Mach7 Technologies Limited (M7T) Earnings Call Transcript & Summary

I think we'll probably still have some more people coming in. But it's a little after 8:30 now, so we'll probably get started for everyone. So first, welcome, everybody, in person. It's nice to see all of you in person. We have a bunch of folks that are online too on Zoom. So welcome to all of you that are remote. So first of all, this is the first time for us that we've got a good contingency of our management team actually in Australia. So I have David Madaffri with me, our COO; and Dyan O'Herne, our CFO, with me here for the week. So we're a great way to kick off the week. I will ask that as we go through the presentation, if you all wouldn't mind holding questions until the end, and then we'll go through any questions that we have in person first, and then we'll get to all of those of you that are online and your questions online at the end. So we'll get through this. Here we go. First, I know most of you have heard the story before, so I won't spend a whole lot of time on this. But one of the things that is unique about Mach7 is our tagline of independence through innovation. What makes Mach7 unique is the fact that we have separate modules with that we sell that can service customers across the spectrum of enterprise imaging. What we don't do is make our customers buy our entire ecosystem in order to take advantage of our technology. So what that means is, as an enterprise-first company, we really like to have our customers focused on consolidating imaging, bringing in images from across the whole hospital system, not just radiology and then being able to serve those images back out to the whole hospital community in a meaningful way. So the 2 major components of the system that will allow us to do that are our VNA and our Enterprise Viewer. There are other components in here as well, and there's other use cases that we'll discuss, which are really important to our customers. But the idea that you don't need to buy our whole ecosystem to find an advantage and to find an ROI with our software is really important. It's sort of central to who we are. So kind of a new slide for those of you who've seen some of our decks before, I just put this together for everyone just to get an idea of Mach7. I sort of -- I usually ignore the '07 to '10 range, I don't talk a lot about that. But in 2016, Mach7 did became a public entity on the ASX through a reverse merger with a company called 3D Medical. That's how we ended up here in Australia. And then in 2018, we signed our most significant contract at that time the Hospital Authority of Hong Kong, which was really an anchor for us with our APAC region, a very central region for us and where our founder is located in Singapore. And then, of course, one of the most important things for us is our acquisition of Client Outlook. That took place in June of '20, I think it finalized in July of '20. That was a transformational acquisition for us. And we had this wonderful back end from an enterprise imaging perspective, but we partnered with client outlook to provide the front end. And the front end is really what radiologists fall in love with, right? The radiologists and other users, they like front ends. So when we acquired Client Outlook, that really made a more complete solution for us and allowed us to quadruple the size of our funnel and our opportunities. So it's transformational for us, for sure. As we started to do, we got more traction in the market. We got recognized by class. We've got better class rankings. We sort of started to get a bit of this network effect within the U.S., especially within the U.S. And that sort of has led us to where we are today, most recently with our NTP contract in the VA, with a lot of contracts in between. So a couple of things that I'll mention here just because I think they're important topics for you to understand sort of our take on things. So these are topics that come up constantly in all of our conversations, right? The first one around AI. Look, for us, it means a lot of things to a lot of people, and we hear a lot about it. But we're generally hearing a lot of more news around bigger data models, natural language models, generative AI, things like that, right? Our take on this is that for us, we know medical imaging is going to be a big consumer of AI products into the future in a lot of different ways, both clinically, operationally and also from the perspective of efficiencies with doctors, it could be anything from voice recognition and how they're transcribing their findings to how they're coming up with their initial clinical findings. We also know, though, that there are some things that are kind of in the way of AI right now in medical imaging. M&A activity is taking place. It seems like weekly, it's probably monthly. But it seems like it's a big part of that market where companies that are coming up with an algorithm today either become obsolete in 6 months or they'd be earlier acquired. So there's a lot of movement in the market right now. And on top of that, at least in the U.S., there's complications around reimbursement. The good news is reimbursement is starting, but it's slow. It's going to take some time, but it's starting. So the more reimbursement hits the team, U.S. system the more likely it will be that facilities will look at AI models and how they can use them clinically. The other piece that I mentioned here is a lack of evidence. And what I really mean by that is first, evidence that it is more efficient for the radiologists and that they can move through more studies, more images with higher quality at a faster rate. They need evidence if that's the case before they invest money is one part. The second part is evidence that they're clinically appropriate. And there's a lot still -- and I was just recently at a class symposium where a speaker was talking about any risks around AI models clinically. And part of that was who's going to be the first hospital in the U.S. that has sued because an inappropriate clinical finding was found. And how is that case going to be prepared and presented. And what kind of an impact will that have on the market? We don't know yet, right? I think the first case is being heard in California actually this month around this. So we'll see how this evolves. But regardless, we know that it's something that that's going to be into the future of medical imaging anyways. So from a Mach7 perspective, again, we're not investing in bringing in the imaging scientists to develop new algorithms to provide clinical features and functions for radiologists or for other users. But what we're concentrating on is making sure all this data that's being centrally located in our archive is available for our customers to be able to use themselves for AI initiatives that they themselves have. Many of them are initiatives that they are bringing in internal. They have their own scientists that are developing their own algorithms. They want to use their own data to develop these algorithms. Other parts of it is they need data that they can easily segregate so that they can do their own testing against commercial models, right? So in those ways, we can really help. We give some facts here just around the number of type of information that we have, a number of images that we have in our solution, just to give some context for how that data can be used internally by our customers. I would emphasize, though, that this isn't data that we intend to monetize, right? This isn't data that we own that we intend to monetize. This is data that we help our customers monetize, right? So make sure we're really clear on that. The other piece on this is we -- I mentioned Client Outlook being a transformational acquisition. I just wanted to, and many of you have seen this slide before, but I just wanted to just spend a second just to really emphasize how important that really was. It showed us that as a company, there is growth opportunity through acquisition. We believe that today, just as we believe that when we acquired Client Outlook. However, given the current market conditions, we are a little cautious, we would want to find an acquisition opportunity that we thought could create the same sort of transformational business model the Client Outlook did for Mach7 when we made that acquisition. So if we do see that right opportunity, we certainly wouldn't shy from it. But at the same time, it would have to be the perfect opportunity, just like I think Client Outlook was the perfect opportunity for us. And we always keep our eyes and ears open for those opportunities. This sort of gives you a little bit of information in that bottom slide. In FY '23, the way to interpret that is we had 17 deals that were for Viewer only. We had 7 deals that were for VNA only. We had 7 deals where the customer bought both products from us at the same time, and we had 6 deals of either a Viewer client buying the VNA or VNA client buying the Viewer from us, right, just to show that we do have some cross-selling opportunities in there, right? Clearly, for us, the value is when people buy both products. That's the ideal solution. Dave will talk about that when he talks about how we sell our product and the value propositions. So just a snapshot from FY '23. As a reminder, our FY '23 results came out, right? We did have some conversations about this already. But we have not released our Q1 FY '24 numbers yet. Those will be released at the end of October. So just know that they're coming, but haven't been released yet. I won't spend a lot of time on this. I think everybody is probably quite familiar with this. But I think 1 of the important parts here really has to do with recurring revenue. The CARR and ARR number, I think, is really important. I think we're seeing our business model where we've had a large percentage of deals that are capital contracts. I think we're seeing some transition there where 2 years ago, we saw -- we were around 50-50. The last couple of years, we've been around 60-40, 60% being subscription, 40% being capital. And I can see as the future progresses that, that number is going to continue to shift over time, more and more towards subscription, right? That has an impact on our business. On the 1 side, it's really good quality recurring revenue, right? On the other side, if it happens too quickly, then we have a situation where our revenue from the capital deals isn't coming in, in the current year, right? So the idea is to really try to curate that transition as effectively as we can and not jump too fast to a full subscription model. I get asked all of the time, why not just say, just subscription only. Well, it's easier said than done. It would be great if we were just starting, but we're several years into this business model. So it's a little bit more complicated than that. So just as an ending note, before we hand this over to Dave. Fragmented opportunities. We have a lot of our long-standing vendors, the IBM's, GE's, Philips of the worlds that are losing market share right now. Vendors are moving further and further away from those single ecosystem solutions. We know they're losing market share. It's leaving market share open to all the rest of us that are trying to gain some market share. We're also seeing volume transition from the -- what we call the acute care space, the hospital space to the ambulatory space, the outpatient space, but not just in the U.S., but all over the globe, we're seeing that happen. That leaves -- there's several vendors out there that can do a great job in the acute care space, they can do a great job in the ambulatory space, but they can't do a great job across the spectrum. So that's an advantage of Mach7, where we can effectively use our software across the breadth of these opportunities. And from a complexity perspective, we're oftentimes asked like what works out better for us, the acute care space or the ambulatory space, where do we concentrate. Look, we concentrate on complex. The more complex the workflow is, the more return on investment you get out of a solution like ours. You're not going to find a single hospital with ordinary workflow, who is doing 100,000 procedures a year, there's a lot of solutions for that type of customer. But to find an ambulatory site with 50 imaging centers and leading services for teleradiology that work out of 10 hospitals or hospital systems that have multiple hospitals, multiple JVs with other outpatients, color radiology services, things like that. All the complexity there, that's where we can thrive. So it's not a matter of what market we thrive in, it's a matter of the more complex the better for us. Of course, #1 have seen our renewal opportunities. We have a lot of opportunity in renewals in FY '24. Look, we hope that always is the case, right? We want our existing customers to continue to renew with us. We generally sign 5-year term agreements. We want them to be 10, 15, 20-year agreements, but they're 5 years at a time. We have a low attrition rate with our customers. So we should get these renewals on a pretty regular basis, right? And I'll let Dave talk a bit about sales and you'll understand more from him, but we expect good things out of sales this year. We expect our sales orders to grow by 20%. And we've stated revenue growth. And then, of course, lower OpEx growth than that we see on the revenue side. Essentially, what I'm trying to say there is, if we start to see a transition in revenue, closer and closer to that subscription model, right? Our OpEx has some variation to it, right? So we will work really hard to maintain margin, right? That's one of the focus of ours is to try to maintain margin. We want to grow the company though. So the focus is on sales orders, the focus is on revenue, the focus is on OpEx and all things are in relation to one another. So I think with that, I am going to hand it over to Dave, who's going to talk to you all about sort of the value propositions and sales of our products.

Remind me a lot of Southern California. I lived there for a number of years, beautiful water. So yesterday at lunch, when I was having lunch with Mike, he told me he's buying boots before he leaves here. I didn't know boots were a thing in Australia. But being from Texas, I think that makes us have something in common. We have a lot of boots in Texas, as you know, we also have big hats. I think the other thing that we have in common is our accents. So as you'll notice, I have a strong southern drawl. Later, you'll be meeting [ Kristi ] and Jeff. They'll be doing a demo of the product. They're also from Texas. So we're going to try to enunciate all of our words slowly for you guys, so it makes sense. And I guess the only thing we don't have in common is football. So hopefully, I will learn more about your football while I'm here this week. How many Australian football fans? A lot. Any American football fans? Just one. Two, all right. Awesome. So I was excited because this morning, I woke up at 2:30. And I'm going through the channels in the hotel room. And I caught the Buffalo Bills, the Jacksonville Jaguars live in Australia, 2:30 in the morning being broadcast from Wilmington in England. So obviously, I was really excited, nearly called in sick today. But Mike has told you should probably come in. So with that, I'm really here to talk about my second love, which is medical imaging. And so fortunately, for Mach7 medical imaging is booming right now. So all of you are aware, traditionally medical imaging typically is in radiology and cardiology. All of you probably had a CAT scan or an MRI or you know people who had ultrasound. But with the advent of this device that most of you are handing in your pockets right now, this is the newest medical image capture device. So if you're out, for instance, playing rugby and you gas your knee cap when you go into the ED more than likely the ED physician is going to take a snapshot of your wound for historical purposes. If you go to dermatology with the skin lesion, again dermatologists now using iPhones to take pictures of those skin lesions and store them. We now have tiny cameras everywhere in the hospital, taking videos. So we have tiny cameras to go in through your throat to take pictures of polyps in your stomach, we have pictures of surgery when you're having surgeries done. And then all of you are familiar with going into the dentists, getting dental x-rays. Pathologists are now starting to digitize their slides. And so what Mach7 does for its customers is we take all of those images regardless of where they're taking in the enterprise, and we consolidate them into a single image repository, which is on your right. So we also call it a vendor neutral archive. And so what -- by doing that for our customers, we help them eliminate the silos of data throughout their enterprise. And why customers like the Mach7 solution is we are a software-only provider, which allows our customers to use their own infrastructure and more importantly, their own security to protect their data and also their own data storage mechanism. And with the Mach7 solution, our customers can store it on-prem in their own data center. They can store those studies in the cloud with Azure, or AWS or Google. And some of our customers are even doing hybrid, meaning they would store a copy on-prem and then they might store their disaster recovery copy offline and Azure or AWS for cost-saving purposes. So once we have all of those images consolidated at the enterprise level, the next thing we need to do is provide access to clinicians, the access that we provide through our Zero Footprint Viewer, has to be scalable because we're sharing those images with thousands of users across the enterprise. And remember, the enterprise could encompass not only an individual facility or multiple facilities, physicians' offices, mobility devices. So all that data has to be available to those clinicians anywhere, any time on demand. And by the way, it has to be fast, because if you think through it, if you have a vendor neutral archive here in Sydney and a physician in Melbourne wants to pull up a study and look at it, we have to ensure that we deliver that image to Melbourne very quickly, not only for that user, but potentially the thousands of users calling images from different locations at the same time. So that is why people love the Mach7 solution. Centralized repository, very fast, easy to use, intuitive viewer that can deliver images anytime on demand. Within the viewing system, we also have to have specialized tools for different users. So the tools that your dentist uses when he looks at your dental exam is very different than the tool sets that a radiologist might use. A radiologist might need to do 3D imaging. Your radiologists also might need to perform mammography studies, which is very complicated and requires very sophisticated tools to interpret them. So with the Mach7 solution, we have a very customized approach delivering different tools to different users throughout the entire enterprise. This also becomes very important as we move into the academic institutions because not only do we want to deliver those images quickly and efficiently, but we also want to segment them for certain types of users. Meaning if I'm doing a research study that requires some level of anonymization, I want to make sure that I can put those images in a segment in the Vendor Neutral Archive that's only accessible to certain users, keeping that data confidential from other types of users. And then finally, all of this information has to be not only available through your devices. But most physicians also want to see them available through their electronic medical record. So that when I'm in looking at other types of data such as your EKG or some of those other clinical areas of your history, I can launch and view all those images directly from Mach7. So the last box Workflow Optimization is what I really consider to be the secret sauce and what makes us different. If you talk to many of my competitors in the market, most of them will tell you they have a Vendor Neutral Archive and a Zero Footprint Viewer. I would argue that there isn't as good, of course. But what really makes us different and what my competitors don't have is workflow orchestration. So I'll give you a few examples. So as I talked about earlier, we -- let's say that we have a system here in Sydney, that has a set of modalities, let's say, CT and MRI. And they acquired another hospital in Melbourne that has different CTs and MRIs, but we're all sending all that data to one common location. Within the Mach7 solution, I normalize all of that data as it comes in to make sure that I can then deliver that data to the right clinician at the right time in the workflow. It's a very important for making all of this massive amounts of data work together. Think about like your own computer, how you have your own file structure and it's easy to find things. We're doing that at massive scales with massive amounts of data, and that is the secret sauce of the Mach7 solution. Also included in that is DICOM routing, okay? So DICOM routing is important. I'll talk about something that's buzzy right now in the market. A lot of you people are talking about AI, Adaptive Intelligence. So the real important thing about Adaptive Intelligence is the workflow to make it work. And if you can imagine -- if you're a clinical system that's using 13 or 14 different algorithms from a vendor, we have to make sure when those images come in to our solution, whether we send the whole study or certain images from that study to the right algorithm at the right time to make sure the algorithm can do its job and send back the results to the clinician prior to the clinician providing the diagnostic interpretation. Without that key elements of workflow, AI would be doom to fill. So we think as the AI market continues to grow, Mach7 is going to have a great foothold in helping our customers develop workflows, complex workflows around AI. And then lastly for workflow is life cycle management. Unfortunately, most clinicians in the states, I'm not sure here in Australia, but clinicians tend to keep all studies forever, which is very costly from a customer perspective because you're storing studies that are 8, 10, 12 years old. And so although customers haven't adopted through life cycle management, which we give them the ability to do, which is essentially the leading studies, instead, they can use our life cycle management tools to get created maybe instead of deleting that studies, I can take studies older than, say, 7 years old, I can compress them and send them to a cheaper storage alternative. So it allows me to decrease my cost from a storage capacity while maintaining the record for the life of the patient. Does that make sense? All right. Moving on. This is -- I always like to highlight this when talking about our Mach7 solutions because it is unique in the industry. So the tools you see here, if you think traditionally back about imaging, remember, it started in radiology. All these tools here, radiology have been using these tools for years. Because it was important to make the radiology departments work. what Mach7 has done, we've taken all those tools that are traditionally only available in radiology, and we make them available to all the users across the enterprise. So if you go back to my examples earlier, if I'm that doctor doing wound care images or I'm a dermatologist or I'm a pathologist, I need to have on the left, their quality control, I need to be able to make sure that the image I just captured in my iPhone is correct. It's got the correct labels. It's got the correct clinical information so that when I send it back to the vendor neutral archive, I know that is accurate. Traditionally, until Mach7 made those tools available in the enterprise, those customers didn't have the capabilities of doing that. In the middle, worklist. Typically, radiologists have been using worklists for years. We now have taken that idea and spread it to all the users that are in the enterprise. So a couple of use cases for that are if I'm now a clinician and I'm on the floor making rounds, and I'm seeing all my patients, I now have a custom worklist of every patient I'm going to see that day. And more importantly, I can launch and see that patient's clinical imaging records from 1 location and see all those images regardless of where they were taken, at the bedside, while I'm talking to my patient. I also like to use worklist for tumor boards. So if we're going in, we're collaborating with a group of clinicians about tumors, I can't have all of those on my worklist. I can pull out my iPad. I can launch them. I can display them on the screen, and we can collaborate with all the physicians regarding those cases live and on demand. And then we talk a lot about the images, the importance of them being fast, being able to collaborate with other clinicians wherever I'm at. We have tons of stories with our physicians who are out of dinners. He may be the only physicians capable of reading a particular type of study, he can pull out his iPad, at the dinner table, get a call from another clinician in-house and collaborate real-time using the Mach7 solution on his tablet, which is phenomenal, I think. All right. I'd like to use this slide to talk about scalability and flexibility of the solution. So this is a few of our customers. We have 165. So let's start with scalability. If you look at this list, which is pretty extensive, we're able to accommodate very small customers like Wake Radiology. Wake Radiology is a small customer in the U.S. that is using us for a very small use case up to the Hospital Authority of Hong Kong, which is a country. So we also have small community hospitals. We have large IDNs. We have radiology reading rooms. And so our solutions at Mach7 are customizable and scalable based on the size of the organization. They're also very flexible. So if you look at, for instance, Penn Medicine, Penn Medicine uses Mach7 for the Vendor Neutral Archive, so they're storing all their studies on us. They're using us for Enterprise Viewer. So all the clinicians in the enterprise look at images on the Mach7 viewer, but in the department, they use visage for radiology. So conversely, if you go to a place like Stanford Health care, Stanford Health Care is using sector in radiology. They're using sector for archiving, but they're using Mach7 as their enterprise viewer for the speed and performance that we can deliver. And then finally, we have customers like Adventist Health, there in the top left, which is a large IDN in the West Coast that uses for full stack. So not only are they using us for the enterprise archive, they're using us for enterprise viewing. They're also using us for departmental viewing, which means all their radiologists are using this every day to read studies. So it's a very scalable flexible solution, which makes us, I think, very opportunistic around opportunities because we can provide our customers exactly what they're looking for. The next slide, really is about our customers and partners. So in addition to being scalable and flexible for our customers, we're also scalable and flexible with our partners. So here, you see a variety of partners, some big names, some I'm sure you recognize, these partners use us in a variety of ways. So if we start with someone like, is Nuance on here? Nuance, there in the center. Nuance has a power share solution that they market. So that allows all of the Nuance customers to share images across their enterprise within their solution anytime a customer pulls up an image from their power share solution, they're actually utilizing the eUnity Viewer. So they've embedded their software around the Mach7 solution to provide the speed and performance for their customers that they need. We have other customers that are resellers that sell our whole stack like Nuvodia. And then we have other customers have wrapped the software around the Mach7 solution to develop their own solutions. So LucidHealth is a great example. It's a group of radiologists out of the Midwest, take Indianapolis to be specific. And they have built a custom worklist. So they go out and sell this custom worklist to radiology reading groups that provide them a lot of efficiency. And when a customer of theirs launches their worklist, they are actually launching the eUnity Viewer. So there's a lot of use cases, as you can see here, I'm not going to go into them all. But we've been very successful and this market continues to grow for us. I probably get 2 to 3 calls a week from vendors that want to work with Mach7 in a variety of ways. So really excited about that and the opportunities in front of us in that arena. This shows you a little bit of that flexibility. So in the middle is the eUnity Viewer from Mach7. And if you go straight up, that's our own worklist. So when I talked about Adventus Health earlier, Adventus Health is using our worklist, launching our viewer in reading studies. However, if a physician likes the experience of the eUnity viewer, but he has a different preference for workflow using different worklists. What you're seeing around the outside are all the vendors that we can be launched from. So with Clario, which is an Intelerad product, which is quite popular here and Australia, the Clario worklist actually is live in several locations where they launch the eUnity viewer. Same with RadAssist, which is Lucid. Docpanel has built their worklist, which is a reading group out of New York that's really growing rapidly. And then on the bottom left, which we're really excited about, Nuance has built their own worklist. I'm sure all of you saw our announcement regarding the National Teleradiology project where we're going to be providing teleradiology for the Federal Government, which is a big deal for us in the States. We did that in collaboration with Nuance and Microsoft. So in that particular instance, nuance will be driving the workflow, eUnity will be the viewer and archive and our archive will be hosted in Microsoft Azure. So that's a really exciting project. It's kicked off, going well. We are scheduled to go live with that sometime in the June time frame of next year. All right. KLAS, I know that you guys have might brief me, that's something I think you talk about a lot. We continue to do well in KLAS. We absolutely always have room for improvement, and it's difficult. This is a very time-consuming thing for a vendor to do well in KLAS. The key to KLAS is making sure that our customers fill out the surveys. So we've done really well in eUnity for the last several years. We're always on the top. We expect to be in the top 3 again. This year that will be announced roughly right after RSNA, which is the January time frame. In the Vendor Neutral Archive, we have some work to do. And the reason we have work to do in the Vendor Neutral Archive space is because we have a lot of legacy customers. Customers have been using us for a long time. So again, we, as the vendor, have to make sure that those customers take the time to send in their surveys to make sure we get the results to move us up on the KLAS surveys. So we're going to continue working on the Vendor Neutral Archive this year. We're rolling out a new version of the product called V12, that's in production in the customer site. So that's on track. But we're very excited about V12 and the impact that will have on our KLAS rankings. So in with this slide, a few wins that you're probably aware of: St. Paul's with a net new win in Hong Kong. It was part of the Theresa Cathedral System. There, Nuvodia is a customer I've known since my days at Philips. So I dealt with Nuvodia for a long time when I was at Philips Healthcare. When I joined Mach7, they were happy, and I was happy they decided to move over and join us here at Mach7. They're a reseller -- an interesting reseller group, in the sense that, they're actually a radiology group, a group of professional radiologists who started their own IT managed service business, and they've chosen to use the Mach7 solutions to sell to all of their customers. They presently have about 150 customers on Philips. And over time, they'll start moving all those customers to the Mach7 solution. And then last, late last year, a huge win for us. We signed Akumin, which is a large imaging center chain in 48 states, primarily in the South spreading out towards South and West, I guess. But a very large contract for us, lots of users. They had the same amount of radiologists reading for them. I just saw the number. I want to say it was like 328 radiologists in that group reading. So we just installed now. I think the VNA came live, we're starting migration of studies, and we expect all of those imaging centers to be live sometime by late next year. It's a great exciting project for us. And then finally, this last quarter, I guess these are public now. There's the NTP, huge opportunity. So not only -- the reason NTP was so exciting for us is the national teleradiology project is a reading platform on top of all the VA hospitals within the United States. So there's hundreds of VA hospitals in the United States. And so what happens when they get busy or they need specialty readings, they forge studies from those hospitals to the national teleradiology project. That's going to be read on the solution I detailed earlier. That's Phase 1. But the more exciting part for us is they also said that in later phases when those individual VA hospitals come up for their renewal with their departmental PAC Solutions, they have the option instead of going out to bid, which is very painful with the U.S. government, they would have the option to just move that study volume to the NTP in what they're calling a NextGen Fed PACS. So we'll see where that goes. Still early. But we're excited about that project. And then 2 other wins. We signed DIA, which was an existing customer to -- we actually move them into a subscription contract and they're expanding the use of eUnity. Previously, they'd only used us for mammography. Now they'll be using us for all of their studies. And then lastly, I think in a display of their satisfaction, the Hospital Authority of Hong Kong renewed their service agreement with us, which also included professional services. So I think that's it. Hopefully, I didn't take too long, and I spoke slowly enough and annunciated all my words for you guys. I'd next, I think, the more exciting thing, 2 great presenters are actually going to spend a few minutes showing you guys the actual product. That's not something we've done before we thought you guys might enjoy seeing what we actually do. So I'm going to start by handing it over to Jeff Hastings. Jeff, are you there?

I am here. Hello, everyone. Good to see you. Yes, let me go ahead and share my screen. So I wanted to start kind of where Dave left off in terms of building. Is the screen share actually coming through? I don't see it behind you.

Jeff, we're not giving it yet.

Okay. It says I'm sharing it. So I don't know if we need to just stop sharing on your end in order to free up the screen?

All right. You're up.

Okay. Excellent. So I thought I would go ahead and just build on some of the examples that Dave gave as it relates to how facilities are using the VNA. I'm going to start with the VNA side, and then I'll pass it over to [ Kristi ] for the Viewer side. But our focus is really going to be on the differentiators, what makes Mach7 unique and how those directly translate into benefits to the health system. So I'm just starting on the VNA. As Dave said, storing DICOM images is fairly run of the mill. Everyone should be able to do that. But one of the key things in the Mach7 solution is the ability to take that study in. So just think of this as a top-down workflow. We're taking the studies in, we're doing any data normalization that may be needed, modifying the patient medical record number, things of that nature, series descriptions, et cetera, bringing that in, normalizing it and then placing it into the appropriate logical archive segments. So as Dave said, this is a software-only solution. We're allowing the health system to choose their server infrastructure, their storage infrastructure. But then we break that up in the logical segments in order to place radiology in the appropriate segment, cardiology, dermatology, et cetera. And the way we do that is by utilizing the DICOM header information, so it could be as simple as just the department name or it could be any of a number of different filter criteria or identifier -- identifiers that are contained within the study. So it gives us a lot of flexibility in creating rules that say this study should go into this segment. And why is that important? What this allows us to do is 2 things. The first is to provide an enterprise access to all the studies across all of the segments based on their user credentials. So should they be able to see into the dental segment or the dermatology segment. The other thing that allows us to do is to create individualized life cycle management rules. So for dermatology, they may have a different image retention policy than your cardiology or radiology department. So by breaking up the studies into logical segments that allows you to create that granular control for both user access as well as the life cycle management, like I mentioned. The other item I wanted to hit on was the research piece. So very frequently, whenever you're doing research studies, you may want to store those. But in addition to storing them in your own segment, is doing things like anonymizing based on a template that's put in place or maybe even saving it based on a specific Transfer Syntax. So within the medical imaging world, there's many different Transfer Syntaxes or different types of how the image is encoded. Maybe that's a better way to think of it. And so not only can Mach7 do the normalization on the patient identifier level, but even if it's converting it from maybe an old way of scanning that the machine is generating and modifying that, putting it in a newer Transfer Syntax or a specific syntax that the research department may need. So you could think of that as the data normalization on the encoding layer as well. So a tremendous amount of flexibility there. I want to go into teleradiology next because it's very similar flow in terms of top-down workflow, but specifically with teleradiology, many times the spanning sequence or what they're calling it, the type of study may not match up with what the other -- what they've decided the standard should be on the hospital. So the ability to use a compare replace matrix in order to normalize how they're calling or what they're calling or naming that study. The same thing is true also for adding a prefix to the information and identifier like imaging center #1, et cetera. So it doesn't cause any data conflicts or it can be easily identified within the solution when the radiologist is reading it. So you bring the study in, do that data normalization, like I mentioned. But in this example, I'm not archiving it long term. I just want to put it in a teleradiology cash that I've sized for 6 months, 1 year, 2 years, whatever I would like to have. And then at some point, that study just falls off, it hits the high watermark and falls off the cash. So the key thing to remember on this teleradiology workflow is that we have the ability to both normalize the data as well as treated appropriately as it relates to image retention for studies that are coming in from the outside. I'm going to switch over to the -- I'm going to stay with the teleradiology topic, but switch over to the workflow engine to highlight another component as far as the benefits of the Mach7 solution. So frequently within the teleradiology workflow, the technologists at the scanning side is just pushing the study into the hospital. The hospital may or may not know that, that study is coming quite frequently they're not getting any order or any type of information in advance. All they know is that the study has just been sent to them and that's it. Frequently, historically, they're also relying on that technologist to send them the comparison studies. This is a new CT. I want to look at the last 3 CTs for that patient in order to check the progression of the disease or what's going on with their health. So they're relying on the technologists to properly select the study and then send it to them before the patient or that study can be read by the radiologist. That can be quite time-consuming and prone to errors. So what Mach7 can do is actually build out automated rules that allow all of that workflow to occur automatically. So the technologist is just sending the study to the hospital. We're using an internal archive event of archive. So I've received the study, and that's what's going to trigger the workflow for this teleradiology rule that I've built out. So I've received the study. I can query the source that suit to me, suit the study to me. I can go in and automatically query their archive, in this case, an MR of the brain. I'm looking back 4 years, and I'm pulling the 3 most recent priors. But if this was a different type of study, maybe a mammography exam, there's different rules or different guidelines for comparing historical studies. So I'm going to look further back in the archive. I'm going to go back 10 years, and I'm going to pull the 8 most recent priors. In addition to that, I'm going to look in maybe a separate archive and have a rule that says for anything at all, I want to go back 3 years and pull 5 most recent priors. So each of these individual boxes, this is all just automated workflow that has kicked off immediately. But if you turn this into time required for the technologists or staff at that same facility, this can be minutes or sometimes an hour worth of time for them looking, finding the older exam and then sending it to the radiologist. All of that time delay, what that means is that the radiologist is not able to read the new study until all of that has occurred. So by having these automated rules, it saves time for the remote hospital, it saves time for the staff at the hospital itself as well as being able to allow the radiologist to read sooner. In addition to that, so these are all just DICOM transfer pulling the images, if you will. HL7 is another integration methodology that's usually used for billing lab work, those types of things. You could set it up or you send a message and it shows 7 message back to the hospital saying we've got the study, the reading is in progress as well as maybe perhaps creating an order since an order was never sent, you could use simply the receipt of the study to generate the order and appropriate billing on the hospital side as well. So tremendous workflow advantages for the staff by creating these types of rules. Another one that I wanted to mention was the non-DICOM or this even could be DICOM workflow like point-of-care ultrasound. Many times, those -- the pictures they take with their phone for dermatology or a quick ultrasound that they're doing in the emergency room, those are not captured in terms of the image record, made part of the larger longitudinal record and they also may not be billed for simply because they're just a quick look, and there's no actual order that's been generated. So Mach7 can also be used in that situation where we're receiving the study either non-DICOM or DICOM, but then create an HL7 message, that actually generates that outbound order, so it can be billed for properly also so it can be put in the appropriate context within the patient's record. So again, all this ties back to improving the patient care and enabling enterprise access for the images that have been acquired. The next one I wanted to hit on was the actual DICOM routing. And David mentioned this, and Mike also mentioned it in relation to the AI workflow. And so I want to give you a quick example on that. So within AI, as was discussed previously, you have many algorithms that are customized to do very specific things. So it only maybe -- it might be a CT of the brain. That is the only type of the study that needs to be sent to a particular algorithm and something else may be used for a CT of the chest. So a facility may have multiple algorithms, and there's 2 ways to accomplish getting the studies to the algorithm. The first is by sending it directly from the modality or having someone send it from the radiology tax system that can be time consuming and fairly complex as well. If they forget to send it, then that algorithm never processes the data, so you're missing out on the advantages of the AI. The other option is to create an automated rule, and that's where Mach7 comes into play. So in this case, we'd be bringing the study in the same type of tool workflow, bringing the study in. But then based on the criteria that you've established, i.e., the protocol name, the type of study, et cetera, you use that as the trigger to say there's a match, I want to route it to this AI destination. And so all of that would occur automatically, again, with the goal of improving the turnaround time for that AI result to come back and be ready for the radiologists to use during the reading. In addition to that, you can have times where you're wanting to evaluate an AI algorithm. I want to test something I've built on my own or maybe a new algorithm that I'm considering, but I haven't purchased yet, so you could narrow it down and say, for a particular study description, but all the way down to the individual machine name. The Station AE Title, that's the unique name of an individual scanner in the hospital. So you could have a rule that only applies to a specific type of study performed on a specific scanner within the hospital. That's the 1 you're evaluating. So if that's a match, I'm going to send it to 2 different AI algorithms in order to see which 1 might be better suited for my patients, et cetera. In the same thing can be put in place as far as doing any type of anonymization or data normalization on the output side as well. So I've just barely scratched the surface in terms of the functionality that the VNA provides, but I wanted to keep it focused on a few specific use cases that really benefit the health systems that are using our solution. So with that, I'm going to stop sharing, and I will pass it over to [ Kristi, ] who's going to cover the Viewer side of it.

Yes. Let me know when you can see my screen?

I can see it. Mike, others in the room, can you see it on your side?

Yes, come up.

Okay, great. So as Jeff has shared with you guys, we have a pretty powerful VNA that's going to drive our solutions. What I can show you is eUnity. It's the viewing side of the application that's going to allow us to see the images and some of the different workflows that we've talked about. And so we are a HTML5 viewing solutions. We are Zero-Footprint download. When you come in and you start looking at the images, any type of device that can hit an HTML5 browser can open up and look at the images, which is really direct, PCs, tablets, phones like we've talked about. Zero-Footprint download 2 also needs we're not adding patient information and leaving that behind on the customers' devices. So that makes it really nice as far as security goes as people were moving around with our personal devices. We're going to start out by showing you the application in a direct launch. But most of our enterprise viewers are probably going to go in through the EMR. On a direct login, what you're going to see here, is I keep everything very simplistic because we have a lot of people on the enterprise that we're going to need to be able to go through the screen. So as you look at that, we took it very simplistic to be able to grow in interest, immediately access the images that we need to look at. What you'll see here is I quickly opened up [ Mr. Kumar Vinit's ] jacket. For anyone in the room, if you have more than 1 set of images, this might be what some of these X-ray Jacket used to look like. It's just now presented in a list. You'll see from the newest all the way to the oldest, nice, fast and easy. So with this direct launch, it's really easy to identify what images I need when I go in and open up and look at. But when I'm doing that, I'm going to go and have with you guys, and I'm going to start with the oldest set of images. When I open it up, I'm going to really, unrealistically take this through nice, fast, and easy. You guys can see very quickly how I can go through it. This is a trauma case. What you can see down here is I just went through 17 images nice, fast and easy. And what I didn't start with is the way that we have our viewing environment set up. I'm actually in the Texas area. And multiple, multiple takeaway from me is where my servers and my images are being hosted. So we're out of Virginia. We're able to now pull these, and I want a very standard connection to my house. We don't even have fiber into my house right now. So that just didn't give you kind of an idea of how quickly when we talk about -- how quickly someone could come in and use these diagnostic quality images, how quickly they could display them. And when we talk about that, how are we doing that? What really makes us different? So when we look at all these images, what are the things that you'll note here? I want to open up really quickly just 1 particular image. This is a 14 by 17 image when we used to take it on film. It's a 2K image, if you start thinking about that more computer-based. When we look at this, I'm on just a standard laptop, so no fancy graphics cards or anything are driving what I'm pulling for you. So what we're doing is even with this full resolution image, we're looking at how the users are interacting with the data, what kind of displays which are we using, and how are each one of the individual reports configure and lots of other things in the background, we are customizing how we share that information with the client so that we can be very fast. And what you alluded to here is there's an easy zoom factor that allows me to see on this lower resolution monitor. How am I just playing that? But instantaneously, I can go in and grab that 100% to look at this fine and very quickly see that a diagnostic quality where I am. So it makes it something that is very easy for the users to gravitate and wants to use. In our past life in radiology, we used to always know that they got less of an image, you can actually just diagnose somebody. So this is very powerful for us to be able to share that these are full resolution. And we can give them everything that they need regardless of where they are. When we look at this, the other things I always look at, in clinical care, it's not usually just one set of images that are going to be necessary to take care of you or to take care of your family member. What they're really going to be looking at is what's going on in your entire history. So the nice thing is when we talk about that history, it was great on a direct log in that we can see that. But if some of these coming into the EMR, they're launching directly to these images, and they can still get to it. So as the doctor is taking care of this patient, he can go back to the older set of images provided and then follow what's actually going on in this patient's history. You can see here, this patient has gone through lots of X-ray and different types of studies after they had their trauma. And you can also see even when those data sets get a little bit more complex, we can divide those out and change them with some automation that is going to allow anyone. So the surgeons, not just the radiologist, they actually have tools to activate it and make their life simplify, less clicks, more efficient. So you'll see here I have nice lines that are showing me where I am on the spine, but we also build in a lot of tools. They did a really great job identifying some intelligence and the tools and being that somebody might need as we're looking at this. So if I was the surgeon, not the radiologist, I want easy ways to figure out where I am in all these [indiscernible] So you could see if I started here, we have 5 lumbar. So if I started on 3, it started here 5, 4, 3, and I place that marker. It goes ahead and moves all the rest of the images where they need to be, but nice and easy. We can do that on any image. So if I'm over here, I don't have to move my eyes away. Or if I'm over here and I want to start identifying down here where I am, I can quickly continue to label as I'm looking at the data set. So it needs it something again that's very easy for the users to gravitate and use, which gives adoption by a lot of the enterprise users that did not gravitate to using these images and rely heavily on the radiologist reports. We're going to continue as we look at this patient's record though and find out where did they end up. So we can see as they go through, they may have gone all the way through into the surgical environment. Maybe this instead with a broken hip. So as you can see, as we look through the data set, now we're seeing different types of information that may be necessary to take care of the patient. So what you see here is I have a hit, but the surgeon may have needed to add some additional push processing to that, maybe something that we don't do inherently, and that's okay. Because if they have applications that send us that information, you can see how we were able to add that into the patient's record and display it. And because we have a really powerful way to integrate, they could add that into this menu and actually want to recognize that application if they needed to use it where they are. So it's a very powerful field to go in and continue to take care of that patient, regardless of what type of additional information that you may need to add into their records. Adding into what Dave has talked about and what Jeff has shared with you when it comes to the enterprise side of things, we need to be able to really address all the different ways and things that people want to see and then be able to narrow those down if it's not something that's important to what they do. So based on my user actives right, when this patient left us after surgery, you can see they came back in with chest pain. They have their EKG, and this is a really sticky subject. Some people don't want 15 EKGs listed in a patient's record. But you're a cardiologist, you're nurses, they need to say it, to see it. So based on the way we can segment that archive, we have the way now to be able to go in and say, these people are presented with it, other people who don't need to see it, you can see in the background. And then for those who do need to see it, now I have that EKG here. But I have the chest x-ray that actually goes along with that diagnosis so I can look and see what's going on in the heart. Nice and clear. But in a lot of cases, what we see when we've gone through that heart, we find out it's really not anything going on with the heart. Then instead, it might be instead that they've got something actually going on with the GI system, quite common for that to be misleading when they come into the ER. So you can see here how we've added additional information from the endoscopic use, making it a true enterprise view. And then we can have those same things that go next to that CT and that they may have needed. So it continues to build on the patient's progression in the entire enterprise, the different departments that may be necessary to care for this patient. So we're going to take it into the last 2 things of this patient's record. Two of the last places they came to visit us was they were having some pain maybe in that wrist. So what you'll see here is typically, lots of places will make the doctors go back and forth. Well, this turning it into something that's visual is easy for the radiologist, but not necessarily only very clear for someone outside of radiology. So we want to make sure that they have tools at their fingertips that are going to allow them to use it again more effectively to actually see things that they need to see to take care of the patient. And what you'll see here is advanced information that would allow me to say, you know what, no matter where I am, I need to be able to quickly drop that in to a volume metric. I need to be able to see all 3 of the planes nice and clear. Maybe look at what's going on in the hardware, but even more so now I have this overall picture that we were talking about, being able to take those twice of images and quickly look at that hardware nice and easy. So this allows us to see there's really not anything going on with the hardware itself, but you can see maybe what actually was occurring is pain. Pain was actually coming up from a rash in this patient. So here's the dermatology that now gets added into that patient's record making all of the clinical decisions more clear and more effective. So when we see this, it makes our enterprise discussions very broad. We like to take everyone into a lot of how you thought at. Because our product offers a lot of ability to go in and say, have you really thought at every department in your environment. So maybe instead, it is the sleep study. A lot of times, we find people are sitting in office buildings not necessarily mixed in with that chest X-ray, but because it goes so much along with what they would need to see in a heart that's potentially enlarging from sleep apnea, wouldn't it be great to put it into that patient's record. And we can also, from what we've talked about before, look at some of the different divisions. So a lot of times on some of the more complex workflow, patients are going to have things like a dental that goes into the surgery. But we don't want to just be able to see these. We've actually started adding functionality and to be able to support it as well. So your dentist might want to come in and be able to say, we cannot only support the display of the images and the way they might like to see it, but we can actually measure that route for them, and actually say that into the patient's record itself. And you'd be surprised, a lot of dental units out there who need to do 3D take that CT scan that they're doing out of the job to be able to see what's going on in that patient's record and on that patient's jaw line, what's going on with that tooth segment those out, we have the capability to support that. Well, last Saturday which is also for you on the enterprise discussion might be something like your ophthalmology review. On this type of a study, it's really great because when you go in and you start looking, we can see here that, as we're looking at all these different types of images, so you can see how we can add the motion to them. But we also, again, don't just think about displaying them. We want to start adding in common features that allow us to navigate them more effectively. So for example, here, you can see a red-free filter that allows to see the retina nice and clear. I'm going to take us now from just the enterprise view and quickly go into some of the other workflows on this modular solution. So let me log in and let us go in and talk to some of the workflows maybe that have been positioned. It's really amazing to be part of this platform where you can go in and start diving into what are the pain points and a facility. How can we help augment or change the pain point? So I have some really great examples for you that are built into the platform and different ways that customers have actually added us into what they're doing. So I logged in as an administrator, and I'd like to be able to talk to a lot of these. So I keep them at my fingertips. Some of the things distribute a note, we've got customers who have already paid for a tax solution, for example, but they don't have a really good downtime scenario. They need to be able to see things like what's going on in the stroke unit. A lot of people do not think about breast imaging, mammography. When their systems go down, people are reading 100 to 200 studies a day, and there might be 4 or 5 or 10 readers, how far behind are you going to get if you miss 8 hours. We want to be able to, of course, address EV stat reads, but we do have people today who are using us for that type of workflow. As well, you have very image intensive users, Dave mentioned. Orthopedics is one that is very common. They can go in through the EMRs and they can build some small list. But most of the time, that's not quite as effective as they would like it to be. So instead, I could by group or by user go in and build out some workflows that are going to allow them to keep the study, this has been done maybe that are coming through today, including maybe the reports that have been generated by the radiologist. I could also see over the last 7 days of the inpatients that are here or who's telling you with things that I order for today that I need to be able to make sure that I'm ready for. So it gives them some high-powered workflow as well with all of the automated features that a radiologist would typically look at, because I don't know about in Australia, but I know my orthopedic surgeon, who is right here, he sees a minimum of 110 patients in a day. That's a lot of patients to look at it and almost every one of them is going to have images. The last couple of things we can talk to you quick [indiscernible] images, you see like Dave was talking about, being able to go in and direct the different workflows to fix the different types of study and then into the radiologist reading. So for me, when we look at the flexibility of what we can provide to what we can provide on these worklists, you have a lot of opportunity to talk to different scenarios on our platform. When we look at this, I'm going to go for some really quick images just to kind of show you how we tie together that exact same viewing environment. But because of the automation, we're going to launch a lot of additional personality. So you're going to see here, I'm going to just do something very basic and easy, something like chest x-ray mixed with maybe some very high-powered volumetric review that's necessary for trauma. And then we may go in and quickly say, you know what, there is a lot going on in mammography environment, especially with the attrition of the readers. This is a very specialized high demand area that a lot of people are -- have been really honed in on 1 or 2 specific vendors to support it, but we want to give them some flexibility outside of radiology. So we're going to talk about how some of this works. When you load these in, what you're seeing normally in this environment, you're going to see that I would have multiple monitor setups. So you're going to see, you're going to see a couple of flashes on your [ strength ] and this is going to watch you for the second and third monitor. This is usually where the radiologist would be living. But I'm shrinking down for just a quick few seconds, this is the information that we're sharing with them. The piece of paper for my long time ago handling them, this is the orders. This is that full patient history down here. This is the queue that we just built out that we're going to read and any information that might be shared and whether that's coming from the technologists or other people that might need to be able to have that functionality available. Now we're going to take you back to the images. We're going to say how quick and easy that we can look at these and here's my current and my prior and I want to go from single view to my second view, so that's considered anterior or posterior to a quick lateral view. And then what you're going to see down here, I'm going to open this up, you can kind of see what those images look like. But as we do this, I might go next, next, next, how many priors do I want to compare to see what is going on as this patient has come from having a port prior he did not. For now, I can go back in and nice, quick and easy verify what's going on with this. Now my radiologist may keep this open, and they may place it in different locations, so they may want it at the top of the screen more customed to where some of the other vendors may place it where they trained. Or you may see it at the side of the screen. So we want to make sure that they're comfortable and we support where they need to see that information. As well, we want to make sure, again, those tools are nice, fast and easy. So I'm quickly going to go in and do some things with my hot keys to go in, shortcut keys to go in and make the quick ratio measurements that they would typically make. Now I can move on and diagnose, or if I got interrupted, I can quickly come over here and yes, I know. We thought this, trauma case that's ready to go. I'm now taking that telephone call. So I'm going to quickly go through and I'm going to look. You guys can see they only sent me one image. I can't turn things around, but I see there's other stuff going on up here. Can I quickly, even in a lower bandwidth area, open this up? This has got a lot of pathology to it. It's over 9 centered images. And look how fast we were able to load that up and move it around and see the displacement going on in the pellets here that was so hard to see on the other images. So a nice that's an easy review of something like that. But even more so, as I met her, I explained it, I got interrupted. So who am I on the phone with that might need to actually take a look at these. So I'm going to go and share my screen nice, fast and easy. As we begin to look at this, I'm going to have Jeff join me very quickly so that he can see the displacement that I was looking at. And you can see Jeff's cursor, he's live. He is now rolling those images. My hands are away from my keyboard, but that's going to allow us to truly go. Now if I get the keyboard and I go, you know what, how about here? Do you think we need to play screen share so Jeff might come in , or you know what, I think we actually need to do some stabilization here. So you can see how fast and easy it is for us to actually have 2 collaborative, not -- well, it's image 222. And I think if you look at that, we need to move. You can see that true collaboration and effect. We have seen this in great distribution, for example, a veterinarian world, where we were able to see 17, 18 students remotely joining in to learn about how to diagnose animals. So very flexible. I'm going to take us back very quickly. We can go even bigger into this on the volume metric side. We provide a lot of different functionality being able to see this in 2 different planes or to place it into what we call a MIP, maximum intensity projection that really highlights the vasculature in the lungs. We had all of the capabilities built in for quick, fast and easy review. I'm going to take us now instead though into mammography because we haven't really touched on what we can do in that environment. Many people have been really associated 1 or 2 vendors here. And the thing is it even reverse these are -- sorry, these are about 18 images. They are true tomo images. So for anyone in the audience who know, these are quite large data sets. So you can see how fast and easy it was we can immediately display everything. So we have 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 just in the current print. And it's nice facts easy for us to see that we have all of those 12 images immediately displayed. And they were to provide them some really fast workflow with some amazing gaming protocol functionality. That -- Now those across the board for everybody, but it was really generated by those who need to breed mammography because it is a requirement to see the different views. So you can see here as I go through and I look at any of these images, these are requirements for how we look at the data set, but there are some things that are quite unique to us. The ability to stay, and we could hot key this to make it really fast and place it on any kind of peripheral device. But if I see something and I go, you know what, I want to mark this for the surgeon. I don't have to type in a separate or mark it and then measure it and then type something in. We have functionality that is built in to meet that specific need. And nice, easy ways for us to see again prior in this, if they read multimodality and mammo, they don't have to learn different tool sets because it's all the same viewing tools. So I can come in now and basically the next prior, next prior, next prior on all my mammo images nice, fast and easy. It looks like I passed one. Let's go back to here. These are chemosynthesis images, being able to staff those images and go through them quick and easy. As well at any point, we can go back and forth. These are things that other vendors struggle with, but we can do that effectively to keep the mammographer constantly in the same pattern, and we can do this because we can do this where they are in that same effective kind of whether they are screenings, whether they are diagnostics, whether it's 2D -- 2D CAD markers or 3D CAD markers, we can support all of that workflow and that doesn't just have to be within the facility walls. It is cost effective for us to be able to roll that outside to support reading remotely. So some of the facilities who can't get mammography readers can get support through our platform. Any additional types of data sets that we want to be able to open and share might be things like we might be looking at AI. We can generate and put that into the workflow. We can segregate that out. So all of those are things that we're able to go in and address for the customers. And we can do it very modularly based on where they are in their [ IgM ] solution strategy. Mike, I think from here, I'm going to hand it back to you.

Thanks, [ Kristi. ] I appreciate that. So hopefully, this is the first time we really spent time showing our products to the investment community. Hopefully, we showed you the appropriate amount of detail trying to give you a flavor for our 2 most popular products and we're going to start to visualize what we talk about when we're speaking about workflow. So I think with that, we'll open it up to questions in the room first, and then we'll move on to questions that we may have remotely. We will have a microphone that's brought around for questions simply because those that are on the Zoom will be able to hear those questions better with the microphone. So if anyone has any questions?

Jules Cooper from Shaw and Partners. I really appreciate the look in terms of the detail of the products. Just on the data normalization secret sauce, David, that you'd sort of mentioned there. I guess, being an independent VNA probably helps in terms of being able to deliver those benefits to the customer. But could you maybe just elaborate a little bit around how Mach7 would be executing that data normalization versus other independent VNAs? And then also, if you are sort of utilizing technology from a vendor, right, are you thinking being tied to a particular piece of hardware. Just how they navigate some of that data normalization from sort of vendor-specific solution?

Yes. Happy to. It's interesting. I've worked -- I've been in imaging my entire career, worked at numerous vendors. Every vendor I've ever worked at has claimed to have a vendor-neutral archive, which is very misleading. The main difference is most vendors -- vendor-neutral archive that they refer to is really just a DICOM repository. The only thing they're doing for their customers is collecting all the data and storing in the common location. Even if they can do some of the things that we said today such as data normalization, DICOM routing, life cycle management, typically, the only way their customers can get that type of being done just to call the vendor place a service call and when the vendor gets around to it, they'll write those rules that you saw Jeff doing, et cetera. Our moniker and Mach7 is we want to put our customers in charge of their own data. So anything that I can do to the solution at Mach7, to my services team, we provide the customer themselves the training and the flexibility and the access to do all of that activity themselves. And so it makes it very fast and efficient, and that's why customers buy us for their VNA.

This one might be for Mike. So earlier on, you were talking about acquisitions and obviously, eUnity was a very important one. How would you kind of think about now that you've got the front end and the back end? I mean where are the kind of gaps where there is potential to have an impactful acquisition?

Yes, sure. We're always evaluating that. And that answers this, but I think as I look at the market right now, it's really around more workflow orchestration. It's around optimizations. It's around the ability to provide good analytics, good data science capabilities with all this data as we start to gather that. I think that over time, there's going to be a lot of different uses there. And so I would say it's analytics, it's workflow orchestration. It's those type of capabilities. If there's one, I wouldn't say that we've got a gap in our technology staff right now. I think we're where we want to be from a back end and a front-end perspective across the enterprise. I think that we don't want to create a Frankenstein approach, and I think it's really difficult to try to buy specific pieces of technology, if you're trying to buy an advanced visualization tool and add it to a viewer, that becomes quite difficult. And you don't want your product to sort of have that, that sticks together feel. So I think some of that stuff is better to build organically, but things around analytics and optimization and workflow, I think, are opportunities.

Sorry. So just going back to the VNA. I just wanted to ask a question in regards to rolling out the V12 and obviously, you mentioned there, actually choosing. Just wondering the feedback from customers that use the VNA, if there's anything missing there? And if there's that V12 is going to sort of solve some of those issues? Or what's the plan there?

So the main reason we rolled out V12 to replace some of the legacy Microsoft products included in it. So the initial customer that took the first verse of V12 went extremely well, really no issues whatsoever. And we continue to monitor that. I'm not aware of any problems we can have.

No. The big thing for people wanting to take V12 beyond the new functionality that we've built in is the fact that we've replaced the Silverlight technology. Silverlight was a part of Internet Explorer that has had some security gaps in the past. It's end of life. So you had to create a whole new [indiscernible] to accommodate for that. So that's really well, it's really also, it's being on the same path with the IT organizations in regards to doing something like that, right? If they're still on exploring, if they're still using Silverlight and other applications, it may not be quite as important for them as for others. So it's just a priority for our customers.

One other one, just around -- you also spoke, Mike, at the beginning around the more complexity in an enterprise, the better it is for you guys. I mean there's a lot of M&A happening in the U.S. health care space and around the place. What's your view -- I mean, do you think M&A makes for a simpler enterprise or a more complex one? And I mean how do you think that kind of breaks down? Are you getting more customers or less than, you know what I'd say?

Yes, good question. And different answers depending on the acute care or ambulatory care. On the acute care space, it can work to our advantage. On the other hand, if a hospital group is using someone else's technology, there's risk associated to that, right, where, depending on which vendor they have a preference towards. I would say in the world of M&A, it does seem like it doesn't really matter if you're the buyer or the seller right now that just because you're the buyer it doesn't mean you're going to keep the technology you have, you may be doing a buy because you like the technology that the seller has, right? So there's no guarantees there. On the ambulatory side, it's typically making a more and more complex situation, more and more complex, and that usually works to our advantage.

This is [indiscernible] from Jefferies. Just a question on the KLAS ratings, which we brought up before. So I'd be -- I couldn't understand how important these are when we go to marketing and how much it comes up in conversations and helps us win contracts just in regards to the different types of, I guess, categories that they have for KLAS. So we're ranked very well under Universal Viewer side. But is the vendor-neutral archive the only kind of target going forward? And I don't really understand how these things work through in terms of PACS or like above -- if there's a large PACS or if there's a low PACS, is that a common area that we could be talking to?

Sure. So KLAS is a low cost scaled, without a doubt. Listen, we are ranked with KLAS in the 2 sectors, right? The Universe Viewer, which I think KLAS might be redefining the definition of a Universal Viewer actually soon because what exactly is that you'll find that they have a new category in there. I can't remember what the acronym is. But essentially, it's a -- this is part of an ecosystem. You can't buy the product independently. So it's starting to label products like that in KLAS. So you can know, this could be ranked high, but you can't buy it without buying a PAC solution. So KLAS is starting to get a little bit smarter about that. For us, the 2 segments are important. It's just the -- what would be missing for us to classify ourselves as a PAC solution is the simple fact that we will sell either of our components independently. Any of the people that are listed as a PACS provider will not sell their components individually. It is a combined ecosystem that they are selling as a PACS solution. You'll find other vendors that are not included in PACS, that you would traditionally think it could be. And that's generally because they don't have a complete back end. They may have large cases, but they're dependent on other back ends. So not everybody's listed in PAC sections. Most PACS companies are broken down between what size customer that they'll sign whether most PACS companies are not going to do an academic center and an imaging center. Most are going to do one or the other, right? But from a VNA perspective, I'm starting to see more acronyms there from KLAS II with the fact that we got VNA, but it's not independently purchasable, right? The VNA section, you'll notice is starting to become very bifurcated in the sense that a whole chunk of us right now are in unlimited data. It's not just us. There's a number of VNA customers because it's a back-end product, right? So you're talking about IT resources and they're going to respond to surveys and some will, some wall, it's just -- it's giving their attention to do it for all of us, not just us, they'll become a difficult part of managing KLAS. In regards to how important is it. Look, it's important to us from a marketing perspective just because I do think that clients look at that to decide who they want to invite to an RFP process as an example. Is it the end all be all though? It's not, right? It's one data point, one data point amongst many. But it's an objective data point that you can point towards.

Jules here from Shaw again. Just I've got 2, maybe 1 for [ Kristi. ] Thanks for that fly-through on the viewer. Helpful. I'd just be interested to understand lots of functionality and sort of integration capability that you demonstrated there, particularly on the enterprise side. How much of that functionality or IP resides in the viewer itself versus being powered potentially by the VNA? And is the experience if you were sort of using the viewer separately to Mach7's VNA, for example, would it be widely different? So that's the first question. And then the second question is just on that DICOM routing. David, I wondered if you might be able to -- you talked about sort of the secret sauce in the data normalization. But is there some secret sauce potentially or benefit to the independent VNA around the DICOM routing side as we sort of move further into the AI space?

Kristi, you can go first. Are we able to unmute her?

So as far as the functionality goes for the volumetrics, those are part of our platform for the enterprise as well as for if we're launching it from the VNA. The VNA is just going to add in some relevancy rules so some things that allow us to see current suppliers and some workflow in that direction. So as far as the viewing environment goes, you get the standard tools across the board. I think the other thing that I could add in as far as adding in additional applications, so you just have to have the viewing environment to be able to do that, but we can also do that through the VNA. So it's a very modular on how we're able to position that whether they're using just the enterprise or they're going to go ahead and go with the VNA and the enterprise viewer. I hope that answers the question.

And then in regards to DICOM routing, it's really the same story as data normalization. Typically, with our competitors, especially PACS lenders, true PACS lenders, their customers have to tend to buy third-party DICOM routing solutions that they integrate to. And so there are DICOM routing solutions in the market that you can buy to make PACS better. A very few vendors in the market have all of those tools embedded in their solution like Mach7 does. When I say very few, less than 4.

Mike, Just a question on, I'll just talk about switch between kind of like the capital model and the subscription model. Is this really a -- is there any, I guess, ties for the different models on a geographical basis? And does that speak anything towards a shift in terms of geographic waiting? Or is it more so that across different geographies, we're really just trying to switch over to this subscription model?

Yes. Good point, actually. For sure, the U.S., I think, as an industry is heading further and further towards subscription. But outside of the U.S., particularly in the APAC environment, it's heavily capital oriented. Not only that, but is heavily capital perpetual license-oriented in the APAC and Middle East, which is entirely different than what you see in the U.S. and in other OUS markets.

I'll just ask 2 other quick ones. Just in terms of our systems and from a technical perspective, are you able to speak a bit on the latency and bandwidth requirements that we expect? So that's one. And the other one is just asking on the unit economics when we're talking about having the resellers or the wraparounds, and how that works?

Yes. From a bandwidth perspective, we're a low-bandwidth system, right? We don't have specific network requirements that we're providing to our customers. I mean, the fact of the matter is any customer that's got a modern network architecture, our system is going to work fine. Modern wide area network connections are fine. But we don't build our solution with the need to have complicated architecture, either from a network perspective or from an infrastructure perspective, as Dave alluded to, from an infrastructure perspective, we're agnostic. We don't care what vendor people use from a hardware perspective nor do we care if it's an on-prem or private cloud or public cloud. We have S3 storage, we don't care. We're completely agnostic to that. We don't get involved in that. We are a software-only provider. We provide 0 hardware. We are nothing more than services and software. Okay. Sorry. So typically, are resellers of our products. We're providing them pricing, right? They're buying from us and they're generally buying maybe a slight discount. However, they want to resell that product and whatever percentage uplift they want to put on that product, that's on them. That's not part of it to us. We don't do revenue-sharing models or things like that. We're selling the licenses to the reseller, the resellers uplifting that and selling that.

This is 1 more around early on maybe for Dyan or Mike. You're talking about FY '24 and what you've kind of sold in the last year -- sorry, FY '23. You're talking about cross-sell opportunities. I mean, how much of your current customer base would you say are using other vendors? And I guess, just to get a sense for the opportunity for the cross-sell across your...