Security Without Limits: Why LVT is the #1 Innovator in Mobile Surveillance

Jared Richardson:

All right. It's a little past the bottom of the hour. I figure it's a great time to get started. Welcome everyone. My name is Jared Richardson. I'm your host today and I'm with LVT. We're talking about security without limits, specifically analyzing the Frost & Sullivan radar that's out looking at the MSU space. And I wanted to talk specifically today about LVT's ranking in that analyst report as it pertains to innovation. So I have and I am joined by two of our experts in the space. First, I'd like to introduce Steve Lindsay. He's LVT's co-founder and our chief strategist. And of course, we have Ryan Anderson, our head of competitive market intelligence. But let's talk about the Frost Radar. And before we get to that, a few housekeeping items. This webinar is being recorded. You will get the recording a little bit later today. We'll send out a notification.

If at any time you have questions for the panelists, you can leverage the Q&A. Myself and my team members are monitoring those questions. We will have time at the end of this presentation to get to those questions. Or if you ask a really good one in the moment, I might interject and ask our panelists right then and there, so we can talk about that while it's hot and relevant. So those are the housekeeping items. And now let's talk about the Frost and Sullivan report. So who is Frost & Sullivan? You've probably heard of analysts like Gartner and Forrester that produce the Magic Quadrant and the Wave. Well, Frost & Sullivan does the radar. And Frost & Sullivan evaluates vendors on 10 criteria, five in growth and five in innovation. And they are looking at the MSU space, which is excellent. We've been waiting for someone to recognize all the hard work being done in physical security.

So Frost & Sullivan came out with the Frost Radar for mobile surveillance. And you can see the growth index and the innovation index here. And now we're looking at our position as the number one innovator, LVT named the number one innovator in the Frost & Sullivan report. So I want to kick this off and send it over to Ryan really quickly as we celebrate the release of this report. And Ryan, Frost & Sullivan comes out and they're evaluating vendors globally. Why is it so critical that they're looking at this space now? And what are some of the things that in your experience in dealing with analysts, why is this report so critical?

Ryan Andersen:

Yeah. Thanks, Jared. And thanks everybody for joining today. Frost and Sullivan found that these mobile security or mobile surveillance units are now an essential part of any physical security paradigm. And it's really been driven by three things in the market that have occurred. The first is the technology itself has evolved. Second, the threats are evolving. And then third, there are economics that go along with responding to those threats. And so let me touch on each one of these real quick. From an evolving technology perspective, we have the cloud, which is giving access to cameras remotely. No longer do you have to be on the premise in order to access these cameras nor control them. Vision-based detection, as well as vision-based understanding is really helping us move the needle in terms of what guards can actually do. And then reliability of cellular and power and connectivity has really given that rise of having reliability of these systems.

Now, not all vendors take advantage of the reliability that can be delivered, but that's kind of the third element of that first pillar of technology evolution. And then second, assets and threats are also evolving. So what we see a lot is that the pace of change or the pace of the threat vector has really been increasing. And so companies have to react faster. There's a lot rise in liability for a crime that occurs on your parking lots or in your facilities or around your facilities, anywhere on your property, that liability is now increasing. And then lastly, employee safety is now a number one concern for security personnel, making sure those employees are safe. Now as a result of that comes that third pillar, which is the economics. Everything in business and the world is driven by economics, and so companies have to adapt, but adapting costs money.

It takes time and it's painful. And what we're seeing is we're seeing these mobile security units being used as a permanent solution now. No longer is it just being used for a temporary need here or there, but they're becoming a permanent solution that's flexible and adaptable to those threats as they come along.

Steve Lindsey:

Yeah. I think what's really interesting about that, Ryan, is that you nailed it right on the head. The bad actors in these environments are not going to the same places, they're not doing the same things. It's this constant battle between measure and countermeasure and countermeasure constantly going on. So the agility of the systems and the technologies, both from an infrastructure and what they're detecting and how they're responding to those things is a constant evolution, right?

Ryan Andersen:

Yeah. And if a company's locked into high fixed costs, it takes me six months to do a deployment. I've just invested in this other infrastructure. I can't invest in this now even though the threat vector has changed.

Steve Lindsey:

Yeah.

Ryan Andersen:

That's not what companies are looking for now.

Steve Lindsey:

Yeah.

Jared Richardson:

So Frost and Sullivan speaking specifically to innovation and the criteria that they evaluate vendors in, scalability, R&D, the product portfolio, how we respond to mega trends and how we leverage insights from those and customer alignment. So thinking about those five categories, Steve, why do you think LVT is specifically called out as the leader in innovation in this report?

‍Steve Lindsey:

Yeah. So I think it really comes down to really three areas. And the first one really comes down to preventing crime. So when we think about preventing crime, we're not talking about activities that actually truly are reactionary. And so, I think to help with this, there's an illustration we like to give. We call these our jobs to be done. And we'll walk through this in a real world scenario. So the first thing, let's pretend that we have a site that we're trying to protect and there is an individual who then breaches a perimeter and we have technology set up to be able to detect that. So this is what we would call our detect phase, the detection of somebody on site. And so that then moves us into a validate stage. So the very first detection goes into validation and validation basically says, this is where, is this a threat?

Is this just an animal coming through? Did the sensor that I have that's set up to detect a false positive or was it something real? So once it works through that, then it can say, "All right, well, what should I do about this then?" So that's kind of that stage. The next one is what we call deterrence. Let's say that it's a low severity and we want to use automation to be able to deter that. This is where we can actually deter using lights or sound, some talk down or something. And again, it's all in an attempt to change behavior before something bad happens. We like to call that like the lifeguard, right? The lifeguard sees you running on the side of the pool and they blow their whistle and let you know, "Hey, don't run on the side of the pool." We're still in a prevention type method.

Now, let's say that after all of that's done, the individual decides to just carry through with the activity that they were going to do. Now we go into what we call the defend mode. This is where we would deploy a guard, a human to actually go deal with this. This could be either a guard on site, it could be a real time crime center, it could be an alert response system or something, and now they're going to get in real time and actually try to deal with this. So the next phase of this would be what we call reporting. So after the events happened, how do I collect the evidence? How do I report what had happened so that down the line in our workflow here, others can use that information to then build a case, which then takes us to the next step here, which is the investigation.

So this is case building activities like, is this a repeat offender? What are the modus operandi, let's say, that they use or patterns that we can pick up on to see if it's repeat offenders or just a single offender or whatever. And we're building that investigation to take us to the last phase here, which is to prosecute. And this is a very important step, by the way, in holding people accountable. And what's interesting about this is if I was to draw on here where bang is, you'll see that bang is happening right between the deter and the defend step. And so when we put bang there, we can then say that everything left of bang here is a preventative activity. We're trying to prevent crime from happening. An ounce of prevention is worth a pound of cure, right? As my grandpa used to tell me.

So everything right of bang is a reactionary. It's what we would call holding people accountable. And what's interesting about this is we're not saying that prevention is more important than holding people accountable or that accountability is more important than prevention, but there's a symbiotic relationship, right? Prevention is only as effective as people believe that they will get caught. And so if you don't hold people accountable on the right, then deterrence starts to lose its effectiveness. But most entities want prevention because prevention is less costly than trying to clean up afterwards.

Ryan Andersen:

No investigations

‍Steve Lindsey:

Needed. The crime didn't occur in the

Ryan Andersen:

First place.

Steve Lindsey:

Yeah. No loss of revenue, no loss of materials, whatever. So anyway, so if we understand this chart then that prevention's on the left and reaction and accountability is on the right, then what really is innovative about LVT is we were one of the first companies that use technology to try to solve the problem of the left. It's really easy technologically to help bring solutions to the problems on the right, but it's really hard to do that on the left. And so we haven't seen very many or even if any technology companies provide

Ryan Andersen:

That. I see very few that have actually figured out how to actively deter a criminal. They can copy what they see in the marketplace, but I've seen very few that actually

Steve Lindsey:

Understand it. Right. Yeah. So when we came up with the mobile security unit back in 2017, that was innovative to be able to actually deter. Now, what do we mean by deterrent prevention? Is this a blue flashing light? Well, it might work for a month or two, but that's kind of like a scarecrow, right? It scares the person away. But even in our history, we've learned that if all you do is blue flashing light, it starts to desensitize. I mean, the bad guys start to notice that that's, okay, not anything important and they carry on their activities.

Ryan Andersen:

Especially the more sophisticated ones, like organized retail crime, they've seen it across several locations and now this is just one more location for them to go.

Steve Lindsey:

Exactly. Yeah. So we've already learned the lessons that a scarecrow doesn't work, right? So the one thing you want to be careful with with these mobile security units is a blue flashing light isn't enough. It might work in the beginning, but your problems will start coming back, right?

Ryan Andersen:

I even see companies that are selling the blue flashing light by itself, not attached to the MSU, and they sell it as a solution, a deterrent solution.

Steve Lindsey:

Yes.

Ryan Andersen:

And as we know, it's really just a blue flashing light.

Steve Lindsey:

Yeah. The second thing that we see is let's say that you do have an active response. We like to use an example of an audio talk down. Maybe it's a prerecorded message. If the same message is given every single time, and it's kind of done whenever it sees, let's say somebody in the field of view of the camera and then that message is sent, that becomes a motion light is the example we like to use here. So I moved, it said something, I didn't move, it quit saying something, right? And that motion light is a pattern that a lot of criminals love to pick up on the pattern and they say, "Oh, okay. Again, this is just a dummy technology. It's not doing anything."

Ryan Andersen:

When I was a kid, I used to skateboard a lot and there was one driveway that was really great for skateboarding. They had a motion light. It was meant to scare people away, but we knew if we went there, we would motion and then it would give us plenty of light to do our skateboarding.

Steve Lindsey:

Excellent. Yeah. So you picked up on the pattern. So those are two examples of things that we've learned over the years that just don't work, right? So what does work in prevention? Well, it has to be a dynamic experience. It has to be something that's not predictable. There's no pattern. It's believable because it feels like there's a real human behind the scenes. And those are the things that LVT has developed over the years because of the lessons that we've learned up to this point. And one of the examples of this would be the AI talk down that we have, right? This is a talk down that's able to describe what it sees. It's uses a very believable voice and it uses intonation, it seems natural. And what's really interesting about it is it never says the same thing twice, right? So it's a really hard pattern to pick up on.

You don't really know if there's a real human there or not. And again-

Ryan Andersen:

And sometimes there might be a real human on the other end, but they just can't

Steve Lindsey:

Tell. Yeah, they just can't tell. So preventing crime is one of the big innovations that we have focused on since the beginning of mobile security units, it's something we continue to innovate on because we have learned the lessons thus far that have caused that. The second thing is really around this reliability and guardrails, guardrails around privacy and security. So it's not hard to take a cellular modem and connect a camera to it, right? In fact, it's probably easier now than it's ever been before. There's an ethernet port on the back of a cell modem and an ethernet port on the back of a camera. Boom, I'm ready to go.

Ryan Andersen:

If I have any questions, I can ask one of my AI tools to

Steve Lindsey:

Tell

Ryan Andersen:

Me what to do.

Steve Lindsey:

Right. So the question is, what is needed to make that happen? And going back to the Frost & Sullivan report, one of the really interesting quotes that came out of that that I thought was very insightful was this comment that said, "The demand for smart, agile and infrastructure independent surveillance is poised to grow significantly." And this quote really says a lot about the needs for the agility that you'd mentioned before, but also what that means from an infrastructure perspective, right? So you can't rely on ethernet cables and power cables being trenched everywhere to get you the power and communications that you need. And I would even argue that even wifi, I would consider a cable because you're bound to some proximity.

But if we can think about mobility of true disconnectedness of what cellular and satellite infrastructure gives us, we can stick cameras wherever we need them to be, right? And if the problem moves, as you said, we can move the camera and infrastructure with it. But the problem is, is again, how do you cut the cord effectively? And that's one of the innovations that LVT has really mastered. And we break these down into five topics. One of them is reliability, number two, cybersecurity, three is scale, which is important. Four, low cost of ownership and then future proofing of AI. So let's kind of quickly go through each one of these. So reliability, most people think this has to do with power. Do I have enough power and solar panels and batteries to be able to run this? And you see different MSU providers have like 10 solar panels on their thing to try to power everything that they've got.

The more efficient ones have learned to do that with less. But I would argue that that's just one of the pieces of reliability. The others are what happens when your cellular connection goes down, right? What happens if your IOT devices go offline, right? I mean, there's so many things that can go wrong, especially with these units that are deployed that like reliability is a whole sophisticated problem in and of itself to keep units up and running.

‍Ryan Andersen:

Yeah. So I'll just jump in. When we first started doing research in this space, we discovered there are really two types of MSUs out there in the space. One is an engineered system, which is kind of like in the aerospace industry where you go to every component of that system and you optimize it for performance based on the mission that you're trying to achieve. The others that we see out there are what we call assembled systems. It's, I've got a bunch of parts, they're on the shelf, I'm going to put those together. And what we saw early on was when customers saw us at retail outlets all over the country, they said, "Oh, I'd like to sell those too. Those look like they're pretty simple. I can take a modem and I can connect some cameras to it and buy a trailer." And they put it out there.

Their sole focus was on day one, right? Day one just being, "I want to get a solution that looks like an LVT unit out there and I want to make sure that it runs for day one."

Steve Lindsey:

And they probably got it working day one and they were all happy.

Ryan Andersen:

When I was a kid, I built computers for my neighbors, right?

Steve Lindsey:

Yeah. And

Ryan Andersen:

Building computers for my neighbors, I only worried about day one. Day two, firmware patching, making sure it works next month, next year at a very low cost. I didn't think about any of that. And that's what we see with these assembled systems is they're just cobbled together parts and they work okay for day one. They tend to be cheaper. They tend to be purchased outright as opposed to on a subscription basis because who wants to deal with maintenance and firmware updates?

Steve Lindsey:

Yeah.

Ryan Andersen:

So real difference between engineered systems and what we call these assembled

‍Steve Lindsey:

Solutions. Yeah. So there's been 21 years LVT of us, we've always launched our technology disconnected or with the cable cut. So we've had to learn all of these lessons on how do you actually keep systems up and running. And there's just so many things according to that.

Ryan Andersen:

I'll just add two more points. So one is, if we go out and we ask non-customers, people that aren't our customers yet, what kind of solutions they have in place. Some of them will actually say they have an LVT unit in place and we look at our customer database and they don't have one. And what has happened is these assemblers look at LVT as the standard and they sell their unit as an LVT unit. So some non-customers actually think they have one of our solutions because it's now the Kleenex

Steve Lindsey:

Almost

Ryan Andersen:

Of the industry. And then the second point is we hear numbers of very, very high reliability. So uptimes being 100%, I hear that all the time, we've never had an outage. Anybody that does statistics would know you have to have a high enough population sample in order to understand what your true uptime is. So a lot of the vendors, even though they may have measured a unit in Arizona where there's plenty of sunlight, maybe two units, and then they say they've got 100% uptime, they really just have a very small sample size and they don't understand what their real availability levels are.

Steve Lindsey:

In fact, there's no such thing as 100% uptime, not even in the most robust, sophisticated cloud systems right now, right? I think the current state is at least three nines of uptime in cloud services. I think we start pushing four and five nines, but you'll never hear a cloud service provider say we're a hundred percent. And I would say that edge devices like MSUs are even harder to keep up than that because there's so many other things that can go down. So again, if somebody's telling you they're 100% uptime, that's probably your first sign that that really is impossible, right? So what is your real uptime? Yeah, I think that's a great point. I think the next thing that's probably as important but often forgotten is the cybersecurity of all of this. We take for granted the fact that these are cellular networks that these are things are going on or satellite networks.

These are public networks. It's different than your typical infrastructure where I'm hardwired within an IT perimeter that might have firewalls and all that robust, sophisticated cybersecurity stuff in place. And so I think we forget about this, but every day we are hearing about, "Oh, I found this camera online that you can stream for free." In fact, there's many vendors out there in this space with cellular connected cameras that are just on the public internet. And you can argue all day long of, oh, it was just a configuration setting that was wrong or this was just an announcement. Well, the point is it still got out, right? And this is sensitive information. So how do we think about cybersecurity and all of this? Well, LVT, we developed our own private network, right? So our cellular systems are only connecting to a private cellular, global cellular backbone that we have control of and we can monitor.

And that's one of the things that are in place to make sure these types of things don't happen. But what are other things you can think of around cybersecurity threats that we as an industry probably overlook or that is happening that you're seeing from a competitive landscape? Well,

‍Ryan Andersen:

I think you called out a good one. Every once in a while, more frequently than you would like, you find on open websites access to cameras. And that's just because the vendors don't have another way of connecting their cameras back to the cloud system, right? They have to use the public internet because that's the mechanism that they have. They might put a VPN overlay on top of it, but those can still be interrupted or even siphoned. Well, and human error is often

Steve Lindsey:

The problem there.

Ryan Andersen:

And so we see some vendors don't have any control over their field services reps. So when they go and deploy one of these cameras because it's accessible over the public internet, maybe they don't set up the VPN, maybe they don't change the default username or password on that camera. And statistics have shown that new IPV4 addresses or IPV4 addresses are scanned about once every minute.

Steve Lindsey:

Oh, yeah.

Ryan Andersen:

And so you figure if these things are scanned every minute and it's going to respond to a ping, it's going to ask what kind of device it is, it's going to say it's a camera. You can bet that there's a whole long list of default username and passwords that are going

Steve Lindsey:

To be

Ryan Andersen:

Hit against it.

Steve Lindsey:

Well, it's funny you mentioned that because it reminds me back in 2012 when we were really heavy in this and starting to streamline cameras over cellular, we would see the audit logs of our routers and yeah, they were getting hit multiple times a minute from all these bots trying to just find all the vulnerable things that were out there. So when we started seeing that, we got scared and we were like, "Okay, we got to figure out a new way to architect this. " So that's just an example of our historical experience in this. But you're right, these things are getting hit all the time and nobody even knows that it's happening.

Ryan Andersen:

Yeah. The second issue is like firmware updates, right? If you're on the public internet, you've got to be very on top of those firmware updates. The assembled systems, they might not have anybody watching out for firmware. And when you update the firmware in the camera, now you got to think about, how do I update the firmware of my route or the other infrastructure that I might have in that MSU? An assembled system or somebody that's just new in this industry doesn't know to look at these things yet and to make sure that, hey, if I want high reliability, I've got to update the firmware for this device, then this device in this order, and I've tested it and so I know what's going to actually work instead of resulting in downtime in the field.

Steve Lindsey:

Yeah. So that actually leads to the next differentiator. I'll actually skip the scale. Let's go right into the low cost of ownership since you kind of took us there. People, I think, underestimate how difficult it is to keep these systems running from a maintenance perspective, right? You'd mentioned firmware, but there could be vandalism on the devices themselves. These are just things outside of typical wear and tear, but you got vandalism, you've got ... We even talk about some of this overlaps into future proofing, but yeah, let's talk a little about the cost of ownership problems, because again, the way LVT has solved this is we've looked at the cost of cellular data transmission, we've mastered that over time, the IT costs and field services costs of all these things. I mean, there's so many things that go on the cost of ownership, but any thoughts on

Ryan Andersen:

That? Well, I'll just say our business model is one that I should say our predominant business model is one of as a service. So there's a huge incentive on our part to reduce the total cost of ownership for these devices. Again, a vendor that has a lease or a purchase outright, there's much less drive to drive down TCO and much more focus on driving down that initial purchase price. So again, the incentive on our part is to forget that TCO as low as possible. And as we move into other business models, they're going to be able to leverage that, lower cost of ownership versus competition, they're still figuring out how do I get price on day one down, not thinking about day two.

Jared Richardson:

Right. Guys, real quick on the topic of TCA, I got a question from Scott that I want to interject with real quick. Scott asks, when we think about customers leveraging this technology and not really having to do a lot and really lowering the cost of ownership, Scott points out, he asks, "Does LVT have techs that visit sites regularly perform preventative maintenance and checks on units during the program?"

Steve Lindsey:

Yeah, and yes, we do. And there's actually a couple of motions that happen here. We have a full on network operation center that's watching these that are watching for battery condition health. They're looking for even our smart generator technology maintenance on that. And there's all kinds of that preventative motion, both from a telemetry perspective on what we should be doing from a scheduling perspective as well as the field service network to go do it. But yeah, what thoughts do you have on that?

Ryan Andersen:

Yeah. I mean, given, again, the full ownership model, we can do things like pre-test components before they actually go into the field. And so we can do the burn-in, whereas assembled systems, you can't do burn-in because you just don't have enough volume to do a burn-in and make sure that device isn't going to fail in the field. And that's the main thing that I would just add. And then watching telemetry coming off the system, we know in order to slew to queue, for example, you can grab the data on how much power draw that camera's taking to slut a queue and you can understand, you can start to predict if a failure's going to occur on that PTZ because it's drawing too much power when it's moving that same distance that it had moved before.

Steve Lindsey:

Right. In fact, yeah, some of the innovative technology we've created around that was the self-healing, right? And the self-healing also tells us telemetry on what it's had to do, how many times has it maybe had to hard power cycle something versus firmware type stuff, configuration settings. And that starts to be leading at indicators as well, right? Yeah.

‍Ryan Andersen:

And I'll just add on that. We're driven by our uptime. We own these units and we do have field services that will go visit them. For the units that have backup generators, we're definitely there before they run out of fuel. Yes. Right. So if you get those cloudy areas, our smart generators, fuel cell based. Some competitors don't have fuel cell based. They're still using combustion- Combustion

Steve Lindsey:

With oil changes and-

Ryan Andersen:

Noisy, require oil changes about every 150 hours of

Steve Lindsey:

Operation. We were there eight years ago and we learned very quick that doesn't work scale. Yeah. Anyway.

‍Ryan Andersen:

And the combustion ones can get off carbon monoxide as they get older, right? Yeah, that's true.

‍Jared Richardson:

Speaking of uptime too, Ryan, since you brought it up, another question from Scott was, with the cellular connected devices, what if one cell network goes down? How is LVT maintaining a high level of uptime?

Steve Lindsey:

Yeah. So when we talk about the private cellular network, we're talking again, it is a private network, which means it has its own private APN. So we're not talking about virtual private networks here. You had kind of alluded to that. This is not a VPN client on the MSU trying to connect to a VPN server in the cloud. This is truly a private cellular network, which means that we have one SIM card that we stick in there. We actually have some redundancy, but it's basically one SIM card and that SIM card registers with whomever the cell radio is in the area. It could be AT&T, T-Mobile, it could be of a regional carrier. It really doesn't care. It just says, "Hey, I need to connect to a radio." And once it gets there, it says, "Okay, you need to provision me on LVT's private network." So when we think about a carrier going out, it's already inherently built into our system.

This isn't a failover from Verizon SIM to T-Mobile SIM. This is where the actual, our ability to say, "Okay, well, okay, Verizon's not working. Okay, I'm going to try T-Mobile then. I'm going to try AT&T." I keep forgetting who the other three were. And this is what's really nice is the regional carriers. Most people forget that there's regional carriers in each one of these areas too. So we've got not one failover, but we've got almost an infinite number of failovers with anything that's in the service area. And

Ryan Andersen:

It doesn't require manual intervention either to fail over. It's

Steve Lindsey:

Between the networks,

Ryan Andersen:

Right? That's

Steve Lindsey:

True. It's all automatic. Yep. Yeah. So anyway, not to spend too much time on this, but scale is important. We've kind of alluded to scale. I think you did a great job of talking about ... It's one thing to build one unit on day one and think about day one, but now I'm going to support a fleet of hundreds, if not thousands, if not tens of thousands of these things. Now, how do I do that at scale? I can't do that with human field techs, doing configurations and kind of doing all that stuff. So I really have to have a system that's engine Engineered, I'm leveraging your example, engineered to be able to do that as a foundational component. So again, innovation around that. And then the last one I think leads into the rest of the talking track here, and that is future proofing.

This goes back to the agility that you're talking about. Today's AI solutions are not tomorrow's AI solutions because the problems are changing on us constantly. So we've got to think about how we deploy infrastructure that's hardware and software that's not infrastructure dependent. Like do I have to have a certain Windows server with this GPU to run this?

And then what are the system integrators that are involved in trying to get that deployed? What are all the approval processes? I mean, this is a high friction problem and this is usually why technology doesn't roll out very fast in any enterprise. But if you can do that independent with an infrastructure independence, now you have a lot more agility because the hardware, the software, the communications infrastructure, it's all engineered to do that. And again, that's an innovation. I don't know if you want to expand on that.

Ryan Andersen:

Yeah, I would just say we've got plenty of capacity to explore and build the new. We're not capacity constrained on that front and that's on purpose. Yeah. See, I think the second thing I would add is the pace of AI is so great. It seems crazy to me to buy a static solution today to address the AI need. You have to adopt a strategy that lets you to explore AI and how AI is going to work in your particular environment. And if you don't have that capacity to do that, you're going to be left behind.

Steve Lindsey:

Yeah. It almost feels like the new car getting driven off the lot and now it's already been devalued.

Ryan Andersen:

It's difficult to innovate on that new car you just drove off a lot.

Steve Lindsey:

The last area I think we want to cover in this innovation is really around agentic AI. And I don't think people fully understand this and we could do a whole nother webinar on agentic AI. But one of the things I think is important for our listeners and viewers to understand is that we've had AI in security for a while. And I think the easiest example to look at is computer vision. So we've had computer vision analytics that do object detection for people and vehicles and maybe we have weapons detection and some other things. But what's so exciting about agentic AI is the fact that it creates what I would consider a computer vision being almost like an if then statement to something that looks completely non-deterministic. And what's really powerful about this is in physical security, you have no clue what's going to come at you.

And so it's hard to have that computer vision analytic created for every possible scenario that you can think of. And so with agentic AI and the ability to transform video into reasoning models that then take action, it really unlocks a new potential. And again, LVT is leading in this area. We've shown some different things around our AI talk down that does this and others, but I think that's another indicator that Frost and Sullivan picked up on.

Ryan Andersen:

Yeah. And today, most of the assembled systems are just completely reliant on the camera vendors that they hope happen to select. If they want Agentic AI, it's going to be a long road to get there. Right.

Steve Lindsey:

Cameras just aren't built to do it.

Ryan Andersen:

Cameras have analytics to do very specific detections.

Steve Lindsey:

Yeah.

Ryan Andersen:

Right.

Steve Lindsey:

Yeah. So anyway, I think that's really why we were on the innovation list. So I guess taking this to another step, maybe we can dive a little bit. Well, I think we've covered Agentic AI. Like I said, we could spend an hour talking about Agentic AI, but don't want to get too wrapped up into that. So yeah, where else

Ryan Andersen:

Should we go? I would just say, Jared, any more questions come in that we should pause on?

‍Jared Richardson:

We can pontificate on this specifically. Another report Steve is titled The Mobile Surveillance on Frost and Sullivan taking a look at that, but really it's about more than just surveillance. Organizations need to consider that into their entire security strategy. So just curious, Steve, what do you think about the evaluation there and where we really need to be looking at?

Steve Lindsey:

Yeah. Okay. Yeah. So there is another topic on Agentic AI. So yeah, let's imagine that a camera now can transform into descriptions or text everything that it sees, right? And now I can feed that into some reasoning models. What's interesting about this is although I may have deployed the camera for life, safety and security, and I'm probably able to do life, safety and security detections and deterrences better than ever before, but I also have this rich set of information that now is probably relevant to the rest of the business. So I've unlocked my camera for a sole purpose of lifesaving security into more of an operational use case.

Ryan Andersen:

Yeah. I mean, I talk about this as being video, being the new substrate for the physical operating system. A substrate is basically what's the bottom layer that you need in order to do things.

And so video, in my belief, is becoming that substrate for how do we do things in the future, which means it requires coverage is one. And then two, it requires understanding. So the models that you talked about, understanding what's happening, and then reasoning to do something about it. And now, I mean, my view is it's going to become more of an IT-like infrastructure, this kind of video layer across your physical environment that's going to be shared across the organization. It's not just going to be the security folks that are going to be consuming that video. It's going to be security, it's going to be operations, it's going to be HR, it's going to be all the organizations in the company that are going to leverage that substrate, that common thread across all of your physical environment. It's helping you make sense and then make innovate as a business.

‍Steve Lindsey:

Yeah. And what's really interesting in this is now we're seeing this intersection where we've had problems with budgets and resource constraints where life safety and security is usually kind of the last budget item it seems like. They get what's left over in some cases, and I'm sure a lot of our listeners feel that way. But when you think about turning the assets that you have of cameras into operational tools, right, it takes it from a cost center to actually a potential revenue generator on the other extreme of this. And so this is an interesting opportunity for professionals in the life, safety and security profession to start looking at their contribution to their businesses as not just being that cost center, but actually driving innovative change and how we can use video to actually operate better.

Ryan Andersen:

Yeah. And just building on that, you've got to have the ability to leverage that video. You've got to have an operating environment for those AI workloads to be in. And I mean, well, to see how the industry evolves, like whether that becomes an MSU-like form factor for that operating environment or if it becomes something else.

Steve Lindsey:

Yeah. Well, and that's what's exciting too, is you talked about how the power of the cloud really enables a lot of this stuff, right? Moving from on- prem solutions to a cloud-based solution, but I think people who think about that think, well, everything then has to move to the cloud, right? But it just doesn't work because there's bandwidth issues, there's data transmission costs, and you can't just push everything into the camera because there's too much power and there's enough compute there. So really the problem has to be able to live across the entire infrastructure, right? Cameras, edge compute, cloud compute, and how you're structuring that data and aggregating and then putting the intelligence where it needs to be. I mean, it's a very sophisticated pipeline, let's call it, that has to be engineered to do that. And I can't think of a single existing physical security technology that was built to do that, right?

I mean- I can't

Ryan Andersen:

Think of any.

Steve Lindsey:

Yeah, it's not there. So again, we're talking about these really interesting intersections that we're seeing where mobility, where the agentic capabilities, where cutting the cord independent, infrastructure independence, like this is a really exciting time to be in this space because it's so dynamic in what's possible and the problems that can be solved are so broad and we can transform from just a cost center to actually potentially being a revenue generator. And then the ability to force multiply the scarce human resources that we have all at the same time is actually kind of mind belt- Super exciting. Yeah. Very, very exciting. So yeah, Agentic AI is big.

Jared Richardson:

One of the things I wanted to touch on real quick too, Steve, as you're talking about data and as we've seen some really public examples recently about how not protecting data, not safeguarding these systems has eroded public trust. So really quickly, I just wanted to get your thoughts on kind of like the ethics and data privacy as it pertains to what vendors are doing in the mobile security space.

Steve Lindsey:

Yeah. So when we talk about cybersecurity, there's obviously the security of the infrastructure, which we doubled down on, but yeah, there's the data privacy itself. And I think privacy has a lot of topics. I don't know if you want to start with any ideas on that, or I could too.

Ryan Andersen:

Yeah. I mean, I can just tell you what I see in the market. I see vendors that make it super easy to share data that they might be collecting with others and because of the ease of that, it has created all kinds of problems in the public and public perceptions. So the onus is on us as an industry to kind of correct that and make the potential implications of clicking that button to share the data very evident to the person who's going to be clicking it so that doesn't get out of hand like it has for some

Steve Lindsey:

Of our connections. And there's almost two parts of that too. There's the actual data collected in who you're giving it to that isn't the customer. And then there's this concern around using data to train models, right? So there's two concerns under there, but yeah, continue on. Oh, that's all I had on that. Okay.

That's all I had on that one. Yeah. Yeah. So when we think about data privacy, there's this data sovereignty question and that is like, who owns the data? And I think most vendors in the tech world would want to own the data. I think when you look at the business side of these vendors, there's definitely a strength in what's called the network effect, right? The problem though is that network effect then puts in question, well, who owns the data then? Because if you can give that data to any other customer, I no longer control my data. And I think that's been tested as of late on those business models. I think to really do this ethically, there's a couple things that have to be true. First of all, the customer has to own the data at all times. In other words, who has access to the data, including within the vendor themselves?

Does the vendor's employees have access to data?

Does the vendor have to ask permission to use that data at any time, even including training their AI and machine language models? And then when we think about even the concerns from the surveillance state of privacy, we're seeing this as well, where public outrage starts to happen when they feel like their civil liberties are being impacted. And so you see technologies typically around anything that can reveal an identity of a person being a concern. I think all of these things fit in data privacy. And I think it's the responsibility of the vendors to have an ethical approach to this. Not everything is about making money. We have to put the customer and the industry and the general public, their interests at large in this and try to find solutions to do this. And I think one way that LVT looks at this, and if you have thoughts here, one of the things that we focused on when we started doing our detections was we thought instead of trying to identify the person or using technologies that could, can identification really help detect a threat?

Well, possibly you could say that a repeat offender could be that. And I think-

Ryan Andersen:

On the investigation

‍Steve Lindsey:

Side, maybe. Yeah. Yeah. And I've seen some use cases where people want to say, "Well, even from an LPR perspective or a facial matching perspective, if I see this person come back again, I want to know. " I've heard that use case, and I think that's an interesting one that we've got to really look at and analyze how to do that ethically. But end of the day, more than anything, on a detection, it's really behavior, right? I don't care who did it. I don't care what gender, what race, any of these biases that are out there, I just care about the activity being done. And when I see that behavior, that's what I want to try to deter before they actually do something.

‍Ryan Andersen:

Which is a harder problem to solve

Steve Lindsey:

Than an OCR

‍Ryan Andersen:

Picking up a license plate or a face recognition, which has been around for 20

Steve Lindsey:

Years. Right. But the exciting thing is the agentic technologies can now- Enabling it. ... do that. Yeah. It's enabling that. So yeah, I think data privacy, very much connected with cybersecurity, very much connected with ethics. There's a whole bunch of stuff wrapped around all of that.

Jared Richardson:

Great. I want to move ahead since we only have about 15 minutes left in this webinar. There are a few topics I definitely want to touch on. One of the important criteria that Frost and Silverman brings out is customer input and innovation. So I want to start with Ryan here as somebody that has looked at the entire market, and maybe Steve, we could bounce on this and talk about how LBT is taking what our customers are saying and baking that back into the product.

Ryan Andersen:

Yeah. I mean, there are a couple of ways to run your innovation strategy. One is to be customer led, another one's going to be technology led, or you've just got hypotheses and you're going to go test them. And we're going to do both. I mean, you can definitely elaborate on that in terms of where the innovation's going to come from. And Jared, I don't have the full market view. I mean, I sample the market, to be fair. But looking at that sampling that we have, we do listen actively to our customers to understand not what they're asking for per se, but what we believe their needs are after talking with them.

‍Jared Richardson:

We hear from people that have an existing investment that they really love to leverage and use, right? And I think one of the things we like to talk about at LVT is kind of like our open ecosystem.

‍Steve Lindsey:

Yeah. Yeah. And I was just going to kind of elaborate on that. When we think about innovation, not everything can be solved by the same vendor. I mean, there's a lot of things that we wish we could do and be the end all be all for everyone at LVT, but we know what we do really well and we also know what probably partners do really well. And so yeah, we've always been interested in an open ecosystem for that very reason. So why create new beachhead that's already been created? Why not partner with the best in class that can pull those things off? So what this requires is the ability to work with mutual customers, understand that data sovereignty and where it lives, what does a customer need to happen as data moves between systems, the willingness of vendors to actually move data between systems if the customer so chooses to do so.

And again, leverage each other's strengths to provide that force multiplier of a much better outcome for our mutual customer. I love that. I like that. Yeah.

‍Ryan Andersen:

Yeah. I mean, when you think about innovation, like sometimes you need to just go do something on your own because the rest of the ecosystem isn't moving fast enough or there's something you want to do and try. And so you might need to do that on your own. And most of the time you're going to deliver those innovations though through a partner.

‍Steve Lindsey:

Right.

Ryan Andersen:

Right?

Steve Lindsey:

Yeah.

Ryan Andersen:

So I totally agree and love that.

‍Steve Lindsey:

Yeah. And another interesting angle on this too that we see, and this is what we've seen traditionally in software development. I've been developing enterprise software for 30 plus years. A common thread through all these decades has been that even the customer creates value within this ecosystem too. So having a platform where the customer can add their bespoke like customization into that is also very critical into the ecosystem. So obviously vendors need to work together, but then the customer also might want to be a part of that outcome and the tech needed to pull that off.

Ryan Andersen:

Yeah. We definitely hear from our customers about customizations they want of our platform for their particular use case, whether that be retail or in the rail systems. They love the platform and they want to be able to innovate on top of it, which means we need to enable them to customize the workflows, the data, and be able to manipulate elements of it while still delivering it as a service in a highly reliable way.

‍Steve Lindsey:

Right. And when we think about this new vision that we have at LVT, we call intelligence site management, which is kind of this agentic AI idea where life safety and security is at the heart of it, but we can expand the value proposition to the rest of the business. When we start thinking about what has to be true for a solution like that to work, an ecosystem and the ability to do these things is absolutely imperative because there's going to be so many things from a hardware and a software and an AI models. And there's so many components that can be brought together in a way to solve the problems and get the insights and the actionable intelligence that's needed. Yeah.

Ryan Andersen:

Yeah. And we need to enable our customers to be able to do that,

Steve Lindsey:

To

Ryan Andersen:

Pull all those elements together.

Steve Lindsey:

Yeah.

‍Ryan Andersen:

Yeah.

Steve Lindsey:

And so that's really what our moonshot vision is now for the next couple of years, is to try to continue to deliver on that vision. So yeah, Jared, I think we have what, 10 minutes left here. I don't know if we have Q&A time or what you want to do.

‍Jared Richardson:

We do have a number of questions coming in. A lot of them are product focused, but I think you guys can definitely tackle it. I want to revisit a question that Scott asked really early in this presentation. He wants to know, does LVT currently or in the near future have a system geared towards indoor installation, maybe in a manufacturing or warehouse application?

Steve Lindsey:

Yeah, great point. When we talk about intelligent site management, it's made up of three pillars. The first pillar is what we call coverage. The second pillar is what we call intelligence, and the third pillar we call ecosystem. And I think we've kind of touched a little bit on all of those. But when you think about coverage, what coverage is, is the vehicle that gets information into the intelligence and also can take action if the intelligence wants something to be done, right? And so we talked about this a little bit when we talk about prevention, right? We detect, we analyze, and then we can do an automated deterrence all through an agentic experience. Well, I can do that in other scenarios as well. And so the question is, is where do I have the infrastructure in place to do that? So obviously MSUs, popular, we've got pole mounts and building mounts.

Again, same platform infrastructure that can do that, but yeah, that can easily move indoors. Now, we're not trying to be another VMS, we're not trying to do anything that already exists. That's beachhead that already exists. What we're trying to do is take that intelligence layer and make it accessible to those sensors and those things that can be done on the indoors as well, right? That's the vision.

‍Ryan Andersen:

Yeah. And I mean, our design philosophy has always been, we want to be infrastructure independent. And as you move indoors, you've got to really think about, am I going to start leveraging and being locked into a particular network and working with IT and going through all of their

Steve Lindsey:

Processes? All the red tape.

Ryan Andersen:

Or are we going to keep a design point that allows us to operate beyond and above that? Give the customer agility and nimbleness.

Steve Lindsey:

Right. Yeah. The infrastructure independence for anything moving indoors is just as important as anything that we've been doing outdoors, right? Because the rapid deployability, the agility, the low friction with IT is absolutely critical.

Jared Richardson:

Another question from Dan here, and he's curious if you guys are aware of any incidents where LBT has helped reduce property insurance rates.

Ryan Andersen:

Yes. I think, yeah, you would know better than I do on particular customers.

Steve Lindsey:

Yeah. I mean, I think there's a way to look at this, right? When you have numbers where ... In fact, I just recently did an interview with a customer down in Austin, 76% reduction in vehicle break-ins in a public area. If you're consistently showing those levels of prevention, your insurance rates start dropping, right? I mean, it's just going to happen. So that's the beauty of it. But again, let's go back again. You have to run an active deterrence and prevention program. You can't put up a scarecrow because it will start to fade over time.

Ryan Andersen:

After one month, the numbers go back

Steve Lindsey:

To where- They start going back to where they were. You can't put in a motion light because again, it might take a little longer than a scarecrow, but they will come back. So the important thing here is you have to be able to have that dynamic nature to the prevention. And then again, the accountability has to be there. So you have to be able to prosecute people as well to hold them accountable so that they then feed off the fact that, oh, I could get caught. I mean, I can get caught has to be on top of their mind at all times. So yeah, that results then in these lower costs. If you can prevent, then insurance companies feel that there's less risk to actually do that.

Jared Richardson:

Maybe our last question before we end this session, we've seen a few comments regarding the human capital and physical security when we're talking about guards. So Ryan and Steve, I'm wondering if there's a point on how we enable guards, how we make physical security more cost effective rather than relying solely on human guards, if you have any points there.

Ryan Andersen:

Well, I would say everything we do is meant to scale the human guard outcome or scale the human guard. We want those guards or those that are responsible for physical security in any environment to be superhuman and be able to see around corners and be able to deter criminals without risking their lives.

Steve Lindsey:

Yeah. Yeah. In fact, when we think about the jobs to be done, remember the very middle one right after bang is respond.

And again, that's the guard. What we're trying to say here is that a guard may be effective for detection and some of that prevention, but that doesn't scale very much, right? But what does scale is if I can use technology to prevent and then let the guard know when we've done everything we can with deterrence, which often gets rid of upwards of 70% of the noise, right? 70% of the noise can go away through just technology automation, understanding that prevention loop that can be there. Now, that doesn't mean that 100% gets prevented, that means that that guard now can deal with those instances, right? So instead of having the guard have alert fatigue and missing really what's critical for them to be at, the guard actually gets informed when they are actually needed to be involved because the technology's dealt with all the noise.

Ryan Andersen:

Yeah. And we see ratios now. I mean, it used to be one guard to one location, then you put cameras out there and suddenly you had like one guard watching a wall of cameras and you had one to maybe 20 or 12 cameras. And then the AI or at least detection started to come in and then you were only looking at alerts and you got maybe one to 50. And now with AI, you're getting one to 200, that one guard can be in 200 different locations at the same time.

Jared Richardson:

Yeah,

Steve Lindsey:

Definitely a scaling force multiplier.

‍Jared Richardson:

Excellent. Well, thank you, Ryan. Thanks, Steve. And thanks everybody for watching, attending, and especially for all your great questions. If you have any other questions for us at LVT, you can email us at sales@lvt.com and one of our reps will follow up with you right away. Also, watch your inboxes for the recording of this webinar. It'll be coming as soon as we can process it. And thank you again for attending today. We'll also be sure to include a link to the full Frost & Sullivan webinar in that communication with you as well. Have a great day and thank you so much for attending.

Ryan Andersen:

Thanks everybody. Thank you.