Episode Transcript
[00:00:02] Speaker A: I'm Alex Stone, former military service member and law enforcement officer, now CEO of Echelon Protected Services, one of the fastest growing private security firms on the west coast. And this is ride along, where our guest and I witness firsthand the issues affecting our community.
I believe our proven method of enacting meaningful change through compassion and understanding is the best way to make our streets a safer place and truly achieve security through the community.
Alex Stone here. Welcome back to the ride along. Today we have a fantastic guest, Adam Schneider. He is a personal friend, business partner, and current COO, chief operations officer of Sentinel Overwatch Services. Adam, introduce yourself to the folks.
[00:01:00] Speaker B: Thank you, Alex. Thank you for having me on today. I'm really excited.
Yeah. My name is Adam Schneider. I'm currently the chief operations officer for Sentinel Overwatch Services, which is a technology company.
We provide software to small to medium sized gardening companies and a lot of other companies in the security market. I'm a Portland native. I started my first business here when I was 18. I started in the fitness industry because helping people I was really passionate about. And so I drove that passion all the way up north to Canada, where I started a medical company. And we manufactured and wholesale medical STEM machines.
[00:01:39] Speaker A: Medical devices, right?
[00:01:41] Speaker B: Yeah, yeah. For muscle rehabilitation and strength training.
[00:01:44] Speaker A: Fantastic. Actually, my father in law loves it.
[00:01:47] Speaker B: Mm hmm. And after the, after the pandemic, I came back to Portland and just kind of saw the decline in the houseless community.
[00:01:55] Speaker A: Help. So when did you leave Portland?
[00:01:57] Speaker B: I left Portland in 2015.
[00:02:00] Speaker A: 2015. And then you came back 2019.
So that was four and a half, five years.
[00:02:06] Speaker B: Right.
[00:02:07] Speaker A: What was the, what was the number one thing that you felt changed the most?
[00:02:13] Speaker B: The community.
The houseless, especially. I mean, when I first came back into Portland, I remember it because it had been so long since I've been here. It was so nostalgic. Coming over the Fremont Bridge, coming right into downtown right at sunset. I think it was about 715.
And I popped off down on Burnside because I like to come up Burnside, and it was just homeless people all throughout. All throughout Burnside. And it was really, really saddening. I had to pull over and I just kind of looked at everything. I just had to take it in because I'd never seen tents on the sidewalk here. And I grew up, I grew up in Portland coming down to the Rose festival every year. And so at that point, I wasn't sure what I was going to do. When I was moving back from Canada, I was kind of in limbo, and I was looking for my next opportunity.
[00:03:01] Speaker A: You had just sold your company. Right? Is that.
[00:03:03] Speaker B: Yeah, I had. I had resigned as VP up in Canada and had sold back my shares and was looking for new investment opportunities and just kind of taking some time to myself.
And then I was just hit with this whirlwind of houselessness in my community, and I was shocked. And it was something that, you know, I came from a fitness background, wanting to help people, and I just started looking at different ways that I could be involved in the community, different companies that were leveraging, you know, community outreach and how I could be affiliated with that, and then looking at, you know, market trends and technology. And that's kind of how I transitioned into the position I'm in now.
[00:03:41] Speaker A: Yeah. We were introduced by a mutual friend who is also our partner.
[00:03:45] Speaker B: Mm hmm.
[00:03:46] Speaker A: And he essentially said, hey. He said, hey, you got to meet this guy. Super smart, really intelligent, one of the best sales guys I've ever met. And I was like, yeah, set a meeting. And I think you just came over the house one day.
[00:04:00] Speaker B: Yeah, yeah, we had a. We had a five minute phone call, and I ended up coming over to your house.
[00:04:05] Speaker A: We went out for, like, four or 5 hours. I think we watched maybe the UFC fights, probably. Yep. That's usually my. The way I introduce people into my. My life, my circle.
[00:04:12] Speaker B: Yeah, right.
[00:04:13] Speaker A: We turn on UFC, and so, you know, over a couple of fight cards, we, you know, basically, I thought to myself, I really clicked. I was like, man, this guy really gets it right, like, he's super intelligent. Johnny on the spot. And almost, I basically. Almost like, the next day we were working together, essentially.
[00:04:35] Speaker B: Yeah, it was super quick, man. It was very organic, very natural, and the flow just transpired into us strategically working and looking at avenues that we could apply to the market.
[00:04:45] Speaker A: Yeah, we wanted to go deep into tech, and I was like, hey, what do you know about the security industry? And you're like, not much, right? Yeah. You're in medical sales. And I said, well, let's do a deep dive. So you stayed with us for about a year while we're researching and finding out which partners we're going to go with the tech we're going to develop. Right. And we have, you know, Yuri Sernande, our partner. Great guy. He's one of our awesome. He has a development team, and.
And so quickly cascaded into here we are, like, a couple of years later. Right. And tell us exactly what this tech is and how it's really serving the industry.
[00:05:26] Speaker B: All right, so our tech is protected by patents, so we own the patents, and we have deep learning algorithms and continuous model optimization. So we run all of our machine learning, all of our video analytics, our AI analytics, through deep learning, and then we basically ingest all of this data. We run it through deep learning algorithms, and then we push that data back out. And where that data goes, specifically in the security industry, is to security guards. And so what we're doing is we're utilizing technology to drive a quicker response so we can get on site and allow the guarding company to provide a service to their end users. So the property managers, the property owners, clearing out the house's people, stopping intrusions, that kind of thing.
[00:06:13] Speaker A: So to be more succinct, we're using AI that deep learning in order to notify a direct action asset in the field so they can get to a scene or an incident quicker. Right. And so what we identified immediately what's wrong, not only with. With the security industry, but all emergency services, is that the main struggle really isn't the incident. It's really the time it takes to get to the incident. Right.
Something very similar happened, like, in the fifties and sixties, fire departments realized that they're becoming obsolete because of these amazing commercial suppression fire suppression systems. And so to be more relevant, they started bringing the medics out of the hospitals into the firehouse, and every firefighter became an EMT emergency medical technician. And what that did was it put the EMT closer to the incident. So if there's a heart attack in a small neighborhood or a small town, and the hospital's an hour or 2 hours away, you no longer had to go without that emergency medical care on the way to the hospital. Right. You received it and in the ambulance from the firefighters or the EMTs. And so you're getting closer to the incident. Right. And so, in a sense, this kind of technology, we're not getting the actual physical assets closer by relocating them from a centralized location to a decentralized location. But we're getting them closer by cutting in half the cycle time. Right. That amount of time it takes to notify a dispatch center and then get that asset en route. Right. We're taking away that notification process, making it immediate within 1 second.
So, you know, this has been an amazing journey. Tell us about some of the success stories that have come just from deploying the tech here in Portland.
[00:08:07] Speaker B: Yeah. So, I mean, there's so many.
We've deployed on dozens of properties in Portland for a security company, Pacific echelon. So we've worked really closely with them. Yes. I think that's why we're true story.
Yeah. So we've deployed on dozens of properties downtown. One property in particular is located in the Pearl district. When we first deployed, we were getting about ten to twelve incidents a day of drug use, sex related crimes. It was in a parking garage hidden in kind of a corner area.
[00:08:43] Speaker A: Attempted break ins. Break ins of vehicles. Yep, yep.
[00:08:46] Speaker B: All that stuff.
[00:08:47] Speaker A: So ten to twelve crimes occurring.
[00:08:48] Speaker B: Yeah.
[00:08:49] Speaker A: Per day legit. Per day legit incident, yes. Something that a police officer would typically handle.
[00:08:54] Speaker B: Yeah. In any other state other than Portland or in Oregon, police would be responding to these types of crimes.
[00:09:00] Speaker A: Even in Puerto Rico and Guam?
[00:09:02] Speaker B: Yes, yes, definitely. And so when we deployed our AI the first 30 days, we were at about a 96% incident rating.
[00:09:12] Speaker A: What does that mean?
[00:09:14] Speaker B: It means that 96% of the time there's an incident occurring within the lens of that camera.
[00:09:20] Speaker A: Yeah.
[00:09:21] Speaker B: So whether it's a car break in, it's drug use, it's sex related crimes, prostitution, just general loitering, sleeping. Something was taking place 96% of the.
[00:09:32] Speaker A: Time, and the AI was detecting it and saying, hey, you have a real incident going on here.
[00:09:36] Speaker B: Yes. And then that data gets pushed to an officer. Officer then comes and responds and clears out the issue.
[00:09:42] Speaker A: So why not live monitoring? A lot. A lot of these legacy systems, they have, like, monitoring centers in the Philippines or, you know, somewhere overseas, and then they look at the video. Why not go with the system like that?
[00:09:56] Speaker B: Yeah, it's a great question. And, yeah, I think seven years ago it was. It was the system to go with. Right. And like you said, perfectly, it's more of a legacy system now in the sense that criminals know what they're doing. Right. You and I are good at our jobs because we do market research. We spend a lot of time doing this. We train and criminals do the same thing, man. This is their livelihood. This is how they make money. And they get smart. And when you are in front of a camera committing a crime and somebody voices down and says, hey, you in the black shirt, I'm calling security or I'm going to notify the police, they look up and they say, okay, great. And they just leave the camera view and they sit across the street. They wait, and then 15 minutes later, nobody shows up because the person monitoring the camera watched the person walk away.
[00:10:40] Speaker A: They watched them walk away.
[00:10:41] Speaker B: Yeah, exactly.
[00:10:41] Speaker A: From their point of view.
[00:10:43] Speaker B: And so nobody gets called. Now, the criminal knows. I've got 15 minutes, ten minutes at least. And the average crime takes about seven minutes. I think you'd know better than I would.
[00:10:53] Speaker A: But, yeah, that's right. That's right.
[00:10:54] Speaker B: Yeah.
[00:10:54] Speaker A: So about seven average residential, commercial break in, you could break into 20 vehicles in seven minutes easily. That's very easy.
[00:11:02] Speaker B: Yeah. Yeah.
[00:11:03] Speaker A: Definitely get a catalytic converter in seven minutes. Probably three or four minutes.
[00:11:06] Speaker B: Yeah. So that. So that's a big. That's a big hole in. In these kind of legacy models that you're talking about. That. And then also the data goes overseas or out of state, generally to a live monitor. And for, at least in the state of Oregon, in order for a security officer to arrest as a civil person, they have to witness the crime taking place.
[00:11:27] Speaker A: Yeah. In cops speak or in legalese, we would call that 100% probable cause, meaning that we have probable cause to believe a crime occurred and we have 100% believability because we actually witnessed that crime occurring.
[00:11:43] Speaker B: Yeah. And a live monitor can't instruct an officer to make an arrest based on what they're witnessing, especially out of state.
[00:11:51] Speaker A: So that person watching the video camera can't transfer the probable cause. Right. From themselves. And let's say they're in Costa Rica, they can't just tell the guard, oh, I witnessed this. Right, that probable cause statement isn't transferable.
[00:12:08] Speaker B: Right, correct. And so what our technology does is it actually puts all of this information in the officer's hand. So when an incident's triggered, an officer receives a text message and knows exactly the site and the location and what incident's taking place. They also get a 32nd clip so they can see what triggered that incident. And then for their safety and for others, they can go to a live view. And what that does is while they're in route, responding, they can monitor the camera live, which allows them, if they need, to apprehend the individual on site. So if it was a loitering that then escalates to an intrusion, and they're monitoring while they're in route, they now have the jurisdiction to place the individual under arrest.
[00:12:48] Speaker A: So the guard gets. The guard gets the notification. They pull up a live view of the property, and they're like, oh, my gosh, there's a guy inside of a vehicle with a broken window. So right there, you have trespassing.
[00:13:01] Speaker B: Right.
[00:13:02] Speaker A: And then you have entering that vehicle illegally, which can be called many different things in different jurisdictions. And so they're developing that probable cause on that live video as they're in route. Yep. Right. Okay.
[00:13:14] Speaker B: And they can also make the determination whether or not they need to call law enforcement and or if they need backup. Right. Oh, so it gives them the ability.
[00:13:21] Speaker A: And knows the location of the suspect.
[00:13:23] Speaker B: Mm hmm.
[00:13:24] Speaker A: Because you're like, oh, third floor, northeast corner. Check. Okay. Boom. And then no other notifications have come through. Right. So you know that that suspect is alone. They're not with another person. They don't have a lookout. There's not another person in the building with a crowbar because we do object verification and all these things. So, you know, hey, I'm not outnumbered. I got one person. I know where they are. So you have that entire information set to work with, right?
[00:13:50] Speaker B: Yeah.
[00:13:51] Speaker A: That's really amazing, actually.
[00:13:53] Speaker B: Yeah. So it really sets us apart from people who you would consider competitors in the industry. Nobody's utilizing artificial intelligence as a direct action with advanced deep learning.
[00:14:04] Speaker A: Exactly. I love the way you say that. You know, something that I realized coming into this, because I came. I was a gorilla with a gun. That's what, you know, I call a police officer and so. Or a grunt. Right. And so coming in from that side, having to learn a lot about tech, I was shocked to find out that around 95% to 98% of all video feeds internationally are unmonitored.
And it's like, what's the point of having a video camera at that point? You know what I mean?
[00:14:36] Speaker B: Yeah. Well, I think this. These camera systems, right. Were put in with a. The mindset of recovery. Let's put in. Let's put in cameras. We can recover, you know, data if someone breaks a car window. Back when law enforcement was doing their job because they weren't overran by criminal activity, because of the way politics have shifted cameras.
Yeah. I mean, cameras were really beneficial. Right? Someone break into your store, you could pull the video, you could submit it to the police, and then they would assign somebody and they, you know, get an arrest and conviction and hopefully get some money back. Right. However, these days, that's not happening. And so our software takes this reactive system and turns it more into a proactive system. I love it.
[00:15:17] Speaker A: So let's. Let's go deep, deeper into the deep learning, right? So let's talk about how deep this deep learning is. So currently in the news, there are critical incidences, mass casualty events, right. Things, active threats that occur on a daily basis. And so how can we leverage the deep learning to stop these incidents? Right? Like, for an example, it's very. It's becoming very popular to, you know, it used to be a flash mob was when people would go to the mall and dance all at the same time.
[00:15:50] Speaker B: Right?
[00:15:51] Speaker A: But now flash mobbing is used as a active threat to literally steal everything from an entire store. Right. This is happening in major cities all across America. They're leveraging technology in order to get an entire mob to show up. 100 plus people to show up who necessarily don't even know each other. They're just all using the same messaging device. Right. And they're gonna all show up at, let's say, a Nordstrom's, and then they're gonna ransack and riot and steal everything. So is it possible for the deep learning to be aware of crowds and mobs? And. And how is that. How are we going to leverage that to, like, shut a location down to stop that mass entry of those individuals?
[00:16:41] Speaker B: You bring up a good point. Right. It's a. It's a very challenging thing that we face. But utilizing deep learning, we can do a lot of manual model training. And part of that is taking footage, which. There's a ton of footage of these flash mobs.
[00:16:53] Speaker A: Yeah, yeah.
[00:16:54] Speaker B: And we can run it through our deep learning algorithm. And then what we can do is we can pinpoint all of the different people who are instigating that, whether it's by heart rate, by behavioral analytics, and so we can apply these trends. And then when we deploy our software on, say, in Nordstrom's and they have an exterior camera, if we get 1020 people that are moving at a certain rate, that have a lot of these behavioral identifiers that we've assigned from our deep learning analytics, we can actually force the store to go into lockdown or at least notify an individual or representative of the store that this is coming real time, and they can make the decision to lock it down. Right. Because what we don't want to do and what we've seen in MCI is mass causality. Incidences, is people like Best Buy, where they put a half a second delay on that door, on their automatic doors, and as you're walking, you'll never know that it is. But if you start running, the door doesn't open. Right. Because it senses that you're running, and that's to stop from loss prevention. Right. Well, what happens is, in a mass casualty incident where there's a shooting, people are trying to run through that door, and the door's not opening. It became a big liability. And so now these big box retailers are getting sued behind the doors for these types of security measures that they took. Where, with our video analytics and the deep learning, if we deploy this software on the exterior of the Nordstrom's and we get ten to 15 people, the guards are able to verify, like, this is absolutely a flash mob. Right. Our faces are all covered, and we can put identifiers on these mobs, right. If they're wearing a face mask, if they're wearing sunglasses.
[00:18:29] Speaker A: So it's fully contextualized. You're not just looking at how many people, but you're looking at behavioral analysis. Are they indexing? They have a weapon. Is their face covered? Right. What about facial recognition? What if you have known suspects?
[00:18:44] Speaker B: So facial recognition is tricky. It's not as cut out in the movies as they make it seem. Right. But facial recognition is definitely doable. But again, you said the word perfectly. It's contextual. Right. So we take different markers along with the facial photo. So if you have a list of people that you've trespassed or a bolo, be on the lookout. You can upload this into our deep learning algorithm, and it'll study all the characteristics. The issue is storage. Right. In order to store more than, you know, 15 or 20 photos, you need a lot of storage, compatibility, and you need it in the cloud because you need that data real time. And then you have to work around masks and beards and so facial.
[00:19:28] Speaker A: Facial recognition, sunglasses, glasses.
[00:19:30] Speaker B: Right. It is highly accurate, but there's a lot of things that can cause it to not be super accurate. And that's where we use contextual analytics and we use body language indexing, other key features of the individual that has been trespassed or we're on the lookout for, and we can apply all of that. So it's facial recognition with body indexing, behavioral analytics, object verification, and that kind of a thing. So we couple all of that to get more accurate readings.
[00:19:59] Speaker A: Now, are we just applying video? Is this just video analytics solutions? Or, you know, because there's the whole idea of video, but there's all these other things out there other than video.
[00:20:09] Speaker B: Mm hmm. Yeah, there's. There's a lot of sensors. There's a lot of data that.
[00:20:13] Speaker A: I don't want you to give away the secret sauce.
[00:20:14] Speaker B: But we won't. Yeah, yeah, I'll keep that. We can't have our viewers sign NDA's. So, contextual analytics, we can apply our vast system to things like Lidar or SAR or IR radar, and what that allows us to do is to get more accurate readings from further away. So if we couple our video analytics with Lidar and we set a contextual analytic, and we say, you know, if a vehicle is moving at x amount of speed in this zone, and there's people in this zone as well, so.
[00:20:47] Speaker A: Let'S say there's an hypothetically, we have an embassy overseas, and we wouldn't want a speeding vehicle to be traveling at a certain rate towards that embassy. Yep. Right. And so you could use Lidar or radar along with video.
Right. And other analytics other than just video, and combine them together.
[00:21:07] Speaker B: Yep, exactly.
[00:21:09] Speaker A: Okay.
[00:21:09] Speaker B: And it gives us real time, more accurate data, and we can be sure that that would be a potential threat coming towards us.
And again, just coupling these other sensors and technologies together into our vast will allow us to be able to produce those results.
[00:21:23] Speaker A: What about, like, echolocation or sounds?
[00:21:27] Speaker B: Yeah, we do a lot of gunshot detection. That's really the area that we're focusing in right now, is gunshot detection, being able to decipher how far away it is if it's a potential immediate threat.
And so, you know, we can deploy different sensors on rooftops, which is non video related, and then we can also have our vast suspension running with object verification for firearms and everything else in the area.
[00:21:51] Speaker A: So. And then, so you. Let's say you have. You have a complex that's overseas. Let's just take a movie, like 13 hours in Benghazi. Okay, great movie. Right? So they're attacked. They're in an annex. Right? So, not a technical embassy. They're in an annex. And let's say you got gunshots on the northeast corner of that building. Right. And then maybe you had some video of crowds showing up on maybe the northeast or northwest side of the corner. So you can take all these things, and then possible, maybe an inordinate amount of traffic with vehicles, or maybe vehicles are blocking streets several blocks away. So all that would come down to one single pane of information that the. The sound. Right. Echolocation and sound information, lidar, radar, all these things would come together, and then they would be understood within the context of a larger meaning, not just individual. Right. And so is this going on right now? Like, is this something that's deployable?
[00:23:04] Speaker B: Yeah, we're actively deploying this and working on these different methods. Currently, we're working with a group out of Korea.
We're working with their technology and their sensors that couples with our vas. So, essentially, when we get our behavioral analytics coupled with our contextual analytics for firearm detection, we can force lockdown a building or an embassy or a church or a school.
[00:23:29] Speaker A: This is what matters, I think, to Americans. And I think you just hit the nail on the head.
Are we going to be at a place where we can, you know, with a high probability, stop a school shooting? I mean, that. Because that's where we need to be as a safe society.
Right.
[00:23:47] Speaker B: That's my goal every day, is pushing our R and D team to develop and to harden our video analytic solutions for object verification and behavioral indexing, because these are what we need to do model trainings on to get this accurate data. I have a daughter. She's two. I've got another one on the way in October and thinking about putting them into public schools. With the lack of policing that we have right now, I need to provide a solution for them. I want to feel safe that my kids are going to school, and I don't want armed guards standing at the doors. I don't want them to have to walk through metal detectors.
I want them to be able to go to school the way I went to school, which just carefree, you know, and not have to worry about something like this. And so we're close. I think that we're further ahead than anybody else in the game.
And being able to deploy this, it's definitely a top priority.
[00:24:46] Speaker A: Amazing. Well, we could go on and on about this, but what, do you see the future of this? You know, people always ask me, because they know I'm in AI, I'm working with you and all these other groups, and they say, what's the future of this? Like, is it Skynet? Are we headed, like, is that the future of AI? And what are the safety mechanisms around AI to make sure that we're always gonna be in control?
[00:25:18] Speaker B: That's a hard question, right?
[00:25:20] Speaker A: I mean, it is a hard question.
[00:25:22] Speaker B: We developed a technology, but we also give it the ability to develop itself. Right, which is what these deep learning algorithms are doing. And they're catching things that we can't catch or that we miss, or it takes us longer to comprehend. So I think that if you have a strategic developer that has the wrong mindset, they could essentially drive this technology to be uncontrollable. But our R and D team, the team that we work with, are very ethical people, and we have a strong. A strong path that we're going down, and that's to provide a solution to the community, to make them feel safer. And so I don't see us getting to a Skynet point, but we definitely have the ability to run video analytics on pretty much everything. You know, we're on drones. We can. We can install them on moving vehicles, you know, for collision ratings. I mean, we can do a lot, but I don't think we're at a point where it's gonna get away from us right now.
[00:26:23] Speaker A: So it's kind of like all technology, you're always gonna have those bad actors, right?
[00:26:28] Speaker B: Absolutely.
[00:26:28] Speaker A: Those rogue nations that will use technology for power. Right. So we've been working hard. Right. The develop the dev team, you, everyone at the company, I mean, seriously working so much. And we developed this awesome product. It continues to get developed. Right. Every use case is different. Kind of talk about the go to market strategy. Why are we choosing? I mean, we don't just go to guarding companies and like alarm companies, but explain the difference between, you know, our work with larger integrators and enterprise versus that more decentralized platform and why we think that was important.
[00:27:09] Speaker B: Yeah. So, you know, my go to market strategy is that we have the ability to make a direct change in the community, which is where I spend most of my time.
[00:27:19] Speaker A: Right?
[00:27:20] Speaker B: Yep.
Our vast can be deployed in every industry that has cameras. Right. We're deployed for product management and heat mapping in grocery stores. But the biggest rollout strategy for me was safety in the community. But as well as a small business background. Right. I want to give these small to medium sized garden companies the ability to make more money and provide a better service. And so we do that by giving them the best technology on the market. I'm very confident in saying that we have probably the best analytic developers that you can get on the market right now. And so by putting this technology into the hands of these small to medium sized garden companies, it allows them to provide a better service as well as drive RMR up for their business. You don't meet a lot of technology companies that approach you and say, I can drive and increase your RMR by 25% in less than a year across your current portfolio. Yeah. Without having to find new clients. Just going to your existing clients and saying, I've got a vast that can be deployed on your property that will efficiently drive my guards to your, to your site faster than you could even call is huge.
[00:28:30] Speaker A: Yeah.
[00:28:31] Speaker B: I mean, it's a huge value proposition.
[00:28:32] Speaker A: In fact, one of my as echelon, one of my main clients always asked me why should I even have to call?
And that was one of the key influencing statements that really drove me into the tech area, you know, and it's so true. You know, one of the things kind of being on this journey with you, one of the things I realized was that, and I didn't know this until I dug deep into the security industry. But 80% of guarding, so 80% of all security contracts in America are small to medium sized companies.
And for me, if we were only working with large integrators if we were only doing these DoD contracts, right, if we were only working with these huge enterprise companies, sure, we could protect 20% of America, but the other 80% is going unprotected. And a lot of that, a lot of those third places are churches, schools, right? Stores, malls, hospitals, hospitals. And so, you know, you have these, maybe you have a company with maybe 100, 200 guards, and they're protecting, they're taking a contract and they're protecting a grocery store. And is that grocery store going to be safe? You know, and our, and what we kind of, we talked about this and we said we need, we really need to decentralize this platform. We don't need this just for larger integrators that want to keep America safe and want to use it maybe for, you know, different types of analytics, maybe for shipping or, you know, shipping lanes or. But really we want to use it. We want it to be able to make the spaces we live in every day safe, right? And for me, the only way to do that was to decentralize is to be able to go to a small company and Iowa that has 50 guards, right, and say, hey, you can take this, you can deploy this. Not only do your margins go up 15% to 20%, 25% across your current portfolio, but you're going to be able to, you're going to have the ability to possibly lock down a building, right, and stop a mass casualty event. You, your garden company, this tiny, small, little guarding company in the midwest is, is gonna have the ability to save lives, right. And to do it within under 1 second, sub second timing. Kind of one example of this on that large integrator level, you know, one of our technology partners was telling us about, there is a fire that started in a microchip factory and how this technology was able to put that fire out within like 15 minutes, when typically that fire would have gone for maybe 30 or 40 minutes. But the, but because of this technology, the fire was limited to such a small span that in the past that that fire would have stopped production and literally would have cost over $100 million of loss in production when, but with the applications that we're using, it only, it only cost like $15 million. They only lost $15 million of that production that day versus 100 million. And so we're seeing this in a large scale with these large enterprise companies. And my thought is, why can't we use that in a grocery store?
Why can't we use that in a grocery store to save lives, right? If someone's going to come in. If they have a firearm, if we have thermal, if we have indexing, right. Why can't we lock that entire building down and stop that mass casualty event? You know, and it's great that, it's great that these large companies are saving hundreds of millions of dollars, but can we use that technology to save the lives of children? Right? Because for you, and I think that's what it comes down to, how are we going to save the next life, right? That's really what we're about. And so, Adam Schneider, Coo Sentinel, Overwatch services, Adam and I are about to go hit the road.
We're going to go look at some of the properties that are currently running these programs, and he's going to show me how it works because I, I don't have opposable thumbs. They're still developing. So, Adam, anything you want to say to the folks before he throat?
[00:32:53] Speaker B: No, thanks for having me on. Really appreciate it. Looking forward to seeing what you guys do in the community utilizing our software. So I can't, I can't wait to see it.
[00:33:00] Speaker A: Awesome. We were, Echelon was kind of the demo company for this. He said, hey, man, let's demo this at your company. And we're billing around $60,000 a month. We've been doing this for six months.
[00:33:13] Speaker B: Yeah. Less than a year. Half a year.
[00:33:15] Speaker A: Less than a year. So, I mean, we're killing it. We're always killing it. That's what we do here. And so, yeah, let's go see. Let's go talk about these areas and we'll go actually go into the properties and talk about how we've been able to stop crime. Sounds good.
[00:33:31] Speaker B: Yeah. Awesome.
[00:33:31] Speaker A: All right, let's go.
[00:33:32] Speaker B: Thank you.
