How To Champion the AI Revolution At Your Company | Work Done Right with Richard Acton-Maher

Richard Acton-Maher, the Director of Product at construction industry AI powerhouse OpenSpace, joins the Work Done Right podcast to discuss his role in driving the AI revolution in construction. Richard covers a number of interesting topics, including AI shortcomings, the importance of unconventional data sources, and challenges with hardware implementations. A central point that Richard emphasizes throughout the episode is that without well-structured and organized data, the full potential of AI tools will continue to be unrealized. 

Additionally, the episode explores an area in construction where data structuring lags behind: project scheduling data and changes to project plans. Richard stresses the importance of collaboration across general contractors, technology companies, and the industry at large to address this issue. Don’t miss an informative episode that covers universal challenges and opportunities for the intersecting worlds of construction and technology. 

About Richard Acton-Maher

Our guest today is Richard Acton-Maher. Richard is the Director of Product at OpenSpace AI. With over fifteen years of industry-leading product management experience, Richard and his team are on the cutting edge of the AI/ML journey in the construction industry. 

In addition to his work in product management, Richard also served for several years as a consultant for companies such as McKinsey, and he is a graduate of Harvard Business School with a Bachelors in Economics.  

Top 3 Episode Takeaways

  1. The Importance of Data in AI Adoption: Richard emphasizes the critical role of data in the adoption of AI in the construction industry. He highlights that without organized and structured data, AI tools will not be useful or effective. Companies need to focus on collecting and digitizing their data immediately in order to take advantages of the efficiencies that AI can provide. 

  2. The Lag in Project Scheduling Data: One of the areas that Richard views as lagging behind in construction is structuring data related to project schedules, changes to the plan, and their causes. Because scheduling currently lacks a standardized approach for structuring this type of data, it is critical that GCs, technology companies, and the wider construction industry collaborate to address this issue. 

  3. Importance of Capturing Conversations: One key takeaway from the podcast is the significance of capturing not just visual data but also conversations on construction job sites. This includes discussions, decisions, and problem-solving dialogues that occur during projects. This data can potentially provide valuable insights for AI applications in the future, and it’s an area where technology providers like OpenSpace are exploring solutions. 

Bonus Takeaway: Hardware is hard. For any companies struggling with effective hardware integrations, Richard highlights that this is not an isolated problem. Hardware, such as cameras and IoT devices, can introduce complexity, connectivity issues, and friction in the user experience. These challenges must be accounted for and managed appropriately in order for the industry to progress in its digital transformation

Episode Transcript

Wes Edmiston:  

Richard, welcome to the show.  


Richard Acton-Maher:  

Thanks, Wes. Excited to chat today and have a great discussion.  


Wes Edmiston:  

Yeah, I’m looking forward to it. So before we really get deep diving into the rest of, we’ll say, the AI topic in construction and technology as a whole.  


I’m curious, you’re a product director, but you graduated from Harvard with a bachelor in economics. How did you connect those dots and end up where you are now doing what you do know?  


Richard Acton-Maher:  

I grew up kind of building computers and watching Bill Gates start Microsoft, basically.  


And so I had the dream to join the startup world at some point and got an opportunity to join a startup in San Francisco as I was a senior in college and kind of just said yes, flew out to San Francisco, did the know, got the offer.  


And I think at first I thought San Francisco way too far away from Boston. No way I’m going to make that move. And then towards the end I thought, you know, this might be the best thing to do in the world.  


And a couple of weeks later I was working at a startup in San Francisco and worked my way around from there. I’ve always been interested in computers and so found a way to go from working for the CFO, applying some of my economics knowledge to the software side and then eventually got into product and stayed there ever since.  


Wes Edmiston:  

Do you ever still do anything we’ll say in the economics domain? Are you on the side planning out any financial AI journey in economics?  


Richard Acton-Maher:  

I would love to. I do like to keep tabs on bitcoin and cryptocurrency and just kind of study up.  


Sometimes I have more time than others to learn about it, but I do like to watch that and see how those things are playing out and get a little skin in the game as well. And hopefully bitcoin shoots to the moon again sometime soon.  


Wes Edmiston:  

Yeah, we shall see. So AI is a topic that everybody is talking about right now. Seemingly everybody’s talking about right now, but not a lot of companies out there are doing much more than either, we’ll say, a chatbot or using it to help them write emails.  


You guys over at Open Space and a couple of other companies out there in the construction technology space are doing things drastically different, still using underlying AI technology. Could you tell us a bit about kind of what it is that you all are doing and what other kind of technologies out there as well are using this emerging AI technology to do?  


Richard Acton-Maher: 

Yeah, so I think the arc of technology and AI adoption and construction kind of starts with gathering and organizing data, then moves to sort of interpreting and understanding that data and then hopefully at some point, fully automating things.  


And right now, our product helps you gather and organize visual data from your construction site. So the simple description is you put a 360 camera on your hard hat, you walk the job site, you have a full visual record of everything you walked, and then you kind of have a Google Street view interface.  


To access that data, as well as our progress tracking product, which actually interprets that data and tells you how much of your drywall is hung, how much concrete is poured. Gives you some quantified progress on your job site based on what we’re seeing in those pictures.  


And so it’s something that really wouldn’t be possible without AI, because we’re taking a picture every half second. It’s a 360 image. So you’re getting a full 360 degree view of wherever you are. And that just wouldn’t we struggle alone to organize maybe hundreds of photos you might take on your phone.  


It and so organizing thousands and thousands of photos that we can take in a single capture just wouldn’t be possible manually. And so our technology localizes those pictures on the floor plan so you know exactly where each photo was taken and then, of course, interprets that and tells you something about your progress on the job site.  


And so I think the main thing is how effectively you can gather and organize a ton of visual data about your job site, as well as then also start to interpret that data. And I think we are just laser focused on highly effective, highly easy to use reality capture for the job site.  


Visual reality capture for the job site. And part of the reason we’ve been able to apply AI and see our adoption just skyrocket, I think, is that the use case is just very specific and very narrow. We do something very well, and for people that know how to leverage that technology, they’re finding it incredibly easy to adopt and deploy across their operations.  


Wes Edmiston:  

Yeah, I can imagine that. Honestly, the upfront effort of going through and filtering through all these images and structuring it in such a way to get it to kind of categorize categorized in the correct manner was a big lift overall and definitely something that we’ll say one individual will not be able to do throughout the whole of a project.  


Even I’m curious because you’re getting at the fact of you all have. Have this large data set with all of this unique we’ll say image recognition, object recognition, the ability in order to, through place and time, be able to capture these images.  


Something that I’ve heard on the All In podcast or any of these other technology related podcasts that people are very forward looking. Where’s all this stuff going? Especially whenever it comes to AI.  


People keep talking about the the people that are going to see the highest value in AI technologies are those with large unique data sets. And many different construction companies or technology providers for construction kind of fall into that bucket in my mind, where they have whether it’s millions of images, fundamentally, I guess, that you’ve collected through the implementation of a technology like this, or hundreds of thousands of emails or a bank of RFIs that have happened over a long period of time.  


Any of these can be very well viewed as unique data sets that this company, in whichever domain they’re in, kind of holds. What could somebody do? What could a construction company do, either on their side or by reaching out with a technology provider, a partner in this, to start utilizing some of those unique data sets they have to start maybe training a new AI product?  


Richard Acton-Maher: 

Yeah, it’s a great question. I mean, it reminds me of a couple of jobs ago in a different industry. We were doing analysis and AI on essentially B, two B, marketing and sales leads. So we were taking in our customers data and then spitting out some analytics.  


And even if they weren’t ready to use our product yet, there was a company called HubSpot that made this little widget that you could put on your website and it would just start recording data about who was visiting your site and you wouldn’t.  


Have to do anything with that data. You didn’t have to pay HubSpot for it, they would just start logging this data for you. And if a year and a half, two years, three years later, you wanted to start using their product, you’d have that history, and all that data would be saved and structured in a way that could be used.  


And I think we’re at a similar point in the construction industry where the vision for AI is you put it on top of something and it could be a total pile of garbage. But AI figures out where the patterns are and where the structure is and how to organize it, and then how to interpret it and build insights on top of that.  


And that’s definitely not the way it works today, and probably not the way it’s ever going to work, because a pile of garbage is a pile of garbage. And if you can structure your data and organize your data in a way that can be easily fed into whatever AI tools or algorithms are going to arrive on the scene one month or one year from now or ten years from now, it’s going to be a lot easier to adopt that technology.  


And so I think getting your data organized and digitized in all the ways that you can possibly do that, I think is a key thing to start doing now. Some of our customers have been capturing an open space for many years, and they’re going to be mining that data for, I think, at least a decade to come.  


To be able to know that on this type of project, in these conditions, when we used these teams, we were better at this and worse at that. And therefore, going forward, we should focus on this new project, on making sure we have the right people in place.  


Those kinds of insights can be drawn from that data for years to come. And so open space is a great way to start digitizing your data. But there are a lot of other things out there, too. Even just using all the features in procore and using them kind of correctly will help you get that data structured so that they can be interpreted.  


Um, and so I think, you know, it’s it’s not like there’s one stop shop. It’s not just like, start using this one product and just put all your data in it. I think it’s just anywhere you have a chance to get your data digitized and organized, take that opportunity, because someday it’s likely to pay back.  


Wes Edmiston:  

Yeah, that’s a good point. In the space of one, you need to not only collect data, but really, you need to have some level of data in the first place. And also with that data, you need to be able to put it in some form of a structure in order to be able to adequately use and accurately use that data.  


I’m curious also in this, because something that I’m thinking about is a lot of companies don’t have the underlying data, and they haven’t built up, say, industry wide. We haven’t built up a large bank of knowledge that is, in fact structured to start training additional LLMs or AI engines, rather to start helping us with predictive decision making or improved decision making on, we’ll say, a certain style of project or maybe whenever a frequent issue arises, the direction that we need to take in order to get the best outcome.  


What are the limitations, do you see, with creating additional AI tools that are going to help the construction industry beyond just, we’ll say, the level of data that we have? Or do you think that that is, we’ll say, the biggest issue that we have right now, overall?  


Richard Acton-Maher: 

I mean, data is the biggest issue because that’s how AI works. Without data, you have nothing. But I think there are a lot of kind of underpinnings to that. I was listening to, I think, the most recent podcast with Jay Snyder, and he said something about, if you don’t have good processes in place to begin with, adopting the latest and greatest technology is probably not going to be as effective.  


And so I think that’s a great point. I think good process that helps you create structured data, helps you deploy technology in efficient ways is pretty important. And I think ultimately the ability to adopt new technology is particularly challenging in construction and out in the field.  


And having the right kind of mindset and people and process that helps you adopt technology that can be a long term process. And thinking about how to be more technology oriented and ready for that, I think is important too.  


We have this guy in Florida. He actually lives in my hometown, Tampa, Florida, who loves capturing with OpenSpace. And a lot of people think you look a little silly when you put this camera on your hard hat.  


But he thinks it’s great. He thinks it’s hilarious. He loves walking the job site and getting people make fun of him. He shoots right back. People think it’s funny, people ask him questions. He loves doing it.  


In this world, we think of early adopters as people who just buy the latest iPhone and use all the cool gadgets. But sometimes the early adopters are just the guy who’s fun enough and goofy enough to go try this thing that makes you look kind of silly, but ultimately gets you the data you need to leverage the latest and greatest technologies.  


And so thinking about how your organization, your operations, your processes are ready for adoption of new technology, I think there’s a lot of different factors that play into that. And so, yeah, getting the techie goofy eye on staff, at least some project to try out the new stuff, could be a key underpinning to getting the data that you need.  


Wes Edmiston:  

You raise a lot of really good points there, especially like you’re saying selecting the right champion. Not just, we’ll say within senior levels of the organization in order to actually bring in the technology, but also the right people where the rubber meets the road to be able to actually go out there and implement with a level of success.  


Because they are maybe like you’re getting out of there, they have the AUD acidity in order to go out there and try these things or they have the level of care to be able to go out and make sure that things are getting done the right way and provide the feedback whenever they’re not.  


And also they’re willing to work with you. Yeah, it’s definitely a great point as far as whenever it actually gets to the actual implementation of this, in the space of what else we can do with getting prepared to use this data or to collect the data realistically to start building up.  


We’ll set the data sets that we need to be able to train some of these AI tools. Where do you see the biggest gaps in data sets so that we can start to target those areas to be able to better collect the data?  


Where are you seeing that?  


Richard Acton-Maher:  

I mean, the place my mind goes at first is certainly on the schedule side, what the schedule is planned to be and how the schedule is planned, how it actually plays out, which we know is going to be different, and then why it played out differently and what the impact was.  


This is just my own interpretation of what I’m seeing out there. And a lot of the most sort of AI tools today kind of just tell you, hey, we’re going to suck in all your data, and we’re going to tell you what’s on track, what’s off track, what’s that greatest risk, where you need to dig in.  


And one of the things that I think is missing from our underlying data is just what. The plan, the change to the plan and what’s the cause of that? And how can you build a model to anticipate what’s going to change it next time if you don’t know that sort of training data of where you started, where you ended up and why that change occurred.  


And so I think that’s one of the challenges of interpreting the pile of garbage, let’s say, is just where do you find those cause and effect links and how can you mind that data specifically and how you collect that and where you put that?  


I mean, there isn’t really a great way to structure that data right now. And so I think it’s an interesting challenge and it’s one that perhaps the technology companies and the industry are going to have to partner on to figure out where the leading edge is and how we can make that data even better, even more structured to drive really compelling AI results from it.  


Wes Edmiston:  

Yeah, it’s a really good point. Especially in the space of even a technology like open space. They can go through an area and tell you more or less exactly what was done yesterday or last week, whatever the interval is that people are going through and capturing these images, but without also knowing.  


My foreman told me to do this, or the superintendent laid this out to be done on the daily, weekly, monthly basis without having that level of that understanding of the input that achieved that output.  


You’re not actually going to be able to, on the next project, be able to forecast out schedule any better. Because maybe the reason why things were behind because somebody planned to be behind and maybe the reason for that was we had some other crafts that were working in this area so we had to scale back our productivity or our manpower and thus got lesser productivity than we would have otherwise had.  


So, yeah, that’s a really good point. In the space of. What is the plan that we had to get done? What then did we get done? And being able to assess all of that where we can better forecast out in the future.  


Richard Acton-Maher: 

Yeah, and I think that’s long term for OpenSpace and I think for everyone in the industry, what we’re thinking about is how do we continue to push the envelope in terms of visual reality capture. We recently built support for drones and we’re looking at interior drone capture, we’re looking at laser scans.  


But also beyond just the visual, if there are conversations that are happening on the job site that are useful for potential AI insights in the future, how do we build a product that helps us capture those conversations?  


Not by saying, okay, we’ll have the conversation, then turn around and punch in the notes onto your phone, but if you’re going to send a text message, what if you could send a text message through an app that’s logging all that info and then able to drive insights from it years from now?  


And so how you really capture more of reality, not just the pictures, but what’s actually going on on the job site and why, I think is a key aspect of where the industry is going to go from a data and AI evolution perspective in the next one to five years.  


Wes Edmiston:  

That’s a good point. There’s a thing that one of our team members here has every time that we join a teams meeting now. It’s called His Autopilot, which is like Autopilot, I don’t know if you’ve seen those, but it automatically captures basically in text what the conversation was and gives you kind of a brief summary of that.  


I can imagine, based off what you’re saying, something similar in the middle of our conference room on a construction site with this microphone in the middle, where it’s just logging the conversation because Big Brother is always watching or something, I guess, I don’t know.  


But really in the space of the conversations that we have, the problems that come up, the decisions that are made to overcome those challenges, that usually happens in a setting like that or in the field where maybe it’s harder to capture, but really, that’s a great point.  


We already have a large bank of this communication back and forth via email that is in a written text format, but we’re not not capturing those conversations. And we could there are technologies that are coming up right now to capture that in a very good way.  


Richard Acton-Maher:  

Yeah, totally. I am familiar with Otter and it’s a great example. I think it reminds me of Google Glass as well. There was a promise of capturing and always on, and it just totally crashed and burned because people couldn’t get over the creepy factor with otter.  


If you’re a Technologist, if you believe in the potential of this technology and this data, that sounds like a great idea. And I love the idea, I love the concept. But I was in a meeting about a year ago with maybe five to ten, mostly people on the engineering and product side at open space, and someone had sent their otter to the meeting.  


And in the first like five minutes, people said, hey, what is this thing here? Oh, it’s this new AI thing. And then someone said, well, should we just kick it out? And I was like, yeah, let’s kick it out.  


So we kicked it out. And for anyone that believes in the future of this technology, it’s like, oh man, no, it needs the data, the person sent it, they need the notes, but leave and be able to get over that creepy factor.  


And no doubt, yeah, if you put a mic that was always on a job site, there’s going to be a lot of stuff captured on that mic and a lot of stuff that people don’t want anyone else to hear. And so how you solve that, that is so fundamental.  


What the people actually feel and think on the job site and how to align the technology to those people in a way that is friendly to them, understandable to them, useful to them, but also captures the data that’s going to be fed into an AI algorithm in some server and who knows where to generate insights for 1015 years to come.  


They’re so unrelated in the long run, but they are so critically connected in the short run. And so that, I think, is why we haven’t figured it out yet because it’s a really hard problem to solve and it involves so many different factors more than just the technology.  


Wes Edmiston:  

Yeah, no, you’re right. Especially when we were talking about getting people on site bought into it. Beyond even just the creepy factor, it’s something new, something additional that we need to worry about.  


Hey, what if something comes up and it misinterprets something that I had said as well? What are the implications of that? There are a lot of thoughts and concerns, some of them valid, some of them not as valid whenever it comes to implementing one of these technologies.  

But it’s just the same as anything else that we’re doing, right? It’s the same behavioral differences as far as what you would expect from anybody on the craft side if you’re just rolling out a new app that they have to interface with.  


And part of it is we have to make it as technology providers, we have to make it easy to use and be transparent and get people’s feedback on it as we go to make sure that stakeholders are bought in. And the other aspect of it is over time, which we need recognize, there will be a time lag, over time, it will get adopted because people will get normalized to it.  


And we’ll all kind of coalesce around this eventually, right?  


Richard Acton-Maher: 

Exactly. And that’s something we’re seeing a lot at open space, is we. We had a lot of the earliest adopters at any one of our companies, especially the bigger GCs that we work with.  


The people that bought our product on day one probably were the earliest adopters, the most tech savvy of anyone in the company, or at least at that top maybe 10th percentile. But as the executives see the benefits of the product, they’re saying, okay, well, great, roll this out across the entire company, me.  


And it’s a much different story when you’re looking at, one, it’s not the early adopters, and two, it’s a lot of conversations that are happening without anyone from open space in the room. And so how you make it easy to use, easy to share with coworkers.  


It’s not us training. It’s going to be our user training other people. How you really make it easy not just for us to sell, but for our customers and our champions to sell across their organizations.  


It’s a totally different ballgame. Not just because they’re less familiar with the technology, but also because they don’t have access to the internal tools we have. They don’t have access to the internal trainings we have.  


And so we’re working a lot to build materials and build tips and tricks and really just build the product in a way that suits not just us selling directly, but champions of ours. Deploying this across an entire organization, and it’s a challenge.  


Wes Edmiston: 

Yeah, it definitely is. But I think it’s something that it’s not just open space that is fighting that fight of how is it that we can best enable all of our users to to use our product right to understand our product?  


How do we make both the product more intuitive, but also the users more knowledgeable? Is there anything in particular? Is there any area in training that you feel is maybe underserved a bit in training that.  


That are frequently asked questions from the users. And how is it that you guys are helping to solve that problem? What are you all doing to help make your users more knowledgeable?  



Richard Acton-Maher:  

Yeah, that’s a great question.  


I mean, a lot of different things I’m thinking of now, but we recently launched Academy, which is an internal interactive training modules for using our product. And so that’s one way that we’re investing in just creating a lot more self serve resources.  


But we also see one of the things that I love the most about our product is that a lot of our champions are using their knowledge of open space and their ability to deploy it on projects, to enhance and to accelerate their own careers.  


Because there is leadership both within their companies and at other places that are happy to hire them, that want this for all their projects but need boots on the ground to make it happen, need people who really understand how to deploy it and get it leveraged.  


And so we’re creating programs and resources for those people as well to get credit for the work they do and get credit for all of that effort they put in, as well as connect with other people and find the best solutions.  


But ultimately, it comes down to the product, and we can always make our product better, and we can always make the way I think about it is we need the smoothest possible on ramp from not using our product to being fully up and running and using every aspect of the product.  


And right now, to be honest, you got to buy this 360 camera. You have to figure out how to get it on your hard hat or buy one from us. You have to walk around the job site with the camera on your head, which some people may not like.  


And so we’re looking at sorts of ways we can just smooth that experience and maybe get you there eventually. But start with just capturing with your phone or making the capture process insanely simple.  


As simple as we just released a feature called Quick Connect where you just turn the camera on a couple of taps. The app automatically connects to the camera. So you don’t have to enter in a password.  


You don’t have to go to your WiFi settings and connect to the correct SSID. We just kind of do all that for you and things as simple as that. I mean, if we’ve got one of our people on the phone explaining to someone how to do that, it’s pretty easy explaining them the old way.  


But the new way, if you go into a conference room with 20 of your peers years and tell them, hey, go check this out, it’s really cool download the app here. Here are some cameras to use. All 20 of those people are going to struggle to make that connection work.  


Well, not anymore, because it’s just insanely simple in our app. And we didn’t think it was necessarily worth it to make it that easy, and we didn’t know how. It took a lot of really focusing and digging in to build that workflow.  


But now that we did it, it’s so clear that it was one of the critical things we did to make it easier to deploy our product across a large set of projects.  


Wes Edmiston:  

Good choice with that. I’ve seen personally, in my experience, that interface with hardware is a frequent pain point for customers.  


For users, it is a typical spot where there’s some level of friction just either through the connectivity with it not just connecting to it the first time, but maybe if something drops off, what happens then?  


Or even just the initial acquisition of this, the procurement of the hardware in the first place. Is that something that you all see as well? And if it is, I’m assuming that the steps that you’re taking, like you were talking about with making it easier to connect or some of the things that you’re doing to help reduce that friction, is that right?  


Richard Acton-Maher: 

Yeah. No, 100%. I mean, it is the hardware that makes it just it takes it to a whole other level of complexity, frustration, uncertainty. I look at the app and I tap Connect and there’s a little spinner.  


And even in that moment, even though I know it’s. Going to connect. It’s just like what is it doing? Why is it taking 7 seconds one time and 3 seconds another time? And how much feedback do you give it?  


It reminds me of the SimCity thing when it’s like loading the city. It would say reticulating splines. I think I’m dating myself by making this reference but even if you just put something fake in front of the user it probably makes them feel a little better about it.  


Obviously we don’t want to make stuff up and lie to our users but yeah, the hardware connection piece is a lot more complex from a technology perspective but I think it’s a lot less easy to understand and therefore a lot less easy to get comfortable with for the user as well.  


Wes Edmiston: 

Yeah it’s one of those things which I’ll say we love our hardware partners don’t want anybody to get the wrong impression but it’s like also the domain where we have the least amount of control effectively because there’s just that interface where we only have ownership up to a certain point.  


So it’s definitely difficult in a lot of different ways in order to make that experience as easy as possible and to get people trained up on how to use it, how to use it effectively and then how to problem solve whenever anything does come up.  


Agree completely. Now, you referenced know kind of the initial adopters, the early adopters of open space technology I guess that finally comes to mind. What are the trends that you’ve seen as far as kind of some of the users right?  


Who were your early adopters? Which industry were they in? As far as where in construction were they? And do you all have kind of like your typical customer base and what do they typically look like?  


Do you see more utilization in hospitals as opposed to office buildings or vice versa. Do you see anything in industrial or residential? What does that look like to you?  


Richard Acton-Maher:  

The simple rule of thumb is I think we see our adoption highest where there is the greatest number of dollars being deployed per acreage on any construction site.  


And so if you’ve got a really tall building, it’s a lot of dollars being put into a very small footprint. And therefore the ability to buy a couple of cameras and do a ton of captures every single day.  


The leverage on that hardware investment is very high. Whereas if, let’s say you’re building a bunch of custom homes across some large swath of land, it’s a lot less economical to have, let’s say, a camera in every single home or to have to drive between homes and do captures.  


And so it also helps, obviously, that a lot of our biggest customers are the biggest GCs in the world. And so they have technology departments and they have budgets and they have built the institutional capability to adopt new technology and to try out new technology and to make some kind of calculated bets.  


And I think it also helps that no project is the same, everything is different. But a lot of the basic underpinnings to commercial vertical construction are the same in terms of pouring concrete and hanging drywall, running plumbing and electrical.  


And so the consistency of those projects and the density of those projects, I think is where is what has led to that being the place where our product has been adopted most aggressively and successfully?  


For example, then to answer the question specifically about hospitals, I think that is an even better value to our customers because there’s so much going on in those buildings. There’s so many subtrades, there’s so many different things that need to happen every hour of every day, almost, to get that project moving and on time.  


That visual record of exactly where things are and when is even more beneficial when you’ve got so many things going on. And so, yeah, I think generally it’s just that efficiency, that leverage of one camera can do a ton of good on a job site that’s got 20 floors and you can capture all of them just by walking up a flight of stairs and doing a lap and then doing that 20 times.  


Wes Edmiston:  

Yeah, definitely. The aspect of, we’ll say, dollars per acre was a really good way of summarizing that as well. I imagine that correlates with the amount of front end engineering that happens in it as well.  


For those individual projects, they’re probably even better set up and established to implement a technology like this because they have suitable drawings and can have a solid baseline of data going into project execution to where whenever you’re where you can get the most out of these technologies as well.  


That makes sense. Yeah, totally. And, yeah, we have a BIM compare feature where you can put the BIM model side by side to any capture. And so, yeah, if you don’t have a BIM model, you don’t need it to use our product.  


But if you do have a BIM model, there’s even more value there because that side by side comparison can be super. That’s that’s definitely valuable, especially whenever it comes to all things quality, which is a big focus for the Cumulus team.