“We see an expansion of robots doing 80% of a job in the service sector, allowing humans to do what's complex."
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Episode 119 is a conversation with Greg Scott and Michelle Snider of SRT Labs, a technology startup specializing in robotics, but now offering a more comprehensive smart building offering as well.
We dove deep into applications for robots in facilities and how smart buildings stacks can incorporate data from robots as they move around. Plus, these folks are experts at serving the federal government, so we dove into their advice for others hoping to do the same.
Without further ado, please enjoy the Nexus podcast with SRT Labs.
A message from our partner, enVerid Systems:
Improving indoor air quality (IAQ) with optimized ventilation and air cleaning need not conflict with building decarbonization and climate resilience goals.
Read enVerid's new white paper, How to Achieve Sustainable Indoor Air Quality, to learn how a four-step Clean First approach can be used to design and operate low-energy, high-IAQ, climate resilient buildings of the future.
Mentions and Links
- SRT Labs (1:12)
- Discovery Robotics (16:17)
- Seegrid (16:23)
- Brain Corp (16:27)
- Avidbots (16:27)
- Siemens (21:45)
- Cyberclean Systems (30:40)
- Starship (1:00:35)
- Knightscope (1:00:39)
- Last Week Tonight: Infrastructure (1:08:48)
- Nexus Podcast #059 with Cisco DeVries (1:09:41)
- Siemens Podcast Network (1:11:14)
- Inclusion on Purpose Podcast (1:11:48)
- Inside of You Podcast (1:12:02)
- Infinite Game by Simon Sinek (1:12:19)
- SRT Labs (5:00)
- Use cases for robots (11:17)
- "Smart" definitions in the context of robots (19:49)
- Robotic product adoption (23:14)
- Problems unique to the federal sphere (41:58)
- Map-based data collection (46:54)
- The parallels and connection points with smart buildings (55:42)
- SRT's full offering and how it’s helping campuses (1:01:43)
- “Platforms” (1:05:05)
- Carveouts (1:08:03)
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Note: transcript was created using an imperfect machine learning tool and lightly edited by a human (so you can get the gist). Please forgive errors!
[00:00:33] James Dice: A cook note from our sponsor in various systems. We've discussed this a few times on the show in the past, improving indoor air quality with optimized ventilation and air cleaning. Doesn't need to conflict with building decarbonization and climate resiliency goals to show you why that's true and beret systems and a group of other IQ and energy experts put together a new white paper called how to achieve sustainable indoor air quality.
Check the link in the show notes to learn, to have a four [00:01:00] step clean first approach can be used to design and operate low energy, but high. Cue. Climate resilient buildings of the future.
[00:01:09] James Dice: This episode is a conversation with Greg Scott and Michelle Snyder of SRT labs, a technology startup specializing in robotics, but now offering a more comprehensive, smart building solution as well. We dove deep into applications for robotics and facilities and how smart building stacks can incorporate data from robotics as they move around.
Plus these folks are experts at serving the federal government. So we dove into their advice for others, hoping to do the same. A quick announcement before we dive in it's the end of the month. And that means the nexus pro community is getting together for our monthly member gathering this month. We'll hear all about darkness, smart buildings program and how they're using technology now. And in the future.
To get invites to all these events like this. Join the firstname.lastname@example.org labs that online slash nexus pro. Without further ado, please enjoy the nexus podcast with SRT labs. Hey, Greg and Michelle, welcome to the show. Can [00:02:00] you introduce yourself, starting with Greg?
[00:02:02] Greg Scott: Sounds great. Thanks so much for having us James. We're excited to be here. Um, so my name is Greg Scott. I'm the founder and CEO of SRT labs. Uh, I started my career at NASA. I got to launch the space shuttle from NASA's mission control center in Houston, Texas, which as a geeky sci-fi kid growing up and maybe still today, I was definitely a dream from true job, uh, right outta college.
Uh, but after the Columbia accident in 2003, uh, took an opportunity to do my PhD in space robotics at a university in London, uh, the university of Surrey and then, uh, was headhunted by the us Navy to build next generation robotics technologies for, uh, the department of defense. Cool.
[00:02:43] James Dice: And we'll dive into some of that here in a bit.
Um, Michelle, how about you introduce yourself?
[00:02:50] Michelle Snider: Hi, uh, so my name is Michelle Snyder. I'm a senior data analyst, uh, at SRT labs, uh, working on the solutions team. So my background is in [00:03:00] mathematics and physics. Um, I did my graduate work in algebraic, common torques, um, which is all about finding ways to describe complex mathematical problems by mapping them to simple counting problems.
Um, so essentially translating something from a complicated space, into a simpler space where it's easier to analyze. So out of, uh, after I finished my PhD, uh, I went to work for a federally funded research and development center, um, where I got to work on a wide range of projects that were in a much more applied space.
So working with computer scientists and, and electrical engineers and physicists, as well as other mathematicians, uh, working on solving classified problems for three letter agencies. So it was a lot of taking on the sort of higher risk, higher reward, longer term type projects, uh, for, for different government problems.
And, uh, and, and because the, the business model was basically to hire people based on the strength of their backgrounds, rather than their specific expertise, uh, [00:04:00] because the problem space was constantly changing. So it was a very, a very dynamic place to work, uh, on a lot of different types of problems.
And despite it being for government agencies, it's much more of an academic type environment. Um, so working with people with all different kinds of backgrounds to find new approaches, to complex problems, which may or may not have well defined solutions. Uh, so it's been 10 years doing that, um, picking out a whole bunch of different skills and different technical areas.
Uh, and then I came to work for SRT, which is a similar type solving undefined problems with complicated solutions. So, um, I've been here for about a year now, again, uh, senior data analyst on the solutions team. Um, and, uh, I'll dig into more of what kinds of stuff I do as we go, I think.
[00:04:47] James Dice: Yeah, absolutely. I don't know how many PhDs we've had on the show, but you guys have definitely increased the number or the percentage significantly.
So thanks for . Thanks for joining us. Um, great. Greg. Can you [00:05:00] talk about, so I don't think a lot of people on the, you know, in this audience have heard of you guys at this point. Um, I certainly hadn't when, whenever we started talking about doing this show, um, so Greg, can you talk about who the company, what the company is, what you guys do and give us a little bit of the founding story
[00:05:19] Greg Scott: sure.
Would be, would be happy to. Um, so I, I was one of the founders back in 2013. Uh, I was still working for the Naval research lab at the time. Um, my job with the Navy was building kind of next gen designing cool technologies, next generation tech. And, um, uh, while I was there, the idea of entrepreneurship starting my own company, uh, with the, with my, my team of founders was, was always something I'd wanted to do.
Uh, the Navy of course, wouldn't allow me to do so in a way that competed with what I was doing with the Navy. Um, and, and we actually decided to start the company in the area of custodial robotics. Um, so very different from what we were doing, uh, what I [00:06:00] was doing at the Navy and, and my co-founders at the time and their federal backgrounds as well.
Um, and, uh, when founded in 2013, um, there was, uh, not a whole lot of advances in technology related to how robotic systems would work or communicate together, especially not in the service space. Sure. On assembly lines, in the industrial space, there were kind of bespoke solutions in that area. But looking at the service industry and, and the small, but at the time growing number of custodial robots, not the small Roombas, although that, that kind of fits the mold.
We were looking more at the larger shopping cart sized, custodial robots, or delivery robots or concierge systems. And all of these platforms in and of themselves were, were great. I mean, especially for almost a decade ago in, in 20 13, 14, 15 timeframe. Um, but as robotics platforms, they were great, but they didn't really, they weren't sophisticated really to communicate with other outside systems, uh, heck even cloud technologies at the [00:07:00] time.
Uh, weren't really able to handle robotics fleet management software. Um, but that's where we started. And we could see the, the kind of the title wave, uh, coming for, uh, for robotics in the service industry. Um, so we started really building this, um, map-based platform for fleet management of service robots.
And, and as we were seeing the, the, not just the value that this was adding to robotics, but also where we could layer in internet of things where we could layer in building automation systems. And, and to some extent industrial controllers, um, we, we found that there was this true need for what we would call a mission control center for smart buildings, being able to understand and create that ecosystem of what's happening with all that connected technology, which is otherwise siloed and, and left to its own self to itself within those groups, uh, those individual silos.
[00:07:57] James Dice: Got it. Got it. Anything to add to that, Michelle? [00:08:00]
[00:08:00] Michelle Snider: Uh, yeah, so I'll just say I've been with the company about a year now. Um, I got involved because the. The COO of the company, Dr. Carolyn Elle. Um, I met her through, uh, work that we were both doing with an organization called the association for women in mathematics, um, doing volunteer work for that.
Uh, and so she convinced me that they were basically building the company that they wanted to work in. Um, and that there was this really interesting problem space, and they could really use someone to come in and take, take this data that was coming together from all these multiple sources and, and present it in a way that would, um, actually provide useful actionable intelligence to the clients.
So that's sort of where, where I came in as a, Greg's been building this for years and I kind of spoked in with like, let me make some shiny things to put on the end here.
[00:08:50] Greg Scott: you make us look smart, Michelle. We love it. uh,
[00:08:54] James Dice: great. So, so catch me up on your guys' customers. So it sounds like the Navy could potentially be one of those [00:09:00] customers.
Um, How do you guys go to market and what types of customers do you, uh, work with
[00:09:05] Greg Scott: today? Sure. Well, most, most of our growth has, has been slow and organic. Um, on purpose. That's been our strategy we've been, excuse me, we've really wanted to, um, slowly build and evolve the company in such a way that we're, uh, we're able to build that.
Um, Uh, that, uh, culture kind of that, that, um, uh, Michelle just referenced. So what we've started, where we'd started was, you know, small state and federal grant programs. And we moved into department of defense contracting. Um, that's where we've had, uh, a majority of our, uh, revenue and customers have come outta the department of defense, us Navy, us Marine Corps, um, where they're able to utilize our software for, uh, machine health monitoring, warehouse automation, um, and, uh, looking at, at, at machine shops and, and dry docks and, uh, paint [00:10:00] booths and blast booths and some of their, their, uh, facilities where they've had to deal with.
We had to deal with a lot of downtime issues and they don't know why. And this is where, and maybe I'll take a half a step back kind of the, the product that we've built, we call the one, uh, the one platform. Um, uh, and I know you love the word platform. We'll get into that a little bit later, but the idea the, the one platform is, is, you know, on its most simplistic side, it's an API hub for building data, um, on a more complex side, it's, it's more that mission control center where all this information is available.
Uh, and you're able to, to automate actions across devices from different manufacturers. Um, so as, as we look at at that kind of core platform and how it aligns with department of defense needs for, uh, reducing downtime and operations, uh, faster, um, uh, faster processing of information of warehouse [00:11:00] deliverables of those type of pieces, we've been able to, to take that, that federal contracting capability and build a pretty solid product, uh, which has now spun out into higher education and, uh, commercial facilities as well.
[00:11:14] James Dice: Very cool. So we're gonna unpack, we'll get back to platform. We'll get back to API hub, like you talked about earlier first, I wanted to hit robotics. So robotics is not a topic that we've had on the show before. Um, Getting a little bit outside of the smart buildings world. Just a tiny little bit, but then we'll bring it back later.
Um, absolutely. So when you talk about robotics as, as when it comes to your solutions and your products, what are the types of robots you're talking about? So you mentioned service robots, but like what are they actually doing and what are the use cases for each of those types of.
[00:11:53] Greg Scott: Uh, yeah, that what what's, what's exciting about, um, this being a robot geek myself, um, you know, this, this is [00:12:00] a space I love SRT labs really is service, robotics and technologies.
Um, that's the, kind of the core of, of what we do. And, um, and if you look at the types of robots that we interface with, um, Even just in general, in the service industry, you can look at these as, uh, custodial robots or delivery robots, or even warehouse robots, which we loop together, uh, in the service phase.
Um, and there's a number of different hardware companies that do that dozens of them. Now, back when we founded in 2013, there were, uh, two companies in the custodial robotics space. Um, and I could easily name a dozen today, uh, right off the top of my head. Um, but, uh, but being able to, to work with these different companies, these different platforms, these different hardware platforms themselves, uh, to help share an ecosystem, uh, let them all be smarter because of information that they don't have access.
Um, what's been really interesting also is, is how we can [00:13:00] work with, uh, the kind of the custodial robot providers, not the manufacturers, but the companies that, um, that maybe provide cleaning solutions. But a majority of that work comes from the robot and then say the cyber tech that supports that robot.
So the robot can go and clean 95% of the auditorium floor while the person is doing some of the, the accessory type work on the side. Okay. Um, and similarly with warehouses, you know, the warehouse robots can go in and. Retrieve items. As soon as they come through, uh, the inventory request system, they can go out and get a majority of the, uh, pallets and crates that are across the warehouse.
Maybe there's some, they can't reach for various reasons restrictions on the size of the robot or the type of shelf, et cetera. Um, but if they can do 80% of that job, uh, then allowing kind of the humans to do the complex part, the, the nominal conditions, the, the things that are, uh, a little bit nonstandard mm-hmm is really where we [00:14:00] see the, the expansion of robots in the service sector.
Um, being able to, to do a lot of the jobs that. In the custodial space, people either don't want, or are willing to go work, uh, across the street for a little bit more money for, and, uh, and instead provide them with, uh, the more rewarding jobs that support the need still in custodial or warehouse or otherwise.
Um, but, uh, but, but not kind of the boring parts of that job, mm-hmm, , that's the kind of the, the, the hybrid that we like to build, uh, and, and support in the industry.
[00:14:33] James Dice: Got it. Got it. So if I could imagine, I don't know anything about those 12 providers, but if I had to guess, I bet they all have full tech stacks themselves.
Right. Um, and I'm just totally going out on the limb here, but I would bet that if you go buy all 12 of those, you could buy hardware, software, potentially even services that sort of surround that siloed device. Mm-hmm right. Um, so can you talk [00:15:00] about those different tech stacks and then how they integrate with what you guys are building?
So you mention. Data that they don't have. How, how are you guys sort of integrating with all of them and sort of creating that ecosystem of robots
[00:15:14] Greg Scott: and, and that's army of bots, the army of bots, the robot revolution, if you will. Um, you know, the, what what's really interesting about that question is, is we look at robots, really not any differently than an IOT device or a building automation system, right?
Mm-hmm, , it's, it's got a bunch of data from whatever its components are. The difference is the robot moves and the IOT device is usually bolted to a wall somewhere. Um, so if you really think about it, maybe it's just a complex mobile. IOT device. Um, those same tech stacks exist in, uh, for any of the other hardware.
You've got the robots that, you know, those they're a little bit more complicated in that they need some navigation component to them. So they'll have LIDAR or, um, or [00:16:00] sonar or some, some cameras StereoVision cameras, et cetera, uh, which help the robot determine where it is in the building or where it is in the hallway.
Um, and it will localize itself. It can build a two-dimensional or three-dimensional map of its facility, uh, and whether that's a robot from discovery, robotics that can do vacuuming or UV light disinfecting, or whether it's from sea grid, that'll do warehouse pallet moving, or, you know, brain Corp or avid bots that have some custodial places and few others.
Um, The tech stacks themselves are relatively similar. They work really well. If you buy one of their robots or a hundred of their robots and you can monitor their robots and that information, um, in their PLA in their software platform and their software, where, uh, product, um, where, where things get tricky is that we, if one robot is driving down a hallway because you need it to be vacuumed and the second robot is doing delivery because it's a male support robot, or what have you.[00:17:00]
They don't necessarily know each other is there. They know there's an obstacle. They know it's moving. Maybe they can tell that it's a box instead of a person. Um, but at the end of the day, they don't know it's another robot. And they don't know that that other robot has some updates to the map of what the other robot might be, uh, coming across in the next few minutes and, and being able to.
Uh, create an ecosystem where robots can communicate in and of themselves, um, is, is a real value add to the facility managers, the ones who they have to deal with the technologies they don't want to. As, as you know, you've talked about a lot on your show, all the, the different siloed systems, just in building automation, across the campus buildings.
Yeah. Um, you start layering in robots that way and you buy one and it's great. You buy 10 from the same manufacturer and it's helpful, but a little bit crazy, but then you get two or three manufacturers in there. And as you alluded to different software, different hardware, different sensors, different maintenance [00:18:00] requirements, et cetera.
Yeah. Um, makes them really, it, it takes facility managers, extra resources. To manage the resources that are supposed to reduce their resources um, and, uh, and it just adds to the stress level. And that's kind of that piece that we wanna be able to, to address and, and taking that one step farther. Um, and you know, I can talk about robots all day and I'm excited to be here to do that.
But, um, one of, one of the other things that, that we found is, is really valuable is, you know, you may have a robot that is doing its job, but you also wanna collect some other data that the robot doesn't collect on its own. Maybe you wanna get air quality data around your building, but you don't want a hundred sensors that you have to pay for and manage.
One of the things that, that we've been working with a lot of our vendor partners on cuz again, we're just a software company. We partner with the companies that have all the great hardware being able to bolt an air quality sensor on a vacuuming robot, as it drives around a. You know, now you're collecting [00:19:00] across the hallways, across the building.
What is going on with air quality at the time, or if you look at the warehouse space, um, being able to bolt on an R F I D reader to a robot that's going around and picking up pallets, you can do real time inventory consistently updating it, every pass of that robot. Even if it's not supposed to get that CRE full of boots, it's gonna know it's there every time it's within a couple of aisles of it.
Hmm. So being able to layer in IOT type systems with the robots to provide that, like order of magnitude value, add to the end user at the reduced cost is, is just some massive opportunity that we've identified in space. And, and that our vendor partners are excited to be able to have their platforms be a part of.
[00:19:48] James Dice: Very cool. So the industry throws around a lot of, um, these different words, like smart cleaning. And smart warehouses, I guess as well. Uh, if we're talking about warehouses, [00:20:00] um, is there a definition from that and then like, is it robot plus something else? Or can you talk about what those terms sort of mean in the context of, of robots?
[00:20:14] Greg Scott: Uh, well, by, by definition, a robot is smart and I'm gonna use the phrase in another kind of as a recursive call here, but, um, you know, unlike a, you can have a, a soap dispenser on a wall. That's a dumb device. Yeah. Where it, you just pull a trigger, it gives you soap. You can have a smart device because it'll report how much quantity of sofas got left or what the battery state is, et cetera.
Um, you can't have a robot that isn't. You can't have a robot that doesn't process data. Um, so because it's kind of inherently built into what a robot is, um, you know, that, that term almost isn't used because the, by definition, the robot [00:21:00] has some form of intelligence to locate itself and navigator on a facility and, you know, have its information accessible to the end user.
Um, so, so when you look at a smart building, you know, you certainly talk, uh, in, in your, in your podcast about, you know, what, what a smart building is. Um, and, and primarily that, that revolves around building automation platforms, cuz that's usually the core mm-hmm . Um, and then maybe there's some other smart devices that are within that building.
Uh, so once you layer in the robots now you've got another smart device. From another manufacturer. Um, and, and what we've found is that a lot of the traditional smart building players like Siemens, for example, all of those guys, um, they don't really know what to do with robots. And, and that's been kind of fascinating as, as we've built a, a great relationship with the team at Siemens.
Um, They're excited to [00:22:00] actually get robot data and understand how that works because they don't have that experience. They don't have the, the history. Um, and that's a piece that we're able to provide back to them as one of our, one of our technology partners that, Hey, here's, here's kind of a standard format for what robot data looks like.
Here's why it's valuable within, in this example, the Siemens ecosystem, you know, you can, you could, if your fire alarm is going off and that's through the building automation platform, Robot's not gonna know it. It's not listening for a fire alarm or flashing lights, or frankly even gonna have a thermal sensor on it if it's driving through the fire.
Um, but you're gonna want that building automation system to tell the robot, Hey, there's a fire. Get out of the way of the hallway, cuz people are trying to leave the building. And, and that's what, what we really look at as, as we're looking at smart buildings, it's not just the siloed systems or some subset of connected siloed systems.
It's, it's really trying to add that value across building [00:23:00] automation, IOT, robotics, so that, uh, um, uh, so that the, the facility team, and of course then the, the daily users in those buildings are, are really getting the most benefit from this type of automation and support.
[00:23:14] James Dice: Got it. Robots. Um, I always ask about the human side of things and the human workflows that.
Robots help. Right. So, um, we've talked a little bit about someone kind of being able to go do something better with their time. Right. Um, can you talk a little bit about how humans then interact with them and how you can get humans to then adopt robot products, um, and sort of insert them into their day to day lives.
Maybe Michelle will go to you
[00:23:50] Michelle Snider: for that one. Yeah. So the team that I work on, uh, is that we call the solutions team, but we like to [00:24:00] joke that we're the problems team, because we're really actually trying to find what are the problems that we can solve for people. Uh, right. Uh, when we talk to customers and we listen to what they're, you know, just get them to complain about.
What's annoying about their day. We're trying to find where are those pain. Where are those things that are making their days harder and how can we make those better? Right. And, and really what we're really listening for is are there problems that we can solve using sensors and data specifically? And that's usually, no, usually that's not gonna solve everything, but that's kind of where we start.
Right. But the, you know, what, what we're talking about here is that you have all, you have this robot, you have, you can have these extra sensors to it, and now you're collecting all of this data. Right. But people don't need more data that just gives them one more thing that they have to think harder about what their next steps are gonna be.
Um, what they really need is to have all of that data synthesized into something that, um, just gives them exactly what they need to make their, their day better. [00:25:00] Right. And so when we're talking about implementing this, you know, the, the real goal of adoption being implementing this longer term process change that starts with just saying, Hey, can we make your day like five or 10?
Can we like five or 10 minutes off of. Annoying stuff that you have to do every day. Totally. So it's, it's about synthesizing what they're doing in their current process and then feeding it back into their workflow. Right. Um, I it's, I can collect all that data. I can make a whole bunch of analytics say I made you all these graphs.
All you have to do is look at them every day and stare at them and figure out what to do next. Nobody is gonna buy into that. Right? So what we have to do is figure out how to take what is coming off, these robots, what is coming off of these sensors, um, pull it together and then, and then hand them what their next step is gonna be, um, in such a way that it is completely logical, that that was the next step.
Um, so it just kind of feels like it should have been there all along, but then we also have to remind them, you know, that we did this [00:26:00] SRT logo on the, on your next step there. So there's things that I, that we really like to think about is, um, you have, you have these proxy measures, right? What are the things you can actually measure.
And then what are the measures that people actually care about? Right. So the one that I like to think about, um, is the simplest example is just air filters, right? So with an air filter, you have a differential pressure sensor that's in your air hand lift system, that's feeding into your building automation system.
That data is already there. So if we integrate with that system, we can pull that data out. Right. Um, so that differential pressure though, is a proxy measure. It's measuring, it's telling you, okay, how much dust and pollen in particulate matter has accumulated in this filter. Right? So, you know, that means that it's gonna depend on how much air I'm trying to push through it.
It's going to start to raise my energy bill if it starts to get too clogged, right. It starts to become this very complicated problem. [00:27:00] So, uh, yeah, I can collect all this differential pressure data and I can make this awesome plot, but really what does the client wanna know? The client just wants to say.
Uh, so do I have to change that filter today? Or can I just, can I just wait another month? Right? Mm-hmm can I push it off a little bit longer? Right. So, so we're always trying to listen for what are, what is the data we can collect? And then what is the thing that we can actually hand to the client and say, you don't need to worry about all of this integration that went into the fact that we can now pull this data out of your existing system.
But what is it that we can just give you this nice little package thing and say, okay, we'll just send you an alert when you need to change your filter. And that's all you have to worry about. Right? And so that it's sort of, there's a balancing act there with the, you know, there's this old adage that says what gets measured gets managed, right?
So if you start just saying, oh, I'm just tracking the differential pressure, you can start to say, but I care about the differential pressure. Like, no, I don't really, what I really care about is how is that telling me [00:28:00] something. About what the, the client wants to know, but also making sure that the client knows what we're doing.
So I didn't just, you know, take my magic eight ball and say, oh, looks like you should change your filter today. Right? So there's a balance between explaining to the client what it is that we're doing, how we're packaging up their like what we're, what we're taking the data and doing with it. But then, then at the end of the day, they can kind of forget about all that.
Once they're convinced that they know what's happening and just say, oh great, I don't have to change my, shall I get a little text message?
[00:28:32] Greg Scott: Mm-hmm yeah.
[00:28:34] James Dice: You're after my heart, Michelle, because it's been a long time, a long time since I ranted about how there's so many companies in this industry that wanna show you a dashboard.
And there's so much that has to go into going from that dashboard or looking at it to get to what action am I supposed to take? And it's such a long, such a huge gap. Most of the time. So the fact that you guys are thinking about that is, is great. Are there any examples? [00:29:00] That was a great filter example.
Are there any examples with robots cuz I'm trying to figure out how the humans interact with the robots and the data that you're collecting on the robots
[00:29:10] Greg Scott: specifically. Well, if, if you look at the custodial robotics space, um, there, we'll just say you've got robots that can scrub the floor as they drive around their big shopping cart, size, uh, system mm-hmm
Um, now in the custodial space, uh, there are standards, um, that if you wanna level one clean, it's gonna, you know, it's gonna take X number of minutes, per hundred square feet to get a level one clean. If you wanna level five clean, it's gonna take you this many minutes per square foot. And, and that's how, uh, building service, uh, contractors or so service companies, um, contractors, that's how they bid on projects.
You've got a hundred thousand square feet and you wanna level two clean. It's gonna cost you this much. Um, What's interesting is when you now layer in robots that are designed to do a deep, clean, or could be designed to do [00:30:00] a deep clean all the time mm-hmm and will always do the same amount of clean there's no smoke breaks or, you know, uh, hiccups or anything that it's, that's gotta deal with otherwise.
Um, but the model doesn't work. You can't. A certain amount per square foot in the same way, because it's not a human taking eight hours to do that work. It's now a robot taking four hours to do twice the job. Hmm. And, and it's, it's an interesting balance between how do you, how do the actual processes that, that, that are used standard in the industry?
How do those change? Yeah. And there's a few companies now, um, that, that we work with, uh, cyber clean systems is one that comes to mind where. Um, they have a fleet of robots from four or five different manufacturers. And, and we, we use our software to help monitor some of those systems for them. Um, and, and depending on the need of the customer, the customer may want some UV, light disinfecting and some floor mopping.
You know, they [00:31:00] pull the right robots together. They send one individual as their cyber tech with the robots to, to support the job. Um, but they bid on the projects in a way that's different. It's not quite the same as the standard. It says, okay, well, you can get this level two clean. But it's gonna cost this much, which is different because now our human labor is lower, but our robot labor is higher, but here's the level of clean you want.
We know what that should look like on the books. It's not gonna cost us that much because we're using a robot to do the LA do part of the labor so they can kind of underbid what the contract would look like while still providing the same, if not better service. Interesting. So it's a really, it it's, it turns the process on its head because the processes don't exist.
That know how to deal with robots. There's a, there are, um, not many, but there's a few companies out there. That, uh, that are looking at, how do you create new standards? Uh, standards of clean standards of cleaning cost rates, uh, that are [00:32:00] related to robotic cleaning that are related to smart soap dispensers and how that affects a contract, uh, custodial cleaning, uh, agreement.
So there's, there's a, there's a lot of change in that world. And right now, the, each of these companies that we know, we work with three different ones in that space. Um, each one of them has a different approach to it. Um, and all of them are good and all of them are valuable. Um, but they approach the problem in a, in a very process driven way.
That's different from each other.
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[00:33:37] James Dice: Yeah. Some of those parallels, the, the change that's happening with like HVAC and control system maintenance as well in those sort of service contracts. So are these companies that you guys work with, are they service providers that would also do the non robotic cleaning methods and they're just offering a robotic option or is, are there companies [00:34:00] coming in and saying we're we only do robotic support, robot supported cleaning, um, you know, and coming in to sort of disrupt the old players, how how's that
[00:34:11] Greg Scott: working out?
It's a mix of both one, one of the providers we have, uh, that we work with company called, uh, Kubo. They only use one brand of robot and they send that brand of robot in to do the job. And then they have some support text with them. Another company we work with nexus robotics, they've got a whole fleet of different types of robots from different manufacturers that have different applications.
Um, and they go in again, it's robot driven. Um, but they, the offering that they have is more diverse and we've got another company. We work with swarm services where they're a true custodial company. Um, but they layer in the robots when there's a specific application that they can do the job better, faster, cheaper, uh, with robots, but that's not their core focus.
And there's many more out there. Some that we work with as well, but those are kind of three very different business models that they [00:35:00] use because, and, and they approach the process in different ways.
[00:35:03] James Dice: And are any of the building owners themselves procuring the robots and then having their staff. Use them.
How does that, how is that happening? Yeah.
[00:35:13] Greg Scott: That, that, that tends to align more with the companies that don't subcontract, the cleaning. Uh, yeah. And some, a really great example of that is, a school district outside of Philly, where, they have, uh, custodial, labor union. And, um, what they've done is they've been able to work with the labor union to augment the, because of the number of, of schools they have, uh, in their district.
They're able to say, okay, well, let's build instead of hiring a half. Part-time person through the union let's instead of hiring four halftime people, or what did they say instead of hiring six halftime people let's hire two full-time people and two robots. So they're able to get what the union wants and have, have permanent well paid custodial [00:36:00] jobs, but we're also getting that robotic capability where now those labor jobs are learning to use the robot and applying it across, you know, a high school hallway, right.
Where you've got a massive amount of, of open, clear square footage, um, that the robots would thrive in. Um, so there's, there's a, um, uh, the, the organizations that tend to be either municipality or higher education or, um, uh, public schools where they have their own staff, that tends to be where, uh, the owners are then quote owners, or at least thers are, um, are buying the systems outright.
[00:36:39] James Dice: Cool. Yeah. Um, So I want to do a little bit of a segue to talking about the federal government. So you guys have kind of come out of the federal government and now headed more towards, uh, private sector or higher ed different types of public sector buildings. Um, so this question is gonna not be interesting for everyone outside of the [00:37:00] us, but can you guys talk about how as a small company you guys have, you know, what, what advice would you have for, for startups that are working with these big government agencies and especially in the time of the, um, inflation reduction act, mm-hmm with all the different, um, different types of incentives that are coming out to start, uh, decarbonize, you know, federal facilities.
As, as one of the line items in the, in the bill. So can you talk a little
[00:37:28] Greg Scott: bit more about that? Yeah. And there's, I mean, there's a, you, you could easily do another segment entirely on the, uh, the inflation act, the infrastructure act and how all of these are applying into the smart building, smart cities world.
Um, there, there is a lot there, but I'll, I'll, I'll start like. Without those two specifically in mind, um, you know, most startups focus on, uh, how do I sell 'em to businesses? You know, what is, you know, or consumers or, or what have you, uh, and federal government is usually the last thing on the list for a, for a [00:38:00] startup.
Um, but what's been interesting. And, and Michelle is, uh, as, as part of this, you know, her background in, in the three letter agencies, she's worked with myself with the department of defense, our COO, Carolyn, who she's mentioned. And Michelle mentioned earlier, um, with the state department, even our CFO with the federal preserve bank, um, we, we get government, um, we don't wanna be in government forever, and we don't want our company to Excel exclusively into the government.
But for us, the idea of putting together a 40 page proposal to. Respond to the solicitation and then wait six months until they maybe give us a 25% chance of winning it. , um, it's actually more, it was more comfortable as we were starting off again, slow and organic. It was a lot more comfortable for us than trying to do, build up a marketing and sales plan for how we're going to approach and compete with JLL as, you know, three people in my basement.
um, so, so you know, that we've clearly [00:39:00] grown since then. Um, but, but having the, the federal expertise we have, and we've got a couple of folk who are prior professors at Harvard and Georgetown and, and others that. They, and we all understand how to put a proposal together and we can, we can, we can tear apart a solicitation and say, okay, this is, this is what they're really after.
Here's how we can work our platform. And more importantly, our team's expertise into solving that problem. Um, and we've had, because of that, um, you know, I think we've had a, a higher than average. I think, you know, the, the, the actual average is something like 20 or 25% of, of federal applications get funded.
Um, and we've had a much higher success rate than that. Um, so, so we've, um, we've, we've been able to kind of leverage our team's expertise in that area. And especially with some of these new bills that are coming out, which are opening up, not just grant programs, but more money into contracting to support infrastructure or to support [00:40:00] small business.
Um, there's a lot of opportunities for working at, uh, state municipality levels, uh, which we're starting to do, um, just in the last couple of months, um, as well as at the federal level. And, um, and, and, and it's hard. I'm not gonna say, I mean, the fact that we think it's easier, it's not easy, it's excruciatingly painful in most cases.
Um, and, and you do have to recognize that you're, you're putting in a lot of effort upfront and you may or may not actually get the contract. And it's not maybe in that respect, it's not too dissimilar from commercial sales, but, um, but at the end of the day, you're, you're really defining the entire project.
In a, kind of a nebulous way because you don't, you're responding to a solicitation, but you don't really know what they want. You're making your best guess. And, and you put in a put in a proposal for it. Um, so I we're, we're a huge, I'm a huge advocate. I advise to a couple of other startups as well. And just, you know, my, my words to [00:41:00] them are always.
Look at the national science foundation or the other S B I R related solicitations that are out for small businesses. Some of them take a couple of hours to put in a, an application. Some of 'em maybe take a couple of days worth of work. Um, but you're starting with 50 75 K 150 K. And you know, what, what we've been able to see is we've gone from a 50 K proposal to 150 K project to a half, a million dollar project or a million and a half dollar project.
So being able to, you know, you've, if, if you can propose something good and you can over deliver, and you're, overdelivering in a program where there is an opportunity for continuation, uh, you really are able to kind of ride that wave, um, uh, in the federal.
[00:41:47] James Dice: Got it. That makes sense. I think, I think there's a lot of startups out there that I would advise the same thing.
Just have to be willing to make it part of your strategy. Yeah. Um, maybe not the whole thing.
[00:41:56] Greg Scott: Definitely not the whole
[00:41:57] James Dice: thing. Yeah. Yeah. [00:42:00] Michelle, I I'd love to hear when you guys actually go to serve these customers, what are some of the problems you're solving that are maybe unique to the federal sphere?
[00:42:09] Michelle Snider: Yeah. So, uh, so the one thing I wanna start off a little bit, just to think about, there's just a lot of government owned space in this country, right? Oh yeah. I mean, you, if you think about the, at the city, the county, the state, and then the federal level, right. There's just a lot of infrastructure that the government owns.
Right. And you know, you know, James that 87% of commercial buildings have no digital systems. Right. You think about, well now what do you think about the government? Right. It's that number's gotta be pretty terrible. Right? Mm-hmm so that. The thing about the government is that you, it has this sort of longer term investment is the way I like to think about it.
Um, so in people and knowledge and resources, and they're, they're really looking for more of this, this, uh, incremental growth is the way I think about it, right? So there's this huge amount of infrastructure that needs to be modernized. Um, and in the [00:43:00] commercial world, you know, people are gonna go build a brand new building, but the government's not gonna do that.
The government has this infrastructure and they're just gonna sort start duct taping some pieces on and try to make it a little bit better. Um, but the, you know, but the thing that I've been drilled in my whole life is the reduced for reuse, recycle. Right? So the, if you think about the government is really just, it's being very environmentally conscious, keeping what it has, just trying to, just trying to make it last as long possible.
[00:43:30] Greg Scott: as, and probably too long.
[00:43:33] Michelle Snider: Right. Probably too long. Right. Um, you know, there's a. The, the fantastic movie Zootopia, right? The there's a DMV worker who is natural sloth. Right. That's how quickly the DMV is moving. Right. So it's like this joke about how slowly the government moves. Like even kids get it.
Right. Like everybody knows this. Um, so, but, but sort of how we're fitting in is like, yes, there are all these, these awesome companies out there that are like, all right, we're gonna [00:44:00] build this super smart, fancy building where everything is integrated from scratch, jump 15 steps ahead at once. Um, but where we are is sort of, okay, well we have this existing infrastructure, how can we start laying the stepping stones to, to bring them into the future, you know, and at a pace that they they're willing to, to work with.
Right. Cause they're not gonna, the government is not gonna demolish one of their buildings St. Start over from scratch and then be like, give me a digital twin. Right. That's just not, that's not how they, how they roll. Right. But just because they're not. They don't have the interest or the funding, to be honest, um, to be able to do that, they still have all of this infrastructure.
So the buildings, um, these, this legacy equipment, this old machinery that's been built to last has been working for decades. They can still benefit from getting more insight into how all of that is working. Right. So one of the places that we look in is the machine monitoring side. We can add IOT sensors to these existing pieces of machinery and [00:45:00] give them information on like the, the vibration of a motor or the electrical current usage, or what's the air quality in your paint booth.
Is it safe for people to be painting in here? Right? Mm-hmm and then we can also take that data and then combine it into a bigger picture usage. Right. So if you. A whole bunch of different, uh, spaces that your people could be working in. And you wanna have an understanding of how they're using the spaces.
We can pull data from occupancy from whether or not fans are running, whether or not paint guns are running and actually give managers an understanding of how each space is being used. And then they can then better allocate their resources. So, so it's really about taking this infrastructure that's there and just adding a couple layers onto it again, to make everyone have a little bit more information to make their days go a little bit better.
So, you know, I, it, I think of it really in the, you know, in the reduced reuse, recycle way, like it's really, it's very, eco-friendly right. It's [00:46:00] very admirable when everything feels like everything now was like, you got planned obsolescence, you got fast fashion. You just everyone's expecting everything to turn over.
I'm gonna buy this cool gadget and I'm gonna have to buy a new one in a year. Right. So I feel like there's something really cool about being able to take these spaces and say, okay, this is what we have. This is what we're keeping, but how can we add a couple layers to make people's days better? Mm-hmm and to just take one, one step out of, out of their process so that they know a little bit more about what's going on.
[00:46:31] James Dice: Yeah. So do you know, so that 87% number, do you know what that number is for the federal government? Have you thought about that before?
[00:46:39] Michelle Snider: I don't. And I, I somehow doubt that
[00:46:42] James Dice: you just feel like it's less or, or more than 87%, I
[00:46:46] Michelle Snider: would guess so. Yeah, I would guess so just from how quickly they move or how slowly they move on things.
[00:46:52] James Dice: Yeah. Yeah, for sure. Cool. Well that, I think that's a great segue into like your guys' technical [00:47:00] solution. Um, I, I think one of the things I wanna circle back on, which I wanna circle back on a lot of things, but one of them them is this. Map based approach. And, um, I heard you say that and I think I wanna just UN ask, you know, what do you mean by that?
But also maybe I'll take a stab at it. So as you've developed the ability to talk to these different robots that are moving throughout the facility at any given time, you had to say, okay, where is that data being taken at any one time? And the ability to say, okay, that's where this piece of data exists in this 3d model of the building.
And then add the timestamp onto that. So you have, where is this in time? Where is this in space? And then the ability to create a data model that interacts that, that allows that piece of data to interact with the rest of the data being produced in the site. Is that, am I on the
[00:47:58] Greg Scott: right track? [00:48:00] Absolutely. Um, you know, I, I, I talk about it as a three-dimensional data model, which I know will roll her eyes with her math hat on, but, um, but, but kind of looking at it as every piece of data that comes into the ecosystem is time stamped.
And every piece of data that comes into the ecosystem is position stamped. And, and now being able to, to do, um, your time based analytics, like any group might do, that's looking at temperature, how temperature changes over time. But now taking that extra dimension to say, well, how does the temperature change over time, over position?
And how do those heat maps across the facility change? And where do you really have the spikes when you look at, at the position based data at different times of day. Um, and that's, what's really what, what, what drove us to this of course came from the robots, but it also came from the customers, right?
Facility managers, people that, that work in buildings, you know, that, that [00:49:00] floor plan you see as the fire escape planet at the front and back doors of every building, like that's, what's in their head. They, they, that's how they see their building and, and. May know that room two 17 is a bathroom, but they may, but they more importantly know that it's like 37 steps down this hallway cuz that's how they visualize what's going on.
Um, so the fact that, that what we, what we built and, and actually have patented is, is kind of around how the robots and the IOT systems work together in a time based and position based data management system. And, um, And, and to really geek out on that. Yeah. You can get the visuals for that. The visuals are great, cuz you can overlay floor plans with, you know, piping plans with temperature distribution plots on a certain day and you can get the robots trajectory and you can see where people who might be tagged or assets they're tagged how they move through a [00:50:00] facility.
Like all that looks cool. Um, but, but as Michelle mentioned earlier, we're not, you know, we, we don't just wanna give them data and dashboards to sit there and play video games all day. They got, they got better things to do. Um, but, but what it does is, is yeah, you can visualize that, but more importantly, all of that data is kept.
Um, in the databases and, and that's what Michelle and our solutions team works on finding all those patterns, understanding where there can be process savings, time savings, um, and, and, and, and helping the, the facility managers who visualize their, their facilities as a, as a map, how that works. Now, we don't do anything like digital twins or.
Um, uh, uh, BIM system type work, we might integrate with those systems at some point in the future, but, um, you know, we don't wanna go down. I don't wanna go down this rabbit hole, but we don't necessarily see the, the value in that for 99% of the [00:51:00] potential customer base. Um, but what we, but what we want to be able to provide is, is the information in a way that the facility managers don't have to think about it, cuz it's wrote nature and reflex.
And uh, and that's what, what we've been really excited to geek out about and, and, and build as, as the core. Got it. I guess I will say to, as part of the segue from the last question with working with the federal government and now with cool technology, getting funding through the federal government allows us to spend the time to build a deep tech product, um, because we're not rushing to patch something every week for a new commercial customer.
For example, it's actually given us four and a half years under various government contracts where, where we've been able to build, uh, this deep tech platform, this, this capability, um, uh, and, and make it robust and secure and flexible and scalable in a, in a way [00:52:00] that, that commercial, you know, if we, if we got our first commercial customer that wouldn't have allowed for that.
Yeah. I understand that. Um, so, you know, kind of calling back out to those companies, those startups that may wanna work in the federal space, there's definitely some advantage to the pain of dealing with the federal government when it comes to building cool innovations. Yeah. As part of what.
[00:52:20] James Dice: Got it. Okay.
So the audience would kill me if I didn't follow back up on the digital twin piece, so we don't have to go. We don't have to go down the rabbit hole. We don't have to go into full detail, but could you just restate what you mean by. What you guys think about the digital twin term and then kind of, it sounds like you're saying we could do this over here and it provides more value.
And I, I think what I'm hearing from you is that like what Michelle talked about earlier, how can I take this piece of tech or this insight and insert into someone's actual workflow? Is that kind of what
[00:52:51] Greg Scott: you mean? Yeah, I I'll start, but I know Michelle will wanna add to this too. Um, you know, I, I think the idea of digital twin [00:53:00] or, or kind of the BIM models of buildings, I think is extremely valuable for.
Apple for salt lake city, terminal five, like for these big, massive infrastructure projects that require all sorts of people to be involved, uh, all sorts of different companies and their technologies and, and everything. Um, especially, and to some extent only if. The, the organization that's doing it has the resources to upkeep it.
And, and I think that's the, that's where we have, you know, some concern why we don't directly play in that space is, um, is that yes, the high value customers are gonna be doing that. The, the really forward thinking customers are gonna be doing that. And that's awesome. Those are the types of customers we want.
Um, but you know, 99.9, repeating percent of the buildings in the country won't have. Uh, and, and never will. So from, from our perspective, although we love the forward leaning, feel [00:54:00] that those organizations have, and, and we work with, um, with a couple of groups that do that type of technology, where we can then layer in actual sensor data, uh, to the models that they've built.
Um, but we don't, um, we, we, we see a much larger opportunity for larger market. That's more easily accessible by a majority of those facility owners and facility managers. Michelle, do you wanna layer anything on top of,
[00:54:25] Michelle Snider: uh, just to say that, you know, buildings are never completed in some sense, right? Like you have a building and its usage is gonna change over time and people are gonna rearrange how the rooms are used.
And, and I picture the digital twin as something that it, it's not gonna be perfect for very long. Right. There's gonna be a lot of upkeep. That, that has to go with it as it's changing. Um, but again, I think it's, I, I just, I think about how would you build a digital twin for a government building that's been there for 75 years, right?
I [00:55:00] mean the, the number of plans you would have to dig up to even do it. So, yeah, I think, I think it's, you know, sort of going back to this big idea that we're, we're meeting people where they are, right. You have this building, what can we layer in to make it a little bit better with minimal amount of effort, minimal amount of, you know, invasion, minimal amount of upfront cost for that matter, um, to give them the biggest wins we can.
So again, I think it has a lot of value if you're building a building from scratch and you have all the plans in front of you and they're already all digital and now everybody who's building, the building can coordinate, but again, that's, that's gonna be a very small percentage of the buildings that are out there right now.
[00:55:41] James Dice: Got it. Okay. So where does, um, You guys are talking about this sort of, I think Greg said 3d data model, um, where does that piece, the 3d data model then connect in with, um, sort of the broader, smart building [00:56:00] ecosystem?
[00:56:00] Greg Scott: Um, so I guess, uh, let me, let me try and answer this one way. Um, you know, where. We're gonna be keeping a, we represent a three-dimensional model of a building, a facility and area.
What have you. Um, and we understand those coordinates from their reference frames for where all the devices are, whether that's a robot that's moving through or a thermostat that's bolted to the wall or, or a VV somewhere. Um, so, so that, that representation at least loosely, if not precisely, uh, does provide value for knowing where things are.
And, and I can see some parallels then to, well, what about the digital twin? Well, the digital twin, that's gonna change over time too. So now how do you, how do you align those? But if, if we ignore the digital twin part and we're looking at how we are, are representing that data, you know, we, we want to make sure that, um, that it's accessible to the end user.[00:57:00]
In a way that they understand it. We actually don't, even though we store 3d model of, of what that facility might be, um, we, the use end users for our software don't have access to it. They don't need to, they don't, we don't need them zooming around like a video game that they're they're, they're, they're playing.
So, uh, instead we're looking at it and we provide it to them as more of a floor. Sometimes a profile, but usually a floor plan, um, because that's how they view their facilities and that's how they use them. Um, so being able to, again, going back to Michelle's point of giving them the, not giving them dashboards to play with necessarily, but giving them the information, the insights, the actions that they need, uh, is where the core comes from.
So if we do have them, if they are using our display, yeah. They'll have a floor plan. They can see some devices, they can click on 'em, they get, get an idea of what's going on or they've got their campus and they can zoom into a building. They can zoom into a floor. Um, all of that is, is, uh, is a capability, but not in this [00:58:00] giant three-dimensional space that they have to navigate.
It's more the point and click because they're gonna know how they're building is aligned and how that, how that fits to the displays that we've built.
[00:58:11] James Dice: Got it. Okay. So let's circle back. Well, actually, I wanted to ask you, so as we talk about these different federal agencies, and then you guys expanding into the commercial world, um, robots strike me as something that kind of goes outside of the smart buildings world.
If we were thinking about that and more into this smart campus or smart facility, like, um, not smart facility, but smart, um, city. When you think about some, like you, um, department of defense bases, for example, it's basically like a city when you drive in there. Um, I was camping at the air force academy last weekend here in Colorado.
And I was like, I was driving inside of the base for 20 minutes before I got to my campsite. And it was like, well, this is like its own [00:59:00] city. Um, so can you talk about how you think about that smart cities concept as it expands beyond one, one
[00:59:07] Greg Scott: building. Uh, sure. Um, I'll, I'll start with that. Um, we, I think you pretty much nailed it on the head where a department of defense base is a municipality is a city in and of itself.
Uh, and, and, and we see direct parallels between, uh, DOD base infrastructure, commercial campuses, like the headquarters of large, uh, organizations. Yeah. University campuses, and now smart cities or towns, right there is, there is, um, uh, structurally not a whole lot of difference, uh, between them now. They all have their different priorities.
And, you know, we, we look at occupancy based automations of HVAC in the higher education space, where if rooms are empty, you can trim down the HVAC a bit, et cetera. Department of defense doesn't care about that. Uh, department of defense is gonna care [01:00:00] about process optimization. Which doesn't really align for the most part in higher ed or to some extent, even in municipal.
So, um, so, so it really does depend on, on the need of the customer, but the transition between them is very smooth and very seamless because the application, uh, the, the, the need for the type of data, uh, is the same or direct parallels across them. Um, I, I would say that, that on, on the robot side, um, there are some robotics platforms that align well for like campuses and, and especially the Starship and their delivery robots.
There's some security robots that drive around like night scope and a few others that the kind of patrol campuses, um, but a majority of the vendors that have robotics products in the like mobile robotics products, um, are actually more on the indoor facility space. So, so we've, we've, we've had this, this interesting, um, Uh, pull, especially from the [01:01:00] BAS vendors, we talked about Siemens before, um, where, where they're getting questioned a lot.
Hey, we wanna buy some robots, which ones work with your systems. And for the most part, their answers are all well, none of them. So, uh, so, so, so we're seeing an opportunity there as facility managers are starting to put the pieces together that their smart building, isn't just a smart HVAC system. It's the lighting, that's all brought together with the security, which is all brought together with the IOT.
And now the robots, you know, as, as that paradigm shift is happening, uh, in the buildings, we're also seeing that parallel as you're going to multi-building and campus type, uh, applic mm-hmm , but the robots do tend to start in the, at the building.
[01:01:43] James Dice: Got it. Yeah. This is interesting to me because you guys are experts in robotics and then expanding into the smart buildings.
And then it seems like the smart buildings folks are then saying, okay, how do we incorporate robots into what we're already doing? So, Michelle, can you talk about just like unpacking [01:02:00] the full offering here, what you guys are thinking about beyond robots? Um, maybe in terms of all the different use cases that you can then enable with this platform.
And then Greg, after that, I wanna get back to you with the platform question, what you mean by that, but Michelle, you go first. Yeah.
[01:02:19] Michelle Snider: So I mean the, the core of the product, the original collaborative robotics platform was, was really to, you know, as Greg was saying, you have, if you have two different robots, they should be able to share data with each other.
Right? Yeah. So, so when we're saying, what are we expanding beyond robots? It's really thinking about. Carrying this forward and saying, well, what if I take information from some import source, which could be stationary, it could be a mobile platform. It could be a software system. Um, and then letting that drive actions.
Right? So our, our product itself is hardware and software agnostic, right? So the goal is. To be able to plug it into to anything that [01:03:00] you can benefit from integrating these disparate systems. You can pull these different pieces together. So, uh, the, uh, the good place to start with that is to think about like a room reservation system on a campus, right?
So you have this massive amount of data that's been organized and shared, and you have, you know, 30,000 people on this university campus, and everyone knows how to use this. Right. Uh, they, they all know how to get to the right class at 9:00 AM on Monday morning. Right? So there's all of this information.
There's all of this behavioral science that went into, how do we build this system so that everyone understands how to use it and how, and we're automating, you know, whatever people did before these systems existed, they had probably spreadsheets about where all the classes were gonna be. Right? Yeah. But what they're really missing there is now you have this whole set of data that maybe other people could benefit from on your campus.
Right. So there's a lot of different things that can follow from it. And, uh, you know, we'll start with [01:04:00] the, the simplest one is like, if I know where all the classes are, I know which bathrooms are gonna need toilet paper first. Right. So that's great that this company built this amazing platform for room reservations.
And then I come in here with my math PhD and I'm like, ah, but what about the toilet paper? think about that one. Right? Uh, that's why you guys hired me. Right. um, but really that's, that's sort of the thing is, is you're taking this data then saying, okay, now that I have this data, what else can I do with it?
Right. Can I use it to say, well, now if I know how many people are gonna generally be in each building on campus, maybe that's where I use. I can use that to trigger a cleaning robot to say, well, this, this building's gonna need a lot more cleaning. Cause it's got a lot more people walking through it.
Right. So sort of starting from what data you can get and then saying, well, okay, now what data can I connect that with? To, to move everything forwards?
[01:04:53] James Dice: Makes sense. Makes sense. I wonder how many math, uh, thesis papers have been written on toilet [01:05:00] paper? Uh, toilet paper. Changeouts right. so, so Greg, I wanna circle back before we end this on, on the platform conversation.
So when you guys say platform, what do you mean by that? Um, and, um, I'm, I'm happy to add my context to it, but I, I didn't wanna dirty what you were gonna say
[01:05:22] Greg Scott: well, well, and, and, you know, we, we know from, from your, uh, your postings and the messy middle, and you know, that the platform, uh, term makes it to some extent, confusing to, uh, facility managers who, um, Think that a platform can do everything.
And, uh, you know, and, and we agree with you a platform, a there is no single platform out there that can do everything. Um, we would love to be able to get to that point as would every other company out there that claims to have a platform. So, um, so, so we see that, that, uh, [01:06:00] that messiness of, of how that term can be construed and, and heard we, we use it from the perspective of.
Bring as much stuff together as we can, um, and do so in the most open and flexible way possible. And as a company decision, you know, we don't have exclusivity agreements with any of our vendor partners. Right. We want, we, we, we love, uh, Working with a part and their, um, uh, smart dispenser platforms for, for bathrooms and hand sanitizer, et cetera.
Um, but we also like to work with Purell and, and torque and some of these other companies that have them and robotics is especially the same way. There's, there's half a dozen platforms out there, half a dozen hardware platforms out there, um, that, uh, uh, that, that do Mo. Floor mopping or that do floor vacuuming.
And, and we wanna make sure that, that the customers who decide to use our product, um, can have the greatest flexibility that they can, um, [01:07:00] in the otherwise siloed systems that we try to bring together. So, you know, we, we would love to get to a point we're moving towards the point now of being more open with how our ecosystem works, both from the perspective of how we build integrations with these vendor partners and, and, and hopefully soon how we can open it up so they can build their own integrations, should they decide to do so?
Um, you know, I, I'm not sure we'll ever get to the point of being open source, but certainly. Platform, uh, to be able to, uh, to, to provide the widest opportunity to our customer base mm-hmm um, so, so you're right. There's, you know, there, there's always gonna be companies that may not want to be part of our ecosystem.
Um, uh, but we are certainly trying to be as open and flexible as possible for, for the customer base. Makes
[01:07:52] James Dice: sense. Makes sense. Well, I wanna thank you guys for coming on the show kind, run down our time here. I'd love to ask you just, uh, more [01:08:00] personal questions before we wrap up, um, little bit of carve outs.
What, what TV show podcast book, uh, movie, etcetera, would you guys recommend the audience checks out? Um, and if it's a PhD program or some sort of paper, it's totally fine. Uh, Michelle, you go first.
[01:08:18] Michelle Snider: All right. Well, uh, that is not what I have, uh, for my, my carve out to share here. Um, so one of the, one of my.
Favorite things to watch movie wise, disaster movies. Right. So I love it when they, you know, predict everything's going wrong. So, but relevant to what we're talking about here, um, about seven years ago, uh, John Oliver last week tonight on HBO or he, every episode, he does a focused, deep dive on some topic.
So he did a whole episode on the importance of infrastructure. Right. And I, and I just got, it's just like, again, after my heart, this whole like, maintain what you got . Um, but the whole, the whole episode ended with he did cuz he always ends with some sort [01:09:00] of flashy something. Right. So he ended with this fake movie preview.
Um, it was in the style of all the classic disaster movies. Um, but, but about infrastructure and the tagline being, if anything exciting happens, we've done it wrong. . So I, I really just, I really enjoyed that whole thing about how do we, how do we make infrastructure? Something that people are actually excited to, to repair and monitor?
Um, that's, what's keeping our company running, you know, it's cool. So that's my, that's my little throwback. Cause I had to look it up. I couldn't believe it was seven, seven whole years ago now.
[01:09:35] James Dice: Yeah, totally. I'll have to rewatch that. I I'm, I know that I watched it when it came out, but I'll have to rewatch that.
My funny John Oliver story is that I was interviewing someone for the podcast about, I don't know it was early in the podcast, so I was way back in the archive at this point. So if you're people are new listeners that probably hadn't heard this, but one day it was like a Tuesday or whatever. John Oliver releases his shows, I guess it's Sunday night.[01:10:00]
So it would've been Monday morning. I wake up and I, I don't know what I was doing, but I was on LinkedIn and. Someone posted John Oliver and it cuz it was about, um, pace programs, property assessed, clean energy programs. So this was, um, you know, a deep dive on pace and how it wasn't, you know, being implemented in the most ethical way everywhere.
And they, so I start watching it cause I'm an energy nerd, but long story short, they profile and sort of kind of attack. The person I was interviewing that afternoon for this podcast on job uh, and, and we, we ended up talking about it on the show, but it was that, that's my funny jot Oliver story that relates to this podcast.
[01:10:46] Greg Scott: wow. That's that's timing right there. yeah, but
[01:10:50] James Dice: very interesting timing. Greg. What about you? What about what's
[01:10:53] Greg Scott: your car about? Um, I mean, if, if I'm, if I'm looking at, in, I mean, in reality, most of my [01:11:00] reading today revolves around very hungry caterpillar or equivalent type literature, nice. Um, for my four year old.
Um, but, uh, but when I do get a chance, you know, I do a little bit within the industry, um, like the, the Siemens podcast, for example, I've been, been following up on some of their partnerships and, and, and some, some of the related groups that we we work with, which is great. Um, but a lot of my time has actually gone more into the corporate side, right.
As the CEO of the company, um, making sure that we can. Um, uh, develop ethical practices and, you know, long term strategies and how to run a good company and, and what to keep in mind. And, um, so, so a lot of, a lot of my, my listening now, uh, primarily podcasts are around like, um, uh, the inclusion Inc podcasts from B Ray, which is, uh, created about small businesses and, and how they, um, uh, how they can ensure to.
Uh, incorporate, uh, diversity and [01:12:00] inclusion as, as part of their hiring practices. Um, inside of you with Michael Rosenbaum is always great to kind of understand from a mental health perspective, how, uh, for the most part celebrities, but other folk are, are dealing with these type of, of things. And, um, and then from a business operations perspective, uh, I'm, I'm about halfway through the infinite game right now from, um, uh, Simon cenek and that's, uh, that's been a really good read is we're, you know, we're a company that's ums trying to grow, trying to scale how do you, how do you build the right culture and the right ecosystem for, uh, for your team and, and your future scale?
[01:12:37] James Dice: Awesome. Those are great recommendations. Uh, I'm sure there will be people like you out there that are building companies that, uh, get a lot out of those recommendations. So thank you. Uh, and thanks for coming on the show. This was really fun. It was great to, uh, unpack
[01:12:49] Greg Scott: this with you all. Absolutely. Thanks so much for having us, James.
I love TA I'd love talking about robots, so, uh thanks for giving us a chance to do so. Absolutely.