Wearable Allies: Transforming Workplace Safety

Pete [01:54]: Well, I’d love to hear about the story of how MākuSafe got started and the company’s mission. So can you tell us a little bit about that, Gabriel?

Gabriel Glynn [02:02]: Yeah, Pete. You know, it really started about 10, a little over 10 years ago. I was spending some time with my father at a facility where he was a safety director and he was responsible for about 2000 people’s lives every day. And he was a team of one.

And I had recently just sold a software company, I was kind of a software guy and spending time with him, trying to understand like, does a safety manager do? What’s your role? And he told me something that actually really kind of broke my heart, which was, I wait for something bad to happen. I figure out why it happened. I put a plan in place so that that bad thing doesn’t happen again. And then I spent a bunch of time filling out paperwork. And by the time I’m done with that, something else bad has happened. And so I repeat the process.

And so that’s when I kind of put my brain to work on how can we help folks like my father become less reactive when it comes to workplace safety. It’s certainly easy to understand why things are happening when you’ve had a lot of bad things happening, right? It’s kind of how you learn when you’re a toddler, when you’re a kid, you know, don’t stick your finger in that, don’t touch that. This hurts if you do that, right? But I don’t know that we need to do that in the workplace, right?

We have the ability to capture data that we can turn into information and begin to understand what are some of the risks and hazards that are present and that are ever changing around our workers when they’re on the job site? And are there clues within that data where we could send information off to folks like my father and they could go out and proactively try to mitigate that risk from happening. And so that was the thesis that was born out of a desire to help folks in the safety industry move from reactive to proactive.

Pete [03:44]: That’s a great story, I appreciate that. And that’s kind of what we’ve been trying to do. So we call it predict and prevent, you’re ahead of, you know, ahead of us. And that’s something that we find, you know, very compelling. So how does Make You Safe differentiate itself from others in the workplace safety industry?

Gabriel Glynn [04:03]: Yeah, I think there’s probably a few things that I would focus on. mean, one, our technology captures so much data compared to a lot of the things that are out there. And we can go into more detail, but environmental and motion and human voice and ergonomics, all of these other things. There’s a lot of stuff out there that maybe we’ll just focus on heat stress, for example, or sound exposure. But what we’ve done is we’ve combined all of those things into one because we know there are sometimes things in the environment that cause our body motions to change, right? And so being able to have the data that shows the context of when that’s happening and maybe why that’s happening. I think is important. that’s one thing. It’s just that the broadness.

Number two is our philosophical approach. knew early on, before my father was a safety manager, he worked in the shop for 20 years as a machinist, right? And so, you know, these folks, they want to come in, they want to do their job, they want to get home at the end of the day, they don’t want to be distracted. And so there’s a lot of stuff out there that we’re putting on workers that’s designed to buzz the worker or send them some sort of feedback, to get them to stop doing something that they’re doing. And what I recognize is the reality is that most of those people aren’t ergonomists, right? They’re factory workers, they’re machinists. And so if they’ve got something on their body that’s buzzing them every time they move something or pick something up, I don’t know that they’re going to know what to do about that or what to do with that. Even if they’ve been trained on proper motions, they could be going, I’m doing the proper motion, but yet they have an old football injury that prevents them from moving perfectly to the algorithm, right? And things like that. So we didn’t want to present negative feedback to the worker.

Instead, what we want to do is we want to gather that information, that data in real time and put it in the hands of folks like my father and other operations and safety leaders to be able to go have a human being conversation with that worker and say, Pete, we’re so happy that you’re working so hard. The system is telling us this. We’re just concerned that this isn’t going to be sustainable for you. So we just want to do some observation here and see if there’s something we can change about how you’re doing it so that we can make sure you can continue to do this job for years and years, right. And we feel like culturally that drives better outcomes.

And so those are some of the key things that stand out. As we get into talking a little more in detail about the technology itself and what we’re gathering, voice is a cornerstone of that. We’re one of the only technologies that I’ve seen out there that allows immediate voice communication from frontline workers to leadership to capture all kinds of different things. And that’s a feature that I would say our customers, when they’re evaluating wearable technology, we’re told more often than not that that was a deciding factor in wanting to go with our technology over something else.

Pete [06:50]: So, let’s dig into the technology a little bit. like a wearable device, as you’re saying, seems to be sort of the heart of your solution. Can you walk us through exactly what that device monitors? And then you talked a little bit about how you can do that, and it’s not intrusive to workers. But could you expand on that a little bit as well?

Gabriel Glynn [07:12]: Yeah, sure. So, first of all, pop this off my arm here. This is one of our wearable devices. You can see it’s a little bit larger maybe than my thumb. And that just clips into an arm holster with an elastic armband on the worker, kind of up around their head area, but really out of the way from the work that they’re doing.

And when they check a device out from one of our kiosks, it pairs that device with that worker for the day. And then they just do their job. They don’t have to do anything with the device. Again, the device isn’t going to send them feedback or buzz or beep or do anything. They just do their work. And while they’re doing that, we’re gathering a variety of different types of data. So I’ll focus on environmental first. So it’s a full noise dosimeter. We’re looking at the time weighted average of sound exposure for that worker of every minute of the work that they’re doing, regardless of what’s changed around them.

We’re looking at light levels, temperature, humidity. We calculate a real-time heat index for people. We’re looking at air quality, so VOCs, CO2, air pressure, just a variety of things in the environment, some of which can be really harmful to people directly, but then also some of it is really contextual, right? If we can begin to understand that people are slipping and tripping in a certain area of a facility when the temperature reaches 78 degrees and the humidity is at 63 percent, we know that that’s a risk threshold for us, right? And we can turn on air handlers before we get to that risk level, right? So some of it, again, is contextual.

And then I’ll move over to the motion side. So when we look at physical motions, we’re trying to analyze a few different things. One slips, trips and falls, right? And a slip and a trip that don’t result in an accident is a near miss. Oftentimes, somebody will report, I tripped and I broke my wrist. They go out and they go, what did you trip on? They said this air hose that’s laying here. Then seven other people go, oh yeah, I tripped over that stupid thing yesterday. Those were all near misses that were never reported that told us there’s a common hazard in that area that we can get rid of pretty easily. Slip trips and falls are important.

We’re also looking at what we call worker physicality. So how much energy am I producing while I’m doing my job? And how does that compare to my peers that also do the same job? And we can get into some of the use cases on this data and some of the nuance of it, but it becomes really interesting. You see quite a distribution of people, even if they have the same output, that use more or less energy than each other. So that’s on the ergonomic side of things.

I’ll shift over to location and spatial awareness. So we’re not tracking people’s location constantly. So we don’t know if they went into the bathroom. You know, that kind of goes to your question on adoption and acceptance. What we’re tracking is when an event happens or when something happens, we need to know where generally in the facility it is. Was it on loading dock A or was it on loading dock B? Those kinds of things that we’re looking at for location.

We also do a lot of human to machine connectivity and interaction. As you know, a lot of people are investing in smart machinery, automated machinery and things like that. Well, now if you have a device on a worker that is broadcasting a signal, those smart machines can pick up that signal and it can see that it’s Gabriel Glynne that’s standing in front of that machine. Now it can check and see, I authorized to use the machine? Yes. It can see if my training log is up to date. Yes, it can unlock and let me use it. And it might go to a specific speed that’s prescribed to me all without having to adjust the machinery or do any sort of setup or anything like that. So we do a lot of that interactive piece.

And then the last thing on this device itself is what we were chatting about earlier is the My Voice feature. And that’s a feature where the employee can just push a button, hold the button, it will begin recording what they’re saying up to 15 seconds. And when they let up on that button, within a few seconds, their supervisor is going to get a text message of what they just said. They can listen to the audio file. But it’s a really slick way for employees to report near misses, broken equipment, missing tools, process out of control, quality assurance issues. We get a lot of things through that voice feature. And I don’t know a company out there that doesn’t want to hear more from their frontline because those folks know the most of anybody in the company.

Pete [11:35]: I think I was looking at your website, which I thought was super informative. And one of the videos was very interesting to me because it was an animation of a forklift going through a warehouse. And it seemed to be able to understand when there was a lot of people, like I think the term was high, medium and low density. So can you just like, is that network together then? you can change, can you actually change the parameters of the driver, like tell the driver there’s a lot of people here, how does that work?

Gabriel Glynn [12:11]: Yeah, thanks for asking about that. That’s our newest product and that’s called Scout. And similar to how we do human to machine interaction, we recognize again, know, forklift and pedestrian accidents are usually very severe for the pedestrian. So they’re incredibly costly. And a lot of the technology that was on the marketplace to combat this would stop a forklift in its tracks if it, you know, detected somebody or things like that.

And there’s some inherent challenges in doing that. And sometimes you actually end up end up injuring the person when you lose the load off of the front of the fork truck. And so we thought, you know, our philosophy has been let’s gather data that tells a story and we can put that story in front of our safety and operations leaders and they can decide what they want to do operationally. So when we rolled Scout out, what it is, is it’s a Android tablet or phone that gets mounted inside of the cab of the fork truck and it’s running our Make You Smart app. And what it’s doing is picking up on all these wearable devices on the workers that are around them and it will change the screen color and it will let you know that the density is high, medium or low.

But what’s more important is the data that we gather on the backside and in our pilot with a large beverage company, they ran this at a warehouse and after a month of gathering data, they had a baseline of how many interactions between people and, and for trucks they have, which is inevitable in that kind of business. And then they looked at things like which drivers were most prone to have interactions? Which pedestrians were most prone? What times of day are these things occurring? Right? So they’re able to see a lot of visualization from this data and they zeroed in on a period of time where they said, there’s a lot of interaction here. And what they discovered is that that was break time. And you had a lot of fork trucks come into the break room with a lot of people coming to the break room. And so now with the data, they said, well, we’re going to set up a parking lot and fork truck operators will park in the parking lot and walk 50 meters to the break room. And they were able to cut down between that and a couple other operational changes, they cut down on over 85 % of their interactions with workers the very next month. And so again, I think the power is when you have the data and it tells you a story, you can take action on it.

The next powerful piece out of that Pete is that you get to see the net results of your action. So if you think back to when I said I sat down with my father and he said, you know, I’ll wait for something bad to happen. Then I put a plan in place so it doesn’t happen again. One of my follow up questions was, how do know your plan was any good? And he said, well, if less people get hurt next year, doing that same thing.

And I’m like, that’s a, that’s a really terrible way to find out that your plan was bad. Right?  And so here again, you know, with the power of data, you can make that operational change and know, is it making an impact and to what degree is that impact? So you can make better business decisions and you’re not just relying on waiting for the stack of accident forms for 2025 to compare it to the stack of accident forms from 2024 and hopefully see that it’s smaller.

Pete [15:25]: That’s a really rare, really great story to tell, right? mean, such a large decrease by using a network kind of technology. That’s very interesting. So you talked a little bit about the AI. Let me just circle back for a minute. How does MākuSafe help employers interpret data collected by the hardware beyond kind of that example you gave? Are there other things that you do?

Gabriel Glynn [15:45]: Yeah, so been in market for about three years now. We’ve gathered seven million hours of human work on our devices, which equates out to billions and billions and billions of data points. And so early on, we recognized that we’re going to be streaming a lot of different types of data and a pretty high quantity of data. And we have to be able to apply technology to allow folks like my father who –and he’ll admit it too, he’s not very technologically savvy, right? So you have to give them a single pane of glass that takes all of that data and turns it into actionable insights. And that’s what we call Make You Smart. That’s our software platform that processes, analyzes all this data, groups things together and very easy to digest visualizations, things like, know, stack ranking physicality of workers and, you know, seeing what the trends are in certain areas or job roles of air quality or sound exposures, those kinds of things.

Something that literally they can just get in, they can look at a dashboard and in their million square foot facility with 2,000 people, he can go, I need to go here, I need to talk to this person and here’s the data that tells me why I need to talk to them. We’ve got to figure something out that’s going on there. That’s a way better process than the hour-long stroll that he would take through the factory twice a day, just to see if you could be in the right place at the right time to affect something.

Pete [17:12]: Yeah, that’s pretty amazing. Any more examples of how you’re able to use the data to give a client an unexpected insight to an ergonomic challenge or productivity issue? That may be the traditional sort of wander around, not wander around, but go around with a purpose, sort of be in the right place at the right time approach might’ve missed?

Gabriel Glynn [17:32]: Yeah, one of my favorite things about being in this role is hearing the stories from customers. And I remember early on, one of the companies we were doing work for, this specific job role flagged as being the highest physicality job role in the facility. And they called us up and said, there’s something wrong with your data. This is one of the easiest jobs. Can you look into it? And we looked through the data. We’re like, it’s it shows that it’s really physical. they said, well, we don’t get that. It’s these little gals, and all they do is they roll these empty plastic barrels that used to be full of fluid onto a pallet, and then the pallet gets taken away. And that’s really about all they do.

So I said, well, go do an observation. Spend an actual shift and see if you can identify why this is flagging. But it didn’t take him long. The other part of the roll that he wasn’t really thinking about was the fact that when these barrels come and they’re full and they get set on the floor, they have to take a cap off of that barrel and they use something called a bung wrench, which is a big long metal wrench. And again, these are kind of smaller gals, barrels are kind of tall. Well, the first one gets up there and start jerking away at this cap, trying to get it to break loose. And she’s working on it, working on it. Finally, another gal gets done with her thing. She comes over and she starts pushing from the other side until crack, you know, that cap comes off and they’re able to pull it off.

And so they were, they were blown away and they, so they invested in, I think it was a hundred dollars in a pneumatic bung opener, which is a little device that you just put on top of the cap and they push a button and it pops that thing right out of there. And, and they were so grateful. They’re like, that was our least favorite part of the job, you know, and they never complained to people and never told anybody, but you know, that was shoulder surgeries waiting to happen and just, you know, high strain on the body.

So that’s one that stands out to me. We found a pinhole leak in a hydraulic hose that fed detergent into a washing machine at a facility and air quality was flagging as bad. Nobody could see anything or notice anything different. Well, they brought equipment in and figured out that it was a leak in this hydraulic hose.

We had a voice memo of a worker. A fall was detected that sent an alert first, then the voice memo came through and it was a worker and it was on a construction site. And he said, I just fell off the south side of the building. I’m fine, but I’m hanging by my tether. Can somebody come pull me back up? So, yeah, there was a combination of features on the technology that alerted somebody, you know, that something was wrong. And so we get, you know, so many stories like that and it just really motivates our team. share that with everybody across the company. Even the software guys that are just there to write software, they love hearing that the work that they’re doing is making a difference in the lives of these people.

Pete [20:25): I would certainly feel like that if I was hanging by each other. I’d be like, thank God for this device, right? Because not much is going to good is going to happen from there. You one of the things we hear about wearable technology is I think there’s some concerns about privacy or being monitored. So how has that changed? How has MākuSafe addressed, you know, those kind of objections in order to increase user acceptance?

Gabriel Glynn [20:53): Yeah, I’ll break that into two responses. One is the technology itself and then the other is the philosophy behind it. So, you know, first of all, we’re not gathering biometric data. There are devices out there that will measure your skin moisture and your heart rate and, you know, those kinds of things. We’re not looking at any of that. We’re looking outward from the worker, not inward. And so I think that that and the fact that we don’t provide that negative feedback, you don’t have that annoying device on your arm that’s going buzz buzz buzz all day while you’re trying to do your job and trying to focus. And so I think that’s part of it.

Then if I think philosophically, we started this company as a group of people interested in workplace safety. And across our team now we have almost every single person on the business side of our company has gone through and become a certified occupational safety specialist. They know OSHA code. Now it’s not a CSP. It’s not something you dedicate years to, but it is a very long, in-depth and expensive training process that we put people through because if you’re going to be on the business side of the house and interact with our customers and talk to the people on the plant floor on day-to-day basis, you need to know what they’re facing and what they’re dealing with. And what we’ve heard time and time again from customers is how refreshing it is to talk to a technology company that speaks safety.

And there’s a lot of tech out there in this space and software applications and anybody, you know, AI can write an iPhone app these days, you know, for workplace safety. But we really believe in it. We breathe it. You know, it’s personal to us. So I think that that shines through in the work that we do. And I think that’s a differentiator as well.

And when we talk to the employees, when we’re going to deploy at a new location, typically we’ll stand right up there with leadership of the organization. They’re saying, here’s what we’re doing and here’s why we’re doing it. And we’re showing them, here’s the data that’s being gathered. Here’s how that data is being used. And usually it takes a few weeks as people are kind of warming up to it. But once something gets picked up and a positive change is made because of the data telling them they needed to make a change, that spreads like wildfire through a facility. When people’s jobs get made easier and their conditions get made more comfortable and it was because the data said something had to change, they really appreciate that. So I think those are some of the things that are driving better adoption today than when we came into the marketplace even just a few years ago.

Pete [23:29): Yeah, I think one of the things you said earlier that I found very interesting and certainly I have not heard before was the idea that through your My Voice, frontline workers actually get a voice in what’s going on. So can you just dig into that a little bit more, maybe an example where you’ve seen how this is done and how it gives a more complete picture of what hazards might be present on a work site?

Gabriel Glynn [23:54): Yeah, I mean, we get a lot of the common, know, there’s a fork truck in front of the fire exit, you know, there’s a spill here on the floor, those kinds of things. But we get all different types of information from the frontline to leadership. I can remember one early on where one of the workers pushed the button and said, hey, this raw material that they’re working with, said, hey, this material smells different than what it normally does. And that was it.

Well, operations got that they called QA, QA went down and found out that the raw material that they received on the truck that day to make their product was an inferior grade of the material that they need and not what was supposed to be on the order. And had that worker not taken a few moments just to say something like this smells funny. All of that would have gone into production and they could have been facing untold amounts of money in recalls and brand damage to their product because it was using inferior materials and all kinds of things. So, I think it’s interesting when you go into these facilities and people still have the suggestion box on the wall that’s 400 yards away from the workers that they’re saying, hey, if you think of something, write it down and put it in there. People just don’t do that.

But you give them a tool like this where they don’t have to stop their work or track somebody down, they can do it quickly. And some of our customers have turned this into a really positive thing culturally, Pete, where one of my favorites was a big board inside this facility. And on one side it said, You Said, and it was all the voice messages. And on the other side, it says, We Did. And it was all the things that they were doing about the stuff that the employees reported through that.

And that level of transparency builds a lot of trust between people. And sometimes a worker just wants to know that they were heard.

Pete [25:43): It would seem, Gabriel, just from what you’ve said and maybe some things I’ve observed that at least as important as the technology to the to your client. It’s also is the client, do they have the culture to accept it and are they transparent? that would that be an accurate statement in terms of successful implementation?

Gabriel Glynn [26:03): 100%. We have some value statements internally that we use to evaluate our prospective customers. In the early days of a startup, when somebody says, hey, we’d like to buy your solution, you say yes, because my gosh, that’s your goal at that time. But it doesn’t take long to quickly learn that not everybody is a good customer. And what we found at the core of those customers that that we found to be not great was culture. It was technology was being implemented for the purpose of going after people. You know, as one example.

And if we hear anything like that in our conversations with the customer, we let them know that culturally, we don’t think that this is going to be a positive project for them. And sure, it’s business that we miss out on, but it also is so costly for us to have to go back in six or 12 months and get equipment back and close down a contract that we worked so hard to get. So culture, would say, is the number one ingredient for technology like this and being successful. You have to have a high degree of trust between the front line and the leadership that’s above them.

Pete [27:09): There’s a famous management guru, Peter Drucker, says, culture eats strategy for breakfast, right? And that has been my experience. At any rate, let me get you to scope up a little from wearables. When you think about wearables and why you decided to do this, why is that a better technology than other potential solutions?

Gabriel Glynn [27:33): Yeah, I think it depends on the application, right? I think there’s a space and a place for a lot of the technologies that are out there in the market. Some of it maybe even as pointed to your previous question, Peter, around the culture of an organization. And asking somebody to put something on their body might culturally be very difficult for them. Whereas if they put cameras up in the facility and they can glean some information from that, they can still be data gathering and be more effective with safety with those tools than without.

And so, you know, part of it, when I originally came up with the concept, I was thinking about, you know, putting sensor clusters around a facility is what I call them sensor clusters. And still I felt like it fell short. One, obviously you wouldn’t have any ergonomic data from a sensor cluster that’s mounted to a wall. But then two, I know from experience of being in these facilities and my father working in a factory my whole childhood, you and I could be 10 feet apart from each other and we got a machine between us and I’m picking stuff up and putting it in and you’re pulling it out the other side and the sound is coming off at you and the exhaust is coming off at me. And at the end of the day, what I experience was completely unique to me, right? And we were just a few feet apart from each other. And so I really wanted to bring technology down to the individual worker level and be able to understand what was that individual worker’s experience and how did it compare to their peers. So we knew we had to do it at the worker level.

Smartphone was an obvious, you know, first thought. But the reality is, you know, a lot of the facilities that we’re in, they’re not allowed to have a phone on the shop floor. It goes in their work locker when they come in. There’s IP reasons for that. There’s safety reasons for that. And so we knew we’d be limited on the application for it that way. And the last thing was we wanted to, we wanted to create an experience for that worker where they felt like they had an ally on their arm.

And that’s why we call the technology the Ally. The device itself is the ally. We wanted them to have that feeling that something is there, it’s present, it’s looking out for them, it’s watching all of these things. So the next time when they go to their supervisor and say, it is hot as hell in the Southeast corner of this facility where I have to work 90% of my shift and the shift supervisor goes, I checked the thermostat and it said it was 90 degrees. that’s, you know, that’s hot, but that’s not as hot as it can get. Right. Well, then you find out from the data, well, the paint booth that’s right next to him is making it so it’s 112 degrees where he’s at. So, you know, the data can be an ally to that worker as well.

Pete [30:14): I think you’ve done an amazing job. It’s a really cool product. On your website, you have a lot of really interesting use cases with pretty good metrics and a great story to tell. But of the results and achievement that MākuSafe has gotten through various implementations, which ones make you most proud?

Gabriel Glynn [30:34): Well, first and foremost, when our customers win awards for safety because of our technology implementation. One just last year won an award from National Safety Council for using technology and data to make significant changes to their safety and safety programs. And that was all done through MākuSafe. So watching them win is a really big positive for us.

And then ultimately, it’s the results. So we just started getting in 2024 results from our customers. And what’s great is they want to share with us. We had this thing implemented at these facilities, here was their record prior to implementing in these facilities, here’s what it is today, and here’s how they compare to the other facilities that do the same thing as them that aren’t using MakuSafe yet in our organization. And the ROI numbers across the board are in the hundreds of percent for implementing our technology. And I look at that and say, well, those are accidents that didn’t happen, that typically happen.

We had one customer that ran 2024 was their first full year. They had their lowest work comp in 12 years. And the owner said, it’s not even fair to compare to that because we were a much smaller company 12 years ago. They’ve grown a lot. So apples to apples, he said, it’s probably the best we’ve ever had. So, hearing those stories is important again to our team. That’s the driving force behind what we do. And when it gets quantified like that for us, then it’s really validating that we are making a difference.

Pete [32:02): Let’s talk about the future. Obviously, you’re a visionary and you’ve created a great organization in this space. But when you look forward, how do you see workplace safety technology evolving?

Gabriel Glynn  [32:14): Yeah, I think a couple of things come to mind. One, I think about the application of our data, right? Nobody’s gathered all of this kind of data in any sort of magnitude at any point in history. And we’re at seven million hours of human work now, and it’s growing exponentially. And the thought of my boys that are 13 and 10, maybe when they’re off in college here in a few years, maybe they have access to data like this. And they’re able to do some analysis and understand that by mixing it with some health data and this exposure data, we find out that if you’re exposed to these types of sounds for X period of time, you know, it’s likely to lead to a higher probability of a mental disorder, right? I don’t think we fully understand or appreciate the things in our environment and the mental toll that they can take on us.

And I’ve been in some of these facilities and I could tell you if I’m sitting next to a machine that goes, ka-chunk, ka-chunk, ka-chunk for 10 hours a day, you know, four or five days a week for 20 years, I’m probably going to be at risk, right? So, I think there’s a lot that can be gleaned from data like this, and I’m excited to know that we’re kind of at the forefront of gathering this type of stuff so that we can learn more about human work and human life.

And then the other side of it is, as we’ve seen, you know, obviously over the last year, the explosion of AI and how AI can process and understand data. You know, I can tell you some of the exciting things that we’re able to do with the data that we have just by asking AI questions around, you know, is there a correlation between this and temperature, or is there a correlation between, you know, sound exposure and this? And it’s really interesting to understand. And we’ve gathered enough data now, too, to be able to trend things like I can tell you, if you’re in the southern, you know, two tiers of states in the United States, the average daily temperature exposure to a worker has gone up one and a half degrees Fahrenheit in three years. That’s interesting, right? And if I’m a business owner, that’s interesting to me because if my facilities are in the South and they’re not air conditioned, for example, I can already look and say, here’s how much time I spend on heat protocol. And here’s what that equals in lost productivity. If this trend line continues by the year 2029, I’m going to have X amount of productivity lost because of heat protocol, because we’re going to be on an X amount more, which means if I want to make a decision on air conditioning my buildings, I’m now armed with some data that tells me I can make this change and I can learn that by 2027, 2028, I’ll have already earned that payback in the increase in productivity. So I think there’s just so much opportunity with that kind of data.

Pete [35:06): Yeah, and you know what would be really interesting. There’s a form of insurance called parametric insurance. And that says based on a parameter from a trusted data source, we’re just going to start paying claims because we know if this parameter reaches this level, bad things are happening. So temperature being one, there are organizations that are now saying, you know, in this case, if the outside temperature gets to X, nobody’s going to work anymore because we’re going to have too many insurance claims. But, speaking of the insurance industry, do you work with insurers or risk managers in any way or do you mostly work with facility owners?

Gabriel Glynn [35:48): Yeah, it’s a mix of both. We found an entry into the insurance space pretty early on through our friends at EMC Insurance Company. They’re headquartered here in Des Moines. They read an article about the technology I was working on and reached out and said, hey, we think you’re going to revolutionize workers’ comp insurance. We’d like to talk to you about it. And they’ve been a great investor, partner, friend of ours for many years now. And they helped us really understand what’s our position in that workers’ comp space.

And so today, a lot of the companies we work with, are large, they’re self-insured, they’re Fortune 500 brands, they have global locations, a lot of scalability opportunity, but we still do work with some of those middle-market customers where maybe you’ve got a few hundred employees up to a couple thousand employees. And many times that organization is a part of a captive, or maybe they are insured and they’ve got maybe a higher deductible, but they’re also getting risk control services. And so we recognize if we can work with a company where the risk control folks at their carrier can also have visibility and a view into the dashboard of MakuSmart, they can also be proactively sending resources out. So now instead of, know, well, it’s my month to go visit Jim’s machine shop over here. I’m going to go do my visit. They can look at their dashboard and say, holy cow, there’s there’s an air quality issue going on at this facility over here. I’m going to hop in the car and bring some of our equipment and we’re going to go figure this thing out, right? So really help them be more efficient and directed in the way that they deploy those risk control resources. So that’s primarily how we found ourselves working in that industry.

Pete [37:33): Gabriel, is there anything else we should know about MākuSafe?

Gabriel Glynn [37:37): I guess I’ll end with a story. So we’ve got a book coming out about the changing world of safety and how wearables is playing a part in that. And so that book’s coming out and in there, I share a bit of a personal story, which is my drive for this industry and my passion for it continues to grow. But I gravitated towards it, not just because of my dad, but I found out that in 1919, there was a pretty catastrophic accident in my hometown of Cedar Rapids. Douglas Starch Works Factory, which was the largest cornstarch producer in the world, that factory employed a lot of people from town.

And one day the factory exploded and killed everybody inside. Except for one person had decided to take their lunch break and go on a date with a gal that he met. And so he had left his buddies and left the building on his lunch date when the explosion happened. And that guy was my great grandfather. So had he not been going on a date with who ended up being my great grandmother, I wouldn’t even be here today talking about this. So, I’ve always just felt this personal draw to the space. My father being a safety guy, my mother being a nurse, both incredibly caring, compassionate professions. I found myself in this space and I found that it’s really comfortable for me. And I appreciate being a part of it. And so many colleagues that helped contribute to the book and to our thoughts and ideas in the process.

Pete [39:05): That is a great story. I like that a lot. That’s really cool. Thank you for your work, because I think you’re saving lives and that’s pretty cool. But thank you for your time, too. Pretty neat stuff. I appreciate it quite a bit.