasks actually to the third party windows.

Rajesh Thangaraj  15:21  
Digital is the backbone that connects queries and drives this desired outcome that outcomes are in the business look to access if they decide to move away from product ownership. And the last thing like how this collaboration between Schneider Ingram micro and Melody CX helps business partners right size. So the giant li built out in a box solution means the first time to market. So this is a validated solution. I've heard this a time, you know, listing on testing, interoperability, POCs, etc. So this means lower deployment cost and improve efficiency and the flexible business model offering and also as a global player, we could actually help you in expanding your reach. I hope I have shed some light on the industry trends and the collaboration. Now I would like to invite Luke to take you through some of the exciting contents on the email and thought to do it really an exciting demo, or what do you look?

Luke Neofytou  16:21  
Thanks for just appreciate that, and appreciate the insights that you've been able to provide. So as we click through, I can give you a greater understanding of what we're providing today. I just wanted to start with, again, as you heard from Ritesh, that there's multiple types of technology that's required. And it's not just a product itself that you need when leveraging and looking at leveraging artificial intelligence, IoT are emerging types of technology, that the end customer acquires end to end solutions. And within Ingram that's really what we're focusing on. You'll see it now.

Luke Neofytou  16:59  
But as we continue through this webinar, but in future as well, our focus is really around providing solutions that are curated, repeatable, and really solving the business problems. While yes, we do components, and we do sell kits. The focus for IoT AI and emerging type of technology is really to provide you with that solution to make it easy to make it simple. So that again, if you don't have that required expertise, if you don't have the time, or you're wanting to start your journey through this, we're able to help support that. I guess just to also note on that as well that for us, our focus industries are around retail, smart building an industrial. Now what we'll speak through today, as well, we'll touch on some of these areas. But it is not limited to that there's a variety of different verticals and market segments that can leverage this type of technology. Alright, without further ado, we'll we'll start getting into a bit of more of the deeper dive of the solution itself and how this has been built out. We'll start around some of the visual analytics, and how this has been completed. Again, as we've spoken about, it's really ensuring that this is an end to end solution with not just the edge, not just the cloud, but everything in between. And just to show you these are some of the vendors and the partnerships that were built in order to be able to complete this and create this solution out to market. Again, providing everything that is required and best of breed. What I wanted to do was give you a bit of a snapshot around the journey, and what this looks like and how it's been created.

Luke Neofytou  18:45  
As you can see here, there's a number of different points on the screen, really being able to give you an understanding of how this is being done. And that very much one privacy, but to ethics has been completed, that we've looked at this in how we can ensure that this is done in the best way possible from a privacy standpoint to ensure that no identifiable data is being compromised. That even for example on the screen here where you'll see that there was a face that has been blurred out within the processing of that, that the individual has been created as a token, and that we don't just look at the face of the person. But we look at the complete behaviour of that body language, if we're looking at falls within aged care facilities, if we're looking at the way that people move, but also as you can see, again, within that tokenized person, that the objects that they are interacting with, if it might be a surface, if it might be the accessories that they have on or the apparel that they're wearing. By leveraging all of this type of data. We're able to start to build out a journey of what that person looks at what that is for them, and what it means for the end customer themselves.

Luke Neofytou  20:10  
The other point that I'd like to add on to this is that, again, when we speak about social awareness, ethics, privacy, and the policies that are enforced within this to ensure that it can be leveraged from both small and customers to large end customers, and we'll run through one scenario of a very large stream bank that's leveraging this solution today. This has been built on synthetic data. So again, not leveraging existing CCTV footage, but actually being created in a game environment. And this allows for us to get higher accuracy to be more cost effective, but also not to be able to work on not just standardised use cases, but custom complex use cases will. And then the security aspect of that as well, as you can see, again, that this has achieved PCI DSS level one as well.

Luke Neofytou  21:07  
As we started to speak through some of the market opportunities, and where we see the opportunity market for artificial intelligence, specifically around the solution that we're going to showcase to you today. While this had started in the retail aspect, and looking at standardised use cases of entry monitoring, or zone engagement, and something that you might see is quite standardised today are quite standard need. This has really branched out from there and looking into a variety of different verticals, industries. If we take healthcare, for example, with slip and fall detection, with aggressive behaviour, and then a range of different end customer requirements. Again, we've seen some of these and been asked for some of these today, how we can help to support them. And it's just to show you that again, while we speak about this today, we'll be able to kind of really give you a snapshot of some of the aspects, but they'll always be used cases that we haven't been asked for today. And that again, the solution while can be leveraged for standardised use cases, we can customise this to effectively any requirement that the end customer does have. Now with that, I will pass it over to Thor, to give you a bit of a deeper dive in the customer journey, and a bit of a demo of the solution as well.

Thor Turrecha  22:32  
Thanks, Luke. That's very insightful. Let me try and control it. Cool. So going back to the theme that we were talking earlier. meldCX has a lot of products and capabilities. And for today's session, we want to zoom into one of our products called Viana. Now Viana is about leveraging cameras as a sensor. And it is really designed to help our customers to track their physical spaces like a website. Right? That's that's very important. So from a partners perspective, our customer persona would be people that has our parks, people that has entrants, people that have shelves, people that basically sells products on the shelf, anyone that has physical stores, and chances are they have websites and mobile apps today. And what we're trying to do is to be able to create an equilibrium an equal way of measuring their physical asset physical store, to what they're doing on the website. However, before we kind of dive deep into what the solution you can can do, I want to emphasise that the cameras that we use the stream that we get from it, and look, I've already stopped talking about it. We want to basically have security in place by design.

Thor Turrecha  24:02  
So as you can see, on the left, one of our colleague Joy kind of allowed us to use her character for this session. But normally we see joy on that kind of left portrait on the slide. But the camera we have put in an AI to anonymize that. And the only thing that the camera would see even on the stream is what you see on the centre, which is a blurred face. And that's basically important because we're not using facial recognition or we don't use facial matching. To what look I mentioned earlier that we are ethical. And we provide a sustainable way to grow the technology without crossing that line from your custom customer trust of data privacy.

Thor Turrecha  24:51  
So instead of going through functional features today, we want to focus on how a journey would look like on how we can track a particular customer that goes into one of your customers physical space, and what insights they get out of it, so they can make better decisions. And we'll talk about this couple of jargons in there that might be difficult to digest straight away. But we want to zoom in straightaway on just the story level, just so we can start to share stories, maybe invite your customer next time when we have sessions like this. So let's go into the journey. Say, oops, there you go. So imagine a retail store or electronic store, right? 250 400 450 square metres. At the point of entry, you could see joy getting into the sort of entrance, the camera would identify joy as a female, young adult. At this stage, the camera would associate joy with a what we call the Re-ID or re-identification ID, which you can see that on the red right there. And here, we're just using standard camera. Right, the good thing about it is that even with just a camera, we now know, number of people going in and out.

Thor Turrecha  26:13  
So you know density, you know, occupancy, it's quite useful today, during COVID. So the point of entry, you as or your customer can start understanding the traffic that goes in top of the funnel pretty much when they go in, depending on what story you're in. Chances are they've got signage screens. Now, by the time joy would kind of put her attention towards the screen, the camera would know based on the gaze tracking. So marketers for that particular physical store would know what kind of content that's actually working for their store. And therefore, you know, a retailer like, like, for instance, for lack of a better example of JB Hi Fi for instance, they would carry 3000 skews inside the store and out of 3000 skews, you might think I only have a few screens that I could run in store campaign. What content should I run? Should it be smart home? Should it be the new iPhone should there be the new Google phone and you need some feedback, like how you do it on YouTube and Facebook on your website.

Thor Turrecha  27:18  
So that's what we're trying to offer here. Because we know how long joy is watching the content, if Joy was just interested with some animation, and you just look into it for three seconds, or Joy's actually watching the content until the end. Now the journey continues where every time there's a camera there, we could repurpose that. And basically look into other behaviours, because the camera itself can track people product behaviours, literally what a human can see, we're trying to train the AI to see that, right. So by the time Joy gets into the product, shelfing and there might be hero products that are showcased in different way, we can then start to see what joy is basically looking at what joy interacting on the product. If she basically lifted an iPhone or a Google phone, we would know. And even by the time Joy starts to do search and compare their their products that quite complex.

Thor Turrecha  28:12  
As you can see, there's a lot of smart home products now smart speakers, smart lights, you know, smart appliances. And often you as a, as a retailer, your customers struggle to figure out how do we design the planogram, in which we are putting the right product right next to each other because Gone are the days that if they go Google, they buy all Google right now people are smart, they go online, they check the feedback, they check the star ratings. And based on that, they'll start to combine products based on what others are feeding them. So they're your customers website alone is not enough. So it's important to understand which of the products on our shelves that are actually correlated to each other so that like a website, imagine an Amazon kind of environment where when you pick a product, you're actually putting the next product that is of interest of that person right next to it and preventing that customer to say, pick one product and take three steps to find another product to compare.

Thor Turrecha  29:18  
And then the experience. Here we've got Google Home, other customers especially they've got bulky products where customer needs to engage, whether it's a car or a bed furniture, they want to see how customers are engaging. So it's very, very important to understand. What we're trying to do is create a different way to leverage value from an existing camera or a new camera in the past cameras is just used for surveillance. today. We use the camera to basically extract more insights that not only is purposeful for security, but could help marketing basically win new campaigns and basically introduce new products in ways that was never done before. And then at the end, the last bit is the purchase, you've probably seen Amazon Go into us, it's almost similar to that one. But we're just not getting it to the level where you could just pick a product and walk away.

Thor Turrecha  30:12  
Now that there's a purpose to that, but I don't think all retailers can afford to do that. So what we're trying to provide is a step into that game where, yes, we're not doing the automated or automated purchase right away. But we're still learning what my customers are doing, you still understand what are the products that they're browsing, but they did not actually purchase? What can I do differently. And more importantly, that information can then be ported into your web and mobile campaigns, that then becomes a retargeting tool. So imagine that journey, the same framework, right? And we're trying to apply that into different types of personas, different types of customers that you're dealing with. So hear, you'd basically, I think, let me go back a bit. What I've shown you was retail, so this is the typical retail footprint, there's an entrance, there's a lot of shelves, there's an area where you can approach the staff, there's areas where you hang out, you discover new products, that's very useful for that occupancy density, collaboration with staff. So really, really good.

Thor Turrecha  31:18  
Next, your plant facility. Now we're not tracking people here. Now we're tracking big prime mover trucks and trying to get cement, or basically get asphalt from from this big shoot right here. But the same technology applies, this is different use case. And that just shows the agility of the proposition that we have here for you to be able to go through your list of customers and say, if you are dealing with car parks, if you are dealing with trucks, vehicles, if you're dealing with product, and you have a network already that has a camera, chances are you could be a good customer for this. And just figuring out the way then to to find the right use case for your customer that they could start really early and fast. Next service stations, as we all know, Tesla could have brought a new kind of taste in the industry where there's a projection that most cars would be electric in the next five years or 10 years.

Thor Turrecha  32:17  
So petrol stations, service stations are not trying to reinvent themselves and say, how do we basically remove the petrol station? And how do we look into creating a different space for customers so that we can adopt into this new normal. Now, for them, their research in the last 10 years has gone out the window because he can't use it now. Therefore, they need to find a way to track and observe what the customers are doing. So now they're looking at how much people are actually spending on site, what are the cars they're driving, but still, at the same time, doesn't really cross that security data security bridge where which stop trying to take faces?

Thor Turrecha  32:55  
The idea is how can we service customers that would go in charge their car for 20 minutes, and there was a retail area there that they could actually do their shopping? And how do they incentivize those things. Now changing 1000s and 1000s of service stations is a very expensive task. So they want to know, and they want to need data to make sure that they're doing it the right way. So it's very useful for transformation. But you can see, the theme is the same. They've got cars, they've got car parks, they've got products on the shelf, and those are really, really good persona to identify some anchors of who could be the best customers for this solution. Next is sorry, my bad. I don't know what happened that we slowly click back again.

Thor Turrecha  33:43  
We're almost there. So safer workplaces. Again, if you have a customer that are thinking how do we go back to workplaces after the lockdown? The same applies the same use cases applies. The framework is about who people like, you know, entrance exit spaces, utilisation collaboration, which of the furnitures basically people are using less common areas, we can track how people are interacting with kind of boardroom tables. So we want to know the density, how they're using the boardroom? Are there only two people using a boardroom good for 25. So there's a lot of these things that could be repurposed based on the user journey that I've shared in relation to joy. But then it always asked question AI, it's a big thing. We've got hundreds of use cases that we could we could we could basically pitch to our customer. Based on our experience, what we've seen is that the best approach is we want to start simple and we want to start small, but then deliver it fast.

Thor Turrecha  34:44  
A good example is people counting everyone needs people counting today. And instead of actually buying another camera, why not use an existing camera existing network and basically put the right infrastructure to support that and augment that to be able to do people counting. So When you start dealing with that it allows you to mobilise really quick, and later, I'll show you a demo of a customer that we've basically onboard really, really quick. And we hope that that story might inspire you. But before I deep dive into how simple and how we take complexity into simple, let me show you some of our complex and some of the epic problems that we're solving globally. Here is what we call the some line, some stands for scan, analyse, and manifest.

Thor Turrecha  35:27  
Now, here's a customer that they pump out 700 million chickens on tray every year, that goes to retailers, right, that goes to your supermarkets. So what we're doing is that we eliminate the human errors for checking if the actual packaging was right, the barcode was basically scanning the price is correct, the branding is correct. And whilst you look into quickly that that might be an operational savings right away, the recent behind that is far bigger. These guys and even with the with the guidance of slider, electric and Ingram, these guys are trying to reduce their carbon footprint, because the reason for that is, if out of 700 million trays of chicken goes to their customers, mostly 10% of that millions of it goes to waste. Why? Because someone has put the wrong sticker, someone has put the wrong barcode, and by the time a customer tries to scan it, it doesn't scan, normally dispatched into a staff that almost straightaway goes into the bin. So we're not just trying to provide feature, but we're trying to try and solve a bigger problem here, whilst at the same time giving the customer sort of quick benefit of getting the operational costs reduced. And basically the wastage of food pretty much. And we call that kind of farm to fork because for this customer, we were helping the farms that raised the chicken, we're helping the plant that process the chicken, and we're helping even the logistics to distribute.

Thor Turrecha  36:55  
And even until the the the product is on display, we're helping the retailers and supermarkets as well to do it in an efficient way to reduce their operating costs, but at the same time, reduce the waste and better for the community. Now, I have a sort of quick demo just to show you where we'd normally start. And I mentioned earlier that we have we have customers that we can have engaged and and we don't know them, we don't know the partner and through Ingram and through Schneider, we've managed to actually service them really, really, really quick. Let me share you my screen. Give me one sec. And and let's go through the journey really, really quick. Bear with me, I'm just trying to share. So I hope you guys can see my screen. Now, this is one of our customer Wayfare their resort in in, in New Zealand. And dev engaged us about I think a month ago, right? So we don't know them. We don't know that the partner and through Ingram they were introduced to us and idea was Thor, we've got a couple of 1000 cars goes into a resort, we wouldn't know how many of them goes up. And we want to know what type of cars that goes up there. And we want to know their flight numbers. And obviously then starts the journey.

Thor Turrecha  38:21  
Within a week we managed to pass in a proposal. And we within a week we managed to solve all the technical queries within a week we managed to solve all the commercial components. And the idea was how do we empower a partner in New Zealand right. So the empowerment really comes in place where the New Zealand partners literally just basically download the file for the instal, they download this onto the device that needs to run the software. And within the single click and within five minutes, the installation is complete. Now once the installation is complete, all the partner would then have to do is go to the site and configure the site and say it's Cardrona. It's one of their big resort. And again, this is a proof of concept to begin with. And this is just to show how fast we could actually sell it. And you could configure the floor. And obviously this is not a floor because it's a big carpark.

Thor Turrecha  39:10  
So we're looking at the car park of the entire mountain. And then from there, you could actually configure it in a way. I'm just trying to get my screen up, configure it in a way that you could edit the floor plan, you could see sensors and you could see the sensors from the screen you could drag it so it's very simple. This is our partner doing it or even our customer doing so we don't need them to have another engineer or another AI person that specialises the whole thing. So that allows us to mobilise really quickly. And then once they deploy that all they see really to monitor it is here's the device that was deployed.

Thor Turrecha  39:46  
Thank you for Ingram Micro for sponsoring, providing the device and the services basically to help them deploy it instal it, and the sensor is from Meraki and then the application here and this is how simple this is the aim For us, it's as simple as you click that and it gets installed at the edge device. So we want to offer it really, really simple. Once you've done that, it's now installed the traffic measurement, all they have to do is look into the traffic management. And as simple as that you get all the information for you. Normally, I do apologise, it normally happens like this, let me just refresh that. Normally, it takes a lot of machine learning engineers to crunch this data, data scientists to crunch this data. And again, we're just showing the data sets. Let me then show some refresh quickly. This is embarrassing. If this doesn't work. Well bear with me guys. Just a bit of refresh. So with the summary, you can see that the carpark that we're looking at 1000, parking slots, occupancy is 492. You see the duration, you see the entries, you see the exits. Now, that might sound really basic. But if I drag you here and show you the manifest, these are all the cars that are going in and out, we get the plate numbers, we get the time we get the direction. But then at the end, when we ask them what is actually your ultimate goal. Their feedback was Thor is the same thing. What what what Schneider Electrics trying to do, they want to reduce their carbon footprint. So they want to try and identify who are the customers are jumping into a bus instead of driving their petrol car up the mountain. And on top of that, now the next step is we want to identify electric cars, so that they would incentivize people that basically use electric cars up in the mountain. So that's their very first step to basically getting into that carbon footprint reduction in the next few years. And now for us, it's a simple kind of step. For us. It's a humble contribution to what they're doing. But I think that value proposition is important for us to showcase to show and that story might be helpful for you as you do a lot of conversation with your customers. And again, we don't have much time, but you can see there's a lot that we can do this product engagement. There's zone engagement, there's audience measurement, the shelf engaging post correlation even. But you can see even the the Chicken Story making sure that we track the chicken that it's handled properly from farm to fork. Those are the complex one. But what I urge you to reach out to us and basically find out a way that how can we offer your customers a way to start simple, start quick, and be able to prove value quickly. But then you still have the capabilities through us to be able to scale. I hope that's informative. Again, we're running out of time, but I just want to close it from that point. Thank you again for letting us share our story. Over to you look,

Luke Neofytou  42:40
Thanks Thor! Appreciate that and do love the demo, always Demo gods. Let me just continue to share my screen now. And again, I know we've got limited time, we just wanted to run through a few success stories, one of them being struck property grouping in New Zealand. And again, this was quite similar in terms of the use case that Thor was showing in regards to traffic measurement. But again, slightly, the use case being the same, but the outcome being slightly different. For them, it's wanting to understand the amount of people that are coming in, and parking, how long they've been there, how long they're being into the shopping centre for and capturing more related insights into that use case. Again, from there, the end customer, quite interested to say this is just a proof of concept. We want to start here. But the ultimate goal is then to be able to look to leverage this to be able to do people counting to be able to mesh multiple layers of the artificial intelligence to get a full end to end picture of understanding the end customers, how long they're shopping for where they're shopping, and the likes as well. And again, this is something that's live currently running in New Zealand at the moment. Now, again, won't go through this one in much detail. But again, as you as you saw that thought was running through this is the live proof of concept. But we're also running for Cardrona in New Zealand and a ski resort at that.

Luke Neofytou  44:17
I guess just to touch on, as I previously spoke about as well and around the privacy and the ethics as well. One of Australia's top four leading banks is currently leveraging this solution as well. And from their point of view was to solve two major problems. One being in the branches and being able to firstly understand as on our website, they understand based on who we're clicking through how long they're clicking through on the websites, but in a physical store, they don't have that type of information. And so again, with the solution that we've put forward here, one portion was for them to understand the people that were coming in the age, the gender, the sentiment and the mood of the people coming into the branches. As an example, with the current COVID situation for them. It was the thought process was the majority of people coming into our branches were elderly over 85 Because they weren't leveraging technology, when in fact it was the males between 20 to 35 predominantly doing home loan applications because they had to do the physically instal. And then there's second portion for them around the content itself being digital signage on the screen. And the marketing team spending quite a considerable amount having over 70 campaigns running at one single point in time. For them it was to better understand, is this content effective? Are they leveraging it? Are they getting the best out of it? How long are they watching it for, and ultimately being able to gain a better return on investment by making changes to the campaigns that they were running. Again, just kind of wrapping up in regards to the solution a bit of the architecture itself. Again, not going to spend a huge amount of time on this, but this is just for anybody tech, techie. The solution architecture. Again, we've included the Meraki cameras here Cisco HyperFlex, as well. But this solution is agnostic. We can leverage access cameras, for example, within the proof of concepts, we do leverage Intel marks as well. On the back end, this is working with Microsoft, from Azure standpoint, and Intel with open vino as well, it's really just to show you that that end to end architecture is there, that includes everything that is required for the end customer. Again, within the solution bundles, just to be able to showcase to you that we've created these pre packaged ready to go, making an easy and simple for yourselves as the reseller, but also for the end customer to make sure this is scalable. And to be able to uptake this quite quickly. Anything from a small proof of concept with one single camera, and an Intel NOC up to 100 plus cameras, we've got these predefined to based on what the end customer wants to achieve. And it's something that we can run through with yourselves. And also jump on calls with the end customer as well.

Luke Neofytou  47:19
Just in regards to integrations, again, you've seen some of this in terms of the API, but just also kind of hone it back that the solution is API centric, that there is an ability to be even more scalable and more flexible. We've had any customers come in with unique scenarios about wanting to be able to integrate with sensors, IoT sensors to be able to open doors to look at access control, to look at a range of different scenarios, even as simple as any customer saying, we just want to email notifications, well, then it might end up going to Well, hey, we've already got a ServiceNow integration in our back end, or we've got licences on that. Is there a way for us to be able to integrate with ServiceNow? Yes, they can, or something. As an example, with Twilio, if they've got cleaners on site, for a shopping centre as an example, and a spill happens, and they need to be able to send somebody out urgently, they can leverage the Twilio integrations to be able to send an alert a text message, an SMS directly to that cleaner to be able to go out there. And again, as Rajesh was saying, minimise that delay, ensuring that the things get done at the quickest time possible, depending on what that solution or what that use case is for the end customer. Just to wrap up, so we've got a little bit of time for questions. Really appreciate everybody's time today. Again, know that we can see that there are a few questions on here. So we'll take them after running through this. But again, we just wanted to wrap up and say that if there are opportunities, if there's something that you're interested about do you see in customer that you currently work with that might have a proof of concept in mind? Is that something that they want to explore, feel free to get in touch with us, we're here to help support you with this type of technology, we understand. It's not the simplest thing to begin and start that journey into artificial intelligence into emerging technology. We've made this as simple and as easy as possible for yourselves as resellers. And for us to be able to support you on this journey to be able to take it forward, and ultimately for us all to succeed. So with that, thank you very much from the slide deck point of view, and we'll jump into the questions

Luke Neofytou  49:51
All right, awesome. Give me one second, just bringing up some of the questions here now. I can see Billy as asked, as CCTV cameras are an important part of the solution. Are there any key feature products you'd like to see from CCTV manufacturers to aid the AI market? Sorry, happy to take that one quickly.

Thor Turrecha  50:14
Yeah, no, absolutely. Um, so at the moment, the CCTV market, obviously there's a bigger transition that's happening from being just focused on security now to kind of a wider approach. So there's security kind of mindset and Now we're getting into the wider part of the business. And there's a different business analytics trying to get into the CCTV world. I think from, you know, if I could ask manufacturer something, it's about collaboration allowing us to, you know, update and augment the AI that they've got on on the camera, I think that would have a significant impact moving forward.

Luke Neofytou  50:51
Awesome, thanks, Thor. Rajesh, I see that there is a question there as well, if if you're happy to kind of read through that.

Rajesh Thangaraj  51:00
Yeah, absolutely. No, I think there's a two things. So we're here. You know, my point actually earlier, is, we have the computing power today, you know, like, when it comes to processing, actually, the, the massive amount of data on premises. So we call it like the weekend contract. If you talk about any large players, cloud players, like they are building the building their cloud models on the edge today. And so we have this company called leavin, like, whatever, actually, the data is created, but it is any technology today. But of course, like when it comes to the business model, the other thing is a business model. Because we need to really accelerate, like a lot of the enterprises today want to accelerate the digital transformation. So building, traditionally, if you see any enterprises building their application, the data centre, it takes time. And I personally experienced that. And I've worked in enterprises for a long time. And it's really challenging for any solution or another product owners to build anything within the data centre. So how we can quickly deploy an application. So that's why I think the cloud will really accelerate this process. But of course, luckily, you can now be cloud based, as I understand that, so they have not just having all the models on the cloud, because again, there's there's a lot of demand for the latency and security and, and the bandwidth issues. It's especially Australia, like, expensive. So that's what they are now bringing working with the IT vendors to bring that model towards the edge. So we have the, you know, the cloud computing part today. And also there's like collaboration and the service models, it's more of how we can accelerate this process, you know, having this a CapEx model, which takes longer time, and the service model outcome based models, which takes in a shorter time to quickly get into the market. So that's why I see that as an advantage. Of course, like no, we work with like not we treat actually customers, we respect the customer decision, whatever it is, like in the follow up, it's more. But what we see in industry, the large enterprise players are moving to assist OPEX model, or the outcome based model. So this model because they wanted to really accelerate this digital transformation process within the organisation.

Luke Neofytou  53:27
Awesome, thanks for Rajesh, I guess I'll just quickly add on to that as well. In regards to the as a service model, again, we do get quite a few questions in regards to the solution and how this can be provided as CapEx as well, just to know, obviously, within remarquer, our financial solutions and financial services can provide not just any of the technology that we provide, but even external technology of third parties to bring that all together, and then provide that either to yourselves as a reseller, or even down to the end customer to be provided as a service as well. So it's something that we have been asked questions around, and great to hear that has come up as well, but definitely something that we can provide.

Rajesh Thangaraj  54:10
Yeah, let's quickly jump on an example. So in a while I was working for a largest retailer. So we have deployed an AI solution in order to flip trip and fall to identify any particles on the flow to the user sensitive cameras. So while we initially started this process, with deploying on the cloud, and the processing, and everything takes like, almost like 160 seconds, just for two or three cameras, to take the data to the cloud and processing, it gets that information back doesn't meet the business timeline, you know, at so that's what we have introduced age. So which actually gives them instant, you know, outcome, you know, to infant processing in real time, nearly real time and able to deliver that messaging to the manager so that to go and claim that in a particular area. So water spill, or like maybe something is broken. So that's what I find like, but you know, if we have the same computing power, whatever actually we can do on the cloud, we realised that we can do the same like computing. Like we can have the same stories same processor actually in the in the local premises as well. And of course, like not end up today. Some cloud versus age computing, you need to have the hybrid model. Because age is going to be on premises and an enterprise become multiple sides, you still need actually the data to be transported to the cloud, and having this enterprise view for the decision makers, and still have to store the data on the data lakes, for a future modelling. So I think we see that today, the market is ready, the technology is ready. And there's real demand.

Luke Neofytou  55:55
Awesome, thanks Rajesh. So there's one question just in regards, and I will just read that out. Does the analytics shift to the cloud effect the middle tech solution? Or can the be honest software operate in a cloud VN container?

Thor Turrecha  56:10
Now, thanks. Thanks for that question, Alexander. I think that that's basically the kind of segment and the type of customers we're helping we see that there's a transition happening right now. So Viana supports edge supports hybrid and supports cloud only. And by default, Viana is operating within a juror. So we could basically run it on traditional approach. It could be VMware or containerized. But there are two what Regis is sharing their use cases that requires instant reaction, like slip and fall step detection. And if you need instant reaction, and you need real time, you need to have the edge computer, you need to have the edge compute to basically get the reaction that you need. However, if you just want to do people counting, and you'd watch the you watch that weekly, or daily, or after four hours, then you could process that in the cloud. But then again, it just depends on what is the current setup, if you don't have much internet connection, like Wayfare, for instance, that that kind of area that you've seen in the resort, they're running on 4G, and on 4G, you pay $32 per gig. And if you stream everything on to the cloud, it becomes expensive. So what we've been doing is, we take the stream of video, we convert that into insights, and then just techs are basically funnelled to the web. So to make it cost effective for them to operate something like that. But yeah, use cases then dictates what solution that we could put forward. And from our solution side of things, we make it as agile as possible, so we can support the different topologies that they want to do.

Luke Neofytou  57:45
Awesome things, Thor. Renata, I know you had a question in regards to Ingram micro distribution. So I'll put your note about that one as well. Not a problem at all. Look, I think that's all that we've got time for today, as well really appreciate everybody jumping on. Again, feel free to reach out at any point in time. If you've got any questions if you want any further information, or you'd like to run through any customer use case or potential that that you're looking for. Again, we're all here to help and support within the solution. And really appreciate everybody's time. We'll also flick through a separate email note on who has won the headset as well and get back to everybody in regards to the vouchers. Thank you very much for your time. Thanks Thor, thanks Rajesh.

Thor Turrecha  58:36
Thank you everyone.

Rajesh Thangaraj  58:37
Thanks Luke, thanks Thor, thanks everyone.