0:01 - Andy
Hey Madeline, what are you using AI for right now?

0:04 - Madeline
I don't use it for much other than like creative ideas and such. What's your opinion on it?

0:09 - Andy
So any like can you give me an example? Creative ideas?

0:12 - Madeline
So back in the spring I had the opportunity to make an invitation for something and I really had no idea where I wanted to start so I was just like give me some mock-up ideas of what this invitation could look like,

0:23 - Andy
Okay, did it come up?

0:25 - Madeline
I basically copied one of the ideas picture for picture of what it did so Adding some of my own creative influence to it.

0:34 - Andy
Yeah, awesome. Well, and that's an interesting conversation perhaps because, you know, AI is so prevalent right now and the guest that we have today is really an AI entrepreneur.

0:49 - Madeline
So, on today's episode, we had Daniel Calabro who is an engineer, but he also is with Ferris App.

0:56 - Andy
And so, y'all talked a lot about AI and the usage of it in the Yeah, we definitely talked about that and just it started with the philosophical conversation, which I find really interesting because the Enhanced podcast, of course, we're all about the why. So why even get into AI? Why use it? It brings up some interesting debates. So we got into that some and we also got into his why really toward the end of the episode. And I would say it was one of the more powerful ones, if not, I mean, it was definitely a strong why, and I don't want to ruin it too much, so it was really interesting to find out his backstory. So that's really what we found out a lot about him, but his reason for getting into engineering at large, and then even into AI, they're very deep. Like a surface like hey I'm just gonna go make some money or I'm just going into engineering because it's it's random yeah there's a deep why and what he's doing so that was a that was an encouraging thing to hear my name is Andy Richardson and this is our producer Madeline and this is the enhanced AEC podcast I've been doing this 27 years I'm still learning so much about the AEC and Let's jump to the episode.

2:29 - Madeline
Welcome to Enhance, an AEC podcast where we learn the why behind AEC professionals so that you can learn your why.

2:36 - Andy
So yeah, welcome to the Enhance podcast, Daniel.

2:41 - Daniel
Thank you for having me. I appreciate it. I've seen all the success that you've been having on LinkedIn. It's super fun to follow along and to watch. It's been great stuff.

2:50 - Andy
Thanks for following along with us on LinkedIn, and likewise, exciting to see what's happening with you and with your life and with your business. And it's an exciting time for somebody in your business, of course, AI. So, yeah, I'm really excited about some of the takes you have on AI. Obviously, we want to hear about your business, Ferris. And yeah, so thanks for coming on today. I like to just start out, I don't know if you've ever heard to our Enhanced Podcast, but so the Enhanced Podcast, it's all about the AEC, but really like the title suggests, it's also about a little deeper questions of why, you know. So it's a little fun take on it. So we want to get into some of that. But I like to say that we talk about the what and the why. And so we want to dig into the granular aspects of your business. And you also have an engineering background. You practice as an engineer. So we want to hear about that, too. But I want to start out with just a few takes from you, maybe curveballs, I call them. I like it. And it said on the preview notes that you unscripted, so we're gonna roll with that.

4:14 - Daniel
Yeah, I like just being able to speak naturally about what I'm thinking about something. I don't want it to be some pre-recorded chatty BTS response.

4:22 - Andy
Yeah, I might have a few of those too to help us along if we stumble enough. But yeah, really, I was thinking about, you know, AI, and I feel that the potential for AI is significant. How do you feel that AI could really change the way we live?

4:42 - Daniel
Well, I think you're seeing it change how people interact with search engines already. So, in the past, everyone would Google something when they had a question, and depending on how you asked Google that question, you'd get a certain queried number of links and responses. Now, with artificial intelligence and these generative pre-trained transformers, what you're getting is the ability to ask any sort of question and get some sort of Now, is that response valuable to the end user? Where is that response being generated from These are all questions that are sort of second and third derivative here. But I think having a wealth of information at a user's fingertips is very important. And what I like to use it for is I like to like steel man my counterpoints to ideas that I have. So I might think something is a really good idea, but I use it to, hey, find blind spots for me in my thinking process. And hey, what What am I not actually looking at here? What could I be thinking about better in a different light? I like to use it for those types of things to get out of my own little bubble, get out of my own perspective, and try to create another one to see if, hey, how am I viewing reality? Is it accurate? Or I might have these biases and these constraints. So I think it provides a very interesting alternative perspective to your typical way of looking at reality through the life experience that you've had. I find that very valuable. There are many, many ways AI is going to change the world that we live in. I know when people talk about AI, there's always this negative elephant in the room that's going to displace a lot of people in what they do and how they live and how they work. And I believe that that is the case for a number of industries. For example, when it comes to Uber drivers or taxi drivers, I'm not sure if you're aware of the full self-driving cars. I was recently in San Francisco. They have the Waymo cars driving around everywhere. And it might seem far-fetched here in 2025, But what does the world look like in 2030 or 2035? And when these cars start to go ahead and displacing workers from their typical jobs, what do those people do with their lives? I think we're going to have to have a conversation as a society for jobs that are easily automatable. What are those people going to do with their jobs? Are new jobs going to arise? And I think that is more of the conversation that I've been having with a lot of people is saying, OK, if AI is to come in and automate a bunch of industries and replace the human to face in those industries, what are we going to be doing with those people? And what are those people going to be doing with their lives and their time and their energy? So I think that's something that we should really start to pay more attention to and ask ourselves societally, what does that mean for everybody?

7:14 - Andy
Yeah, so there's two mentalities. One is the abundance mentality that these technologies can actually create an abundance and more jobs, more availability, more revenue for the population at large, and then the scarcity mentality that people are going to lose jobs. I think there's going to be a, we're going to see, okay, definitely a displacement, but it also, so that's definitely a part of it. And then also, if you're, which camp are you in? Are you kind of more scarcity or abundance?

7:55 - Daniel
I am in the abundance category. So when you look at the production of goods, if the cost to produce these goods decreases, hopefully the cost to purchase those goods would also decrease. So for example, I don't know if you've seen like the price to take like a Waymo or a full self-driving robo-taxi in Texas, but a job that wouldn't typically cost you 20 bucks to someone to drive you a few miles with a self-driving vehicle costs you like a fraction of that, like $5 or $6. So the cost for these services, for these goods, it does decrease. And I think at scale, what you're going to see, hopefully, is a lot of abundance that arises from this. With that abundance comes a lot of free time, and sometimes with a lot of free time that people have, and how do they spend that free time? What do they do with that free time? I'm a little bit more anxious about that, and where does that time and that energy flow to? Does it flow to more creative things like the arts or making music and things of that nature, or are they gonna go to more, I don't know, negative aspects where people are just gonna be a little bit lost and kind of not really knowing what to do. So I think there's going to be abundance of like services and goods, but what comes from that is, whether you want to make it a good thing or a bad thing, I think is up for debate and discussion.

9:15 - Andy
Yeah, very, very interesting conversation. Very philosophical. So right now I guess we can let the macroeconomics people really dive into that one. But no, it's definitely interesting to me, and I look at our culture, I wonder as well, is that really, I don't really see people with extra time going into the arts and learning and growing. I mean, on a large proportion of the population standpoint, have people that enjoy reading and writing and learning new things, music, but it seems like the majority of people just want to sit and watch Netflix all day. So is that what people are gonna do? So it does create some concern there, but maybe people do that because it's just like, I don't have enough time to really, that's my only time to really do that. So like when I get home from work, I just want to binge for two or three hours, and then next thing you know, it's time to go to So yeah, that's definitely interesting to think about some of that. And really, it brings this question of like, why, really early in the show, as far as why are we even using AI? I mean, is that the end game of this, of this technology at large? And then maybe start thinking about how that fits into what you do. But is this the why of technology is so that we can have more free time or why do we want to have AI?

10:51 - Daniel
So I would say for the major players in AI right now the Silicon Valley big tech players this AI revolution or golden age whatever you would like to call it whatever they want to call it it's a money-making opportunity at the end of the day you can create this technology that is going to automate what a lot of people do and take away jobs from what people are doing and from that they are providing a value, they are providing a service, and it's an opportunity for them to go ahead and make money and monetize this technology that they've made. At the end of the day, we do live in a capitalist society and people do need to pay for their quality of life, their standard of living, and there's always this economic competition and battle that is going on. So I think for like the original why, like why did we create this? Well, why did we create the internet? Or why did, I don't know, why do we create any technology really? It was one, to increase the quality of life, but then also it's There's a monetary aspect of it as well that people want to financially gain. And I think that you're seeing that here within the AI space with a lot of these startups and the funding that goes into them. They're multi-billion dollar, almost trillion dollar funding opportunities that are occurring in the space. And people who are in that space obviously enjoy that because they're able to profit from the technology that they're trying to create and usher in to society. I don't find myself in that same basket as to what my why was to get into the industry. Here at Ferris, we're not bootstrapped, so we never asked anyone to fund what we were going to do here. For me, I saw a technology that was extremely powerful back in the early 2020s now. And I was 24 at the time when GPT 3.5 came out, and I saw the potential. At this moment in time, I realized that, hey, this isn't good enough to be an engineer, but if you gave it years, or 15 years, or 20 years, and I'm now a structural engineer in the middle of my career, what does this technology mean for our industry? And getting Silicon Valley to make that decision for me really did not make me feel well, and I couldn't really sleep well at night. I found the technology, one, just fascinating and interesting, and I do have a bit of a computer science background as well, which is where I kind of started to tinker with this technology, never even a lot about starting a company. But as you started to talk to people building in the space, and they were backed by these large technology companies, and you see what they're building, and you hear about their vision of the world, where the cost to compute is going to go very low, and the cost of the services that we create as engineers are also going to be diminished, and they're hoping that it goes to zero, so they no longer have to pay people like yourself and I. I didn't really agree with that ethos for a multitude of reasons. One, I think that's a very slippery slope from an ethical perspective. And then also I think there's a real beauty in human beings and our creative nature and our problem solving nature that I don't think AI has been able to solve to this date. So pairing that all together, I really felt like it was important for me as a young engineer at the time to build the future that I wanted to find myself living within. So I felt like it was an opportunity for me to put together an AI platform at the time and make that a commercially available product and then partner with engineering firms and now contractors and other people within the industry to try to go ahead and make a product that's going to enhance and augment the engineer, not replace them. It's kind of what the perspective that we take here.

14:14 - Andy
Yeah, and I respect that approach to what you're proposing for how, at least right now, with what I'm seeing AI being able to do, it seems like a very realistic goal to that augmentation approach as opposed to maybe, I mean, I think we'll get there to take a larger step, but this seems to be an incremental step right now, this augmentation step. But let's go ahead and get into that a little bit more. I mean, we're kind of in it right now with what you do. So just explain a little bit more about what is It's called askferris.io, but the app is called...

15:03 - Daniel
We just call it Ferris. We try to personify it in my way. It's like a partner that you would be working with at work. When I originally started, one of the main pain points I was trying to solve as a structural engineer was, okay, I've got this large database that I work with every day, whether it's in the cloud or in a locally held server, and getting into that database and out of it with the information that I needed was very frictionful. Someone would ask me, hey, what was the for this beam design here in Shear. And I would have to ask someone, well, what folder is that calculation even saved in? What calculation package is it part of And a lot of times people didn't know. So it was just like randomly clicking through folders and files until you kind of stumbled across what you were looking for. So for me, it's not, that really felt kind of antiquated. And I worked for a few firms and I worked at some mid-tier structural engineering firms. And then I went to go work for a rather larger one with Bechtel. And I saw that same common denominator everywhere I was. So I was like, maybe this is a problem that not only I have, but other people in the industry might have. So I put together what at first wasn't even called Ferris. It was just like some backend computer code and I plugged it into a database and I was able to speak to it as you would speak to chat GPT, but you were able to go through all your project files and pull out information and then we provide links so you could see it and things like that to try to get more information to the engineer. So that way when, I don't know, maybe you're in an OAC meeting and someone's asking you a question about your design, You don't know every piece of information for every project that you work on, but by having this assistance with you to go curate that information for you, you're able to go ahead and pull more information faster, and it helps get people from A to B in their design process, and curating information is really valuable. Another issue that we saw was when I was working, a lot of younger engineers would have questions all the time. They would wish they had a senior engineer to kind of handhold them a little bit more and look at their calculations a little bit more, before they send them out for bid or RFI response or building department responses. And the environment that I was growing up in is the senior management didn't really have a lot of time. The bandwidth of the firm itself, multiple firms that I worked with was very limited. So when engineers were putting together calculation packages or drawing packages, they didn't feel like the QAQC was the best that it could be. And they were very nervous about the work that they were putting out because as a young engineering student, like a couple of years out of college, and you're asked to design multi-story high-rise constructions, projects and you're like, I don't really know if I had done everything right, because you just don't have the experience to truly understand if what you're doing is proper. So we have like a verified calc agent that will go through your calculations and point out any mistakes that we can find within there, as well as like another layer of QA, QC that firms we work with really seem to like, The third agent that we had come up with is a submittal reviewer. That's a very broad term in our industry, but we got all types of similar reviews, like steel shop drawings, concrete mix design reports. And what we were trying to do at first was just automate the process. So, hey, we're looking at a concrete mix design report. These are all the logic checks that we need, water to cement ratio, aggregate size, compressive strength, any add mixtures that are needed. And let's make sure that per our drawings, it matches the mix design that we're getting. You can put two documents over each other and cross-examine them. And then the user puts in a recipe or a prompt that we help them create. And then the artificial intelligence program will go ahead and perform that check and that review for you. When I was working with an old manager of mine, he was like, Dan, this is great. You're really trying to automate these processes, but this is how the younger engineers learn. And he was totally right about that. Like when I was a young engineer, how did I learn to check a steel shop drawing? I had to do it. They didn't teach me how to do that in college. So part of our seminal reviewer right now is the engineering firm comes up with an SOP and these standard operating procedures. Have the logic checks in there, but they also have educational pieces as well. So that way, when a younger engineer is using Ferris, it's not just giving them, hey, this looks good, this looks bad. It's educating them on why it looks good. And hey, if it falls out of tolerance or something doesn't look according to our standards, this is why we have these checks in place. And it educates that younger engineer as well. That's another sort of ethos that we have here. Being a younger engineer, I'm very worried of our industry and my generation in general using AI is like this crutch and it gets in the way of us developing like this really important problem solving part of our brains that I think we all need to go ahead and effectively think through problems and problem solve them effectively. So what we're trying to do here is educate and create an environment that fosters like curiosity and education as opposed to just automating blindly and just clicking through buttons to finish a task. The last agent that we have right now is a template generation agent. So in our future, there's a lot of design guides that you may have that are in PDF form. I know we work with a client who's got a bunch of hand calcs that he's been curating for like 40 years. And when someone has a question on, hey, how do I design this specific condition? They'll hand off that design guide or they'll hand off a binder and they'll say, hey, I designed something similar like this in the past. Give it a look and try to replicate it to your condition. And what we're doing is we're turning those pieces of data, those static pieces of data into dynamic outputs. And those are functioned Excel sheets. So what you'll do is you'll take a hand calculation, it'll turn it into a dynamic spreadsheet that now your entire team can go ahead and use. And you could edit and you kind of turn it into your own template the same way with a design guide. If you were to have like, how do I design this base plate? And you're looking at an AISC design guide, like design guide one, you could take a snippet of that, throw it into Ferris, and then it'll generate a function Excel sheet for the end user. We've noticed that it gets engineering calculations from about zero to 50 to 70. It's never absolutely perfect. But that whole template generation process and really that final QA, QC then falls on to the responsible engineer, and it makes them look at the calculation, what's happening, what's going on, and they throw diagrams and things of that nature in there. And that helps provide value to the engineers, not having to sit down and spend 30 minutes just getting the template to look how they would like it to look in the original form. So we are building that out. For us, right now, you could find us on a website We don't believe that's where the future of all this is going to go. I'm sure as a business owner, you look out to all the different types of applications that are out there and it's probably nauseating and overwhelming to try to like identify them all and like understand them all and like see what they're doing. And I'm not numb to that here. I totally understand like we're just another web-based application that's out there that's promising to help you. What we've been building out and where we're moving here into the future is we want to be able to push value to our end users as opposed to pulling them to our platform to get value. So what does that mean? What does that look like, The first integration that we've rolled out here past couple of weeks is we've integrated Ferris into your teams and into your Slack. So that way you could like at Ferris within Slack or within teams the same way that you would a person. And think about like having like chat GPT in your teams, but chat GPT has been trained on like all your server data. So that way, if you're in like a meeting and you're talking to someone like, Hey, like, do you remember like where we saved this calc or like what does you was, can we change the size? And no one knows. Instead of nobody knowing, you could ask Ferris in that same chat, and then it could bring to you some information to have a little bit more context when you're trying to make design calls and your client facing and things of that nature. We see ourselves living in every sort of software that our engineers use on a daily basis. Again, we have a web-based platform, but we really don't want engineers to have to go there besides linking up all the software that they're already using. And then we're going to act as an intelligence layer that's connecting all these different software applications that you're using on a daily basis. That is our goal right now here.

22:33 - Andy
I mean, you've given me a lot of really good information there. I do want to ask a few follow-up questions as far as really, number one, I want to maybe clarify one thing. Is this just for engineers or because this is more of an at large. So, is there any architecture or contractor that or just purely, hey, I'm an engineer design?

23:01 - Daniel
So, we started and since I'm civil structural native, it started with those are our main focused customers and clients. Since then, we branched off to general contractors who really have like an appetite for this type of automation and technology. So, we are not specifically for civil structural engineers. The software that we're designing and we built to date, it's really runs well when users provide their own data sets. So depending on the data set that you're providing as the end user, Ferris adapts to that data that you are providing it. So the more data that you could provide it, the better that it performs for the tasks that you're looking for it to perform. That's why we're pretty adaptable between all areas of civil and within the general contracting space as well at the moment.

23:44 - Andy
Yeah, what about architects?

23:46 - Daniel
Yeah, an architect could use it. I'm going to be honest with We don't have any architectural clients at the moment, so I haven't worked with an architect yet. But again, any sort of data set, drawings, calculations, specifications, product data sheets, whatever data you're working with, you can go ahead, put that onto our platform, and then have access to the tools that we're using and then get value from that data that you're sitting on.

24:08 - Andy
Yeah, because I mean, one thing you might argue is that, I mean, I think architecture is a perfect example. Kind of getting to our why because we really don't want to be fooling around with these submittals and specifications and most architects that I know want to design, you know, and really engineers even, they want to design. They don't want to necessarily do a shop drawing review. I mean, I do have one engineer that actually likes doing stuff like that. He really loves red lines and shop drawing reviews and specifications, so, but A lot of people just want to do design buildings or whether they're architect or engineer. So I think an app like this can help you with that. You can have it help you augment, whatever you want to call it, and speed up the process. So that answers that question. But I do want to go into another aspect. And by the way, we do have some architects and contractors listening, they may not know what a UR is. Also, other engineers may not necessarily know. I mean, I call it a UC, a uniformity coefficient, but I guess that's what you're referring to, right?

25:24 - Daniel
Yeah, the utilization rate on a beam, so like the capacity that you had within that beam.

25:29 - Andy
Yeah, so basically, it's an engineering nerd thing, right? Sorry about that.

25:33 - Daniel
I'm still an engineer. No, no, you're good.

25:36 - Andy
I'm just trying to bring my architects and contractors in for this a little bit. Yeah, sorry about that. So, anyway, I want to go to another question about, you know, comparing the, you mentioned, like, comparing this to other apps and other things, which certainly we've done. I mean, we want to look at the whole landscape. And then, obviously, you have free versus paid. And then you have, you know, the obvious, you know, ChatGPT Pro and Perplexity Cloud Pro and Gemini Pro, those type of things. So Google Notebook, LLM. So why do we need a Ferris type thing if we have perplexity or Claude or something like that?

26:25 - Daniel
The original problem that we set out to face was, okay, why are these platforms hallucinating at such a high clip? And for those that aren't aware of what a hallucination is, it's when a large language model goes ahead and provides a piece of information that's not factually tied. So it's a lie or we call them hallucinations in our industry. As civil engineers are just working in AAC space, there's this desire to be as accurate as possible. That's kind of a vague term, but as a structural civil engineer, we need to be accurate to the decimal point. So these hallucinations are very costly and they really hamstring the value that these tools provide. One of the reasons why these large language models hallucinate is a technical term called the context window. And what the context window is, is the amount of data that these large language models are looking at and referring to when answering your question. When you're looking at like a commercially available like ChatGPT or Grok or Claude or Perplexity, those are typically like 128,000 tokens, which is about 100,000 words or maybe 300 pages of PDF. When you're looking at your servers, when you're in the AEC space, you've got tens of thousands, if not hundreds of thousands of pages of PDF that you want semantically search sometimes. And using these traditional large language model providers, you're not given that larger context window, which is required to go ahead and look at all this information at one time and answer your questions. Now, we were able to solve it technically here, and the ability to solve that is a compute issue. So like you can increase your context window if you want to spend the money on the compute to increase the context window. When you're dealing with a lot of these Big tech large language model providers. You're not their clients. They don't really care about what we're doing here in AC. They definitely don't care about what we're doing here in civil structural engineering. And that's kind of where we differentiate ourselves at Ferris is that is our clients. That is our customer. We design specific tools and applications for civil engineers, people in the space that are designing and building on a daily basis. And a lot of times, like the Microsoft's of the world, the Google's of the world, the opening eyes of the world, they're not building their products. Products with you in mind. And that's why a lot of these technologies that we've been using throughout our career, whether it's data management softwares or any sort of like a large language model provider software is you're kind of like having to conform to what you've got as opposed to someone building you like custom craft design softwares. And that's kind of where we find ourselves making a little bit like a niche in the market. It's where we find general contractors or engineering firms They want like a tech team. They wish they had a couple guys internally that can go ahead and start like crafting up different types of apps and widgets and software tools for their team to use, but they don't really know how to do that. They don't know how to hire that talent or where to find that talent. And what we've been doing is partnering with firms and kind of being like their tech arm. And we work hands in glove with them to go ahead and build them platforms and tools that they're asking for. And for us, we find It's a really good partnership because we understand their problems. As you know, in our industry, depending on the project that you're on, depending on the team that you're on, workflows change. They're not the same thing cut and dry every project, every time that you're trying to do it. And because of that, the tools that you're working with, they need to also be able to adapt. But if you're working with a Microsoft, how are you going to go and ask them to kind of change their large language model for whatever specific piece of information you're working on or a specific workflow that you're working in? It's really hard to do that. But when you're working with us here at Ferris, you could just email us or chat us and we'll go ahead and we'll start working on that immediately to make our platform work to you and your projects and what you're seeing on a daily basis. So we are offering a much more like custom boutique software development opportunity right now here at Ferris as opposed to just like an off-the-shelf big tech product that you'd get if you wanted to use that.

30:15 - Andy
Yeah, that sounds like a fair response. And so many different directions to go with that too. So, you know, I'm thinking about like going back to the architect or even a structural engineer and contractors. One thing we all have in common is that we all are subject to the building code. So, like doing a building code review, the IBC, International Building Code, or AISC is the Steel code and one of the things I mean we've we've demoed your product so I know you can upload some of these PDFs and things to Ferris and basically ask questions I mean I think that's one of the big benefits of it is like a code researching tool whereas I mean can you just do that with notebook LLM and just upload that I mean what would be the benefit of Ferris versus something like that Is it the same thing you were talking about with the tokens?

31:17 - Daniel
Yeah, the context window is definitely an issue. I think what you're going to start seeing become more of an issue here in the future is people are going to get very protective over their data. All of these code issuers, they are realizing how much value they're sitting on with their design codes. And they realize, wait, we can't build things in reality without our codes and our standards. And why does ChatGBT or why does Notebook LLM get to use that and get value from that and we see nothing in return. I can tell you personally, I know because I've spoken with a lot of these code issuers, they're getting very wise to this. And I don't know if you're going to be able to go ahead and just ask ChachiBT, hey, what's in the ASE 722 for free anymore? I think that was like a very Wild West thing that they were able to like web scrape. And it's not even just our industry. It's kind of like media and content at large. A lot of this information that was used to go ahead and create these large language models, they were kind of web scraped. And some would say stolen, some would say that they were just open source because they were on the internet. I'm not really here to decide that. I'm not a legal expert in that field. But a lot of people are going to get very protective over the data that they are allowing to be issued. I know the ASTM right now, for their current codes and standards, you're actually not allowed, quote unquote, I'm not sure if they're encrypted or not, but to access and upload them to a chat GPT or to They are actually saying hey because they're worried about their data getting stolen and they realize hey if our data is getting stolen and we're not seeing any sort of revenue for this like what does our revenue model look like if all of our information is scraped and no one comes and buys our guide or standard anymore. They're having that kind of existential crisis right now which I think you're going to see for like the next five years is going to be some major wars over like who's owning this data and where does this data come from and who has the access to it. So here at Ferris what we're telling people is we do not train large language models on your data. We do not take your data. We do not steal your data. It's actually very safe and secure. All of our vendors and ourselves included, we're SOC 2 compliant, which is like the highest level of data security in the industry. Some of our clients, they have Department of Transportation work and Department of Engineering work, Department of Energy work. And for those clients of theirs, you need to have like that SOC 2 level of compliance. So we provide that. And what we're offering to our customers and our clients is one, you're not vendor locked. So if you were to go use like Gemini or OpenAI's model, and you start really building a rapport with that system, you're kind of locked into that ecosystem. And the minute they want to change terms and conditions, or they want to, maybe another company comes out with a better language model and you want to use it, but you're already vendor locked on ChatGPT. Well, here at Ferris, we're able to get access to all these publicly available models, offer them to our clients, and there's no risk of them getting their data stolen or siphoned away. And I know people are very happy about that. So kind of a roundabout answer as to kind of like, why would you go ahead and be with us and not with them? But those are some of the things that I'm seeing right now.

34:15 - Andy
When you say model, you're talking about like, like chat GPT as a model.

34:20 - Daniel
So chat GPT, they just came out with like their GPT-5. I know Gemini's flagship model right now for Google is the 2.5 flash. I think it's Grok 4 is like their latest model. It's just this, this software system that talking to and communicating with. And depending on what data that was trained on and the different types of training techniques used, you get better responses for different types of questions and things that you're giving to it.

34:44 - Andy
And you're able to tap into each one of those?

34:47 - Daniel
Yeah, we can. Anything that has a publicly available API, so anything we could pipe into and out of, we have the ability to go ahead. If a user really wanted to use a specific model, we would build out something for them, say, hey, you could use this specific model for this specific task. Something we do internally as well as we benchmark different models against each other. So that way, you don't have to do that as the end engineer. So if you wanted something that was going to go ahead and try to do a deep research on, I don't know, your general notes and your company SOPs, how do you know that Grok than ChatGPT 5? Well, you'd have to benchmark that for yourself. Internally, we do those things, and we can provide data to our customers and our clients so that they make the best decision on what models they're using.

35:30 - Andy
Now, you mentioned about agents, and I've done some searching and researching about agents. So, you mentioned you have four different agents. So, some of us are still getting up to speed on even what an agent is, you know, because, you know, first, you got Chat GPT is kind of like the cornerstone of AI right now, but that's different than an agent, I think, right? So, can you explain to us what an agent is and how that works?

35:58 - Daniel
Yeah, for sure. So when people go and their first exposure to artificial intelligence in its new breath is just this chat interface and this chat bot where you ask a question, it provides information, you give it a prompt, it provides an answer. What an agent is, by definition, it's a piece of software that automates a task from a start. So you give it a piece of information, you give it a task to run and a goal to complete, and it completes that fully and ends with the that specific output without having any human intervention along the way. So the word agent, it can be very buzzwordy at times. And I think it's very much like a marketing thing. I did not come up with the term agent. It's just the industry kind of standard and terminology that's been used to define a piece of software that completes a task for you. What we're using that term agent for right now is anything that completes a workflow for an engineer. So like, for example, our template generation agents, the workflow that it's completing or that it's doing for a user is, hey, it's taking this hand calculation, it's turning it into a functioned Excel sheet. There's no human intervention that occurs from you uploading the images of your calculation to when you can download that piece of Excel software that you're getting. So really, it's just this automated workflow that people are using, this word agents to describe it with.

37:15 - Andy
Yeah, OK. Yeah, I need to check that one out. That sounds like a really good one. I mean, actually, all of these truth sound really helpful. And part of the problem is, as you mentioned earlier, I'm a business owner. We get inundated with different ideas. So, you know, that's why we're having you on today is we're trying to learn about these things and also want to share that with the enhanced community, you know, this software and your insight into AI at large.

37:46 - Daniel
Yeah, for sure. There are so many people out there building incredible tools building incredible agents, building incredible applications, and many of them are our competitors. We've got numerous competitors, I would say 50 plus, that are out there trying to build things that are analogous to us. They tweak it a little bit here, it's a little bit different there. And for the end user, for the client, how do they determine which one is the best and which one should they be using? For us, we want to provide the most value to our customers and to our clients. And that's why we're kind of working towards being more of this niche boutique software design firm. Because building that relationship with our customers and with our clients, we're able to truly tailor our products to their workflows and to their problems, which I think provides a lot of value as opposed to just like downloading a piece of software and trying to figure out how to use it internally.

38:35 - Andy
Now, I want to appreciate you sharing about that too. And I want to change gears a bit to one of the applications that I was thinking about earlier today. Because I've got a few hardcore listeners of the show, and so I have to keep them happy, right? Of course. But one of my listeners, he actually is in forensic engineering, and so if you're listening, you know who you are. And so this is a subset of your market, really, forensic engineering. So I don't know if you've dealt with that before, but basically taking reports and asking it questions, you know, like, so he was saying like, hey, what are some good applications for that? I was wondering, A, is this a good application for that or do you have other ideas that this person could use or a forensic engineer could use for that type of thing?

39:39 - Daniel
Yeah, so we do work with some forensic engineers Taking those reports and asking it questions to understand what's inside of them is something totally within our realm here at Ferris that a user could use Ferris for. Another agent that we had built off for our clients was taking your existing inspection base. So as a forensic engineer, you're going out outside a bunch, you're looking at the existing condition of a structure, you're formulating a report, and that's your products here and clients. How do we kind of automate that process? Are there ways that we can go ahead, take your existing reports and then have an engineer just upload their field notes and then have Ferris output an actual full report that an engineer just needs to go through, Read, make sure it sounds good, make sure it sounds like what they want with the specific pieces of information from the job included. And then they could also hand that off as a deliverable on their job. That's another way you could use this. I would say another application that I didn't necessarily foresee when but engineers are using it a lot, is some of our forensics engineers, they're in like the New York City building market, and a lot of their projects are in buildings that are like a century old, if not older, and these blueprints are scanned documents from when people were using drafting tables, before AutoCAD was even like a second thought, and there are times where you open up these PDFs and there's hundreds of pages of like the scanned blueprint information, and you can't even use the search feature within like your Adobe Reader or Bluebeam, and you're kind of just like muddling your way through these drawings. Here at Ferris, you don't have to do that anymore. We use two types of data extraction. One is that metadata that if you were to use the search feature, it picks up on that and provides responses to you. We also do a visual analysis of your documents where we're taking pictures of it, and we're using visual algorithms to understand what those pictures mean. So that way you're able to go ahead and semantically search a thing that was hand-drawn or something that might not be backed with metadata. And the forensics engineers really like that because they're able to get through these really old drawing sets and find specific details or find specific notes relatively quickly as opposed to just flipping page by page reading through a whole drawing set. So those are a few ways that we've been helping some forensics engineers.

41:50 - Andy
Yeah, it's really fun to go through some of those old drawings and see how everything was written. And I remember one time I was looking at some older drawings and it was like one of the beams was a W, I don't know, 18 35 and then all the other beams in that progression they were joist actually it was like do do Do you know what do means on a draw on an old drawing? No, I don't know drawing It means ditto that means it's the same beam. So it's the same size. And so I Was like what the heck is do you know? It took me forever to figure it out, of course that back then just Google Eventually I figured it out, or maybe it was like a, I don't know, a list server that's stating my technology. But I figured it out somehow, it just means ditto. And so, but like, now with this, you can interpret some of these older, and you should have, if you have an abbreviation, one of our things is, if you have an abbreviation on a drawing, you should have an abbreviation list. So, that's my rule of thumb. But, you know, back in the day, it was like, well, that was industry standard. You know, it didn't.

43:05 - Daniel
It's so funny how things change like that.

43:08 - Andy
So, but I didn't, but, you know, back then, I mean, now you just copy paste W18 by 35 all the way across, or you have Revit to tag it, I guess. But anyway, so it's fun to see how things have evolved. And, you know, all the way from hand drawings to now we have AI reviewing have 2025 AI reviewing those hand-drawn drawings. So what are some other, I guess at large, outside of Ferris and AI, what are some technologies you're seeing in the AEC industry that if perhaps you didn't go into AI, what would you have gone into?

43:50 - Daniel
Wow, that's a broad a broad list. Wow, there's so much incredible technology that's out there right now. I think the robotics aspects of what's going on right now is very interesting. There's a company called Figure. They're kind of a competitor to Tesla's Optimus robot. I'm not sure if you're aware of what that is, but it's a humanoid robot that's going to do housing tasks for people. I think that's very interesting. It's really hard to replicate physical movement in robotics, which I find pretty funny because we're able to replicate, like, thought process and memory a little bit easier than that, but I would say it's probably simpler for us to, like, move a cup on a desk as opposed it is to, like, have the ASC manual, like, recalled verbatim in our heads. But for whatever reason, that's just the way that things have played out. I think robotics is very interesting in this time. I've also went down a rabbit hole of looking at the supply chain of these, like, I guess chips now is, like, one of, like, the most highly debated and talked about pieces of technology out there and there's like this whole war of going on with these semiconductors and these chips and the amount of data that goes into it and the amount of energy that goes into creating these semiconductor chips and like why are they only getting made in Taiwan and then go down a rabbit hole of like how they're actually made these these actual like chips and how they're made it's incredible like they have to create a different type of light it's like a different non-visual type of light to imprint on And how they do that is they take liquefied tin, and they drip it in a stream, and they shoot a laser beam through it. And when the laser beam goes through the tin, it changes the frequency of the light. So that way, it imprints on the chip in the proper way. And they're doing this at a speed and a scale equivalent to landing a 737 jet on a piece of runway. Like if the back tires were to land at a specific spot, it'd be like a playing card. So the tolerances to these things are astronomical, and they have to do them because if any dust gets in the way, it messes up the whole procedure and process. So I think like when you look at this chat GPT and this technology that's out there, for me, I kind of asked myself, like, what went into building it? And like, how did we get here? And I looked at like the chip manufacturing side of it. And that's pretty incredible. But to be honest, like I was a structural engineer when I went to school and growing up, I always wanted to be a structural engineer. And I guess now I'm developing software, but I never thought my life would be here. So I said, if I have to go back, I'm going to go back to and a structural engineer, as long as someone needs me to be there. I think it's one of the coolest and most fulfilling jobs that's possible. I love the profession. I'm incredibly grateful to have the opportunity to have even worked within the profession. I think the thing that I love most about it is we live in a very trustless society. People have conspiracy theory about everything nowadays. But when it comes to buildings and bridges and tunnels, I don't really know a lot of people that are like, yeah, I'm not going to go over that bridge I'm like an anti-bridger and I don't believe like the math and science that went into like building the bridge. I find civil engineering to be like a very hard truth layer in a world where truth is like ever more in question. So I really do appreciate that about the profession. And if whenever Ferris is done and I don't need to be doing this anymore, I'm going to go back to the building, like building high rises and bridges and tunnels and things of that nature.

47:02 - Andy
It's really fun. What was an interesting project you worked on? I mean, I don't know how many, I think you worked in the engineering actual design for a few years. But would you work on any cool projects that you're willing to share?

47:16 - Daniel
Yeah, for sure. When I got out of school, I was down in South Florida. And it was during COVID. And we were doing a lot of high-rise design. So we were doing a lot of cast-in-place concrete, post-tension slab design, which I thought was pretty fascinating. We were doing a lot of finite element analysis on slabs, how they deform, pre-stress, post-stress, things of that nature. I thought that was really fascinating. The coolest project that I probably worked on was I was up in Cape Canaveral for a few years as a structural design engineer. We were working with Bechtel and NASA on the Mobile Launcher 2 job. So this is a giant steel tower where the Artemis SLS, the Space Launcher System, sits on and then launches off of When I got there, I mean, working in like residential commercial, all your loading for the most part is in PSF. And I go to this new project, and all the loading that we're talking about is now in PSI. So it's like an order of magnitude, two orders of magnitude. More intense. And it's like, wow, like the members that we were getting, the member sizes, the steel that we were getting, the welds that we had to classify and test. It was just some of like, it was like building like a bunker is the best way that I could describe it. And then you had to design for thermal loading, for acoustic loading, for vibrational loading. So it was really intense and you're designing in every degree of freedom, every axis of possible rotation that is getting designed for. And the cool part about that job was the mobile launcher, it's mobile given the name, and they roll it out from the VAB, the Vehicle Assembly Building at the Cape, and they roll it on to like Launchpad 39A or Launchpad 40. And in order to do that, there's a weight constraint. So typically in our industry, when a connection doesn't work or a member is failing, we just like beef up the size, we make it bigger to resist more. When weight is a constraint, you can no longer do that. So you have to come up really like creative ways to like brace at specific instances or utilize different types of connection details. So that was also like a really unique crash course into looking at solving these problems in a different light, as opposed to just like beefing up size of members.

49:19 - Andy
Thanks for sharing that. Sounds like some really cool projects, Daniel. I mean, I've never really worked on jobs quite that big or significant before, so. So that's really interesting. I mean, we're more of a boutique engineering company, you know. But nonetheless, we have to have those retail buildings and hotels and houses too, I suppose, right? Yeah, of course. So I wanted to get into your why, of course. There was one other aspect of it. Why don't we go ahead and jump into that? That was one other aspect I wanted to get into as well. I think we hit on this some already as far as what drives you, what's your why in terms of Ferris and what you see for your vision for the AEC. Is there something that drives you Or is it just, are you just like Silicon Valley, and you just wanna beat them to the punch, so to speak?

50:33 - Daniel
Well, they've already beaten me to the punch, I would say. I'm probably dragging a little coattail here, but I've only been doing this for six months, so it's a little bit new. No, for me, when I wanted to become a structural engineer, of all the cool things that you could do out there, why is that the thing that I really wanna do? My why is kinda unique to me, and it's a little bit, I guess, heavy at times, It's really why I do it. It's because when I was a kid, I was growing up in New York City, and my father was a fireman. He passed away on September the 11th in the World Trade Center. And when you grow up on the internet, I was an internet native kid. I was born in 1997. You go and start reading all these crazy conspiracy theories that are online. And you're like, what the heck happened here? You're reading all this crazy stuff. And for me, the only way that I could really figure out, OK, is what I'm reading online real? Or is the commission real? Is the NIST report real? I don't even know what any of this stuff means. So I started getting into it. Buildings designed. I thought I wanted to be an architect until I got to like middle school and then I asked myself like hey like what goes into the actual calculations of making these buildings stand and resist all these forces and then I realized oh my gosh like a structural engineer there's an actual person that's like designed the entire structure and has mathematical calculations to back all of it up. So when I went to Rensselaer Polytechnic Institute that was kind of like my driving force as to why I wanted to study civil structural engineering and why I got into into the trade originally. I think that like I was saying before I think civil engineers have a really unique position in society where we built like the Commonwealth of all society on and in order to build that you need to come to like a truth of like what can stand and what will not. I think at its core that's kind of what we're doing here and that's really important for society and it really fires me up that we get to be that that level of truth-seeking. And then as I started to move more into like a tech native background I was getting really worried about the future of the profession because I I started to see people talk about civil engineers and structural engineers as like people that are going to be replaced and their work was just going to be all done by this AI, this AGI, this artificial general intelligence that was coming. And for me, I was really worried about that because again, who gets to determine what the AGI is? Who determines its biases? Who determines the decisions that it gets to make? And I think one of the beautiful things about our field is that you need to have a seal and you need to be able to prove and prove to the world that you have a relatively logically sound decision-making apparatus that you're building society and structures out of And now if we were to go ahead and kind of replace all the PEs that are out there with AI, is that a world that I want to live in? Personally, I don't. So my why as to why I started Ferris was, hey, can I augment the existing civil engineering industry so that way when someone comes out, because one day they will, there'll be some robot out there that's going to try to sign and sealed drawings, can we compete in the marketplace with that AGI that's coming, supposedly? I'm trying to build something that will help us combat that here in the future.

53:28 - Andy
I mean, I really appreciate you sharing that because, I mean, I got chills when you were telling me that. I had no idea about that back story, Daniel. So, yeah, thank you for sharing that with us. Sounds like an amazing journey that you've been on. So I can definitely see there's a significant why in your career and why you're not just trying to beat somebody to the punch, so to speak. And really, I think that one of the things that it got me thinking about about today is people like myself, I've been doing this 27 years, okay? And it's hard. This is a hard business. And there's a lot of things about it. And when I was doing it, so I worked sole practitioner for a long time. I worked for other people for a long time. It feels like a long time. And there are parts of it I love. I mean, I really do. I love the idea of structural engineering sometimes, you know, like the equations and the math like you're talking about and like pre-stressed concrete and the science of it. It's really interesting, right? And like thinking about the bending moment on the beam and how the section modulus and the stress and I love that stuff. But then you get into like I gotta do calcs for eight hours and I gotta do Read lines and you know and then guess what they change it, right? They're like, by the way, we want the joists to go the other way like it was going north-south We want to go east-west or we want to change this Can you change it to like a 10 inch deep instead of a 12 inch or something like that? And then the and then you draw it in the arc in the contractor So and then that's not even the all the other things right because something didn't fit right because we made a mistake And I'm thinking, well, AI theoretically can help us with a lot of these things, right? I mean, we call it our system AIM, and it's eliminate headaches. And all these that we've just described are really just headaches. It's the effort, it's the undue effort, right, of the engineering. And the fact that you gotta do a pencil calculation, then you gotta redo that pencil calculation. And so forth, but now you've got a technology that can take that pencil calc that you've been storing and put it into AI, or you can QC your drawings and find maybe an error that nobody thought of So, I mean, I guess, any thoughts about that or feedback on any of that? Yeah, for sure. On that idea that eliminating headaches?

56:25 - Daniel
Yeah, for sure. I think that there are so many headaches that you see and I mean, you know better than anybody, how many decisions do you need to make every day? Thousands, tens of thousands of decisions that you're making. And there is decision fatigue when you make all these decisions. Sometimes you do make a mistake because we're humans and sometimes you can't make the right decision every time it just happens. And I think what's really unique about AI is I don't think it really reasons like a human reasons. So it's not gonna be able to take like a problem that you're seeing for the first time and like really understand it and solve it very well. Current application, but I think what it's really good at is becoming a force multiplier for an engineer. And what does that mean? It takes your engineering judgment and your experience that you've curated over 27 years of being an engineer, and when you're looking at a condition like, hey, I want to do a QA QC on a drawing set, what is that lessons learned sheet that you're going to be checking that drawing set against? Now, as opposed to you sitting down and having to look for each one of those conditions throughout all the drawings, we can give it to a Ferris or or any sort of AI system at the moment, and you can give the drawings, you can give that lessons learned sheet, and you can say, hey, can you check these two against each other and highlight anywhere where these rules are violated? And now we can do that at scale, and your engineering judgment and your experience is now leveraged and force-multiplied with the press of a button using something like Ferris.

57:45 - Andy
Yeah, I mean, that sounds amazing. And I mean, now with, you know, pretty much we do office hours and we go over jobs. We're a remote company, so when we go over jobs, it's Read AI is the tool we use, and it's on. I mean, I just like, I'm just going to record everything now, and I'm thinking, why not just go ahead and apply that information to the lessons learned, and have something like Ferris say, here's Andy going through a live job, and not one live job, but here's all the jobs over the past year. And you can see like, okay, this is why we don't use that type of beam in that typical situation. It's because, or this is, you know, I said it a hundred times, we got to galvanize steel when it's outdoors. And we forget about that. Like, oh, we forgot to call it out or something. So anyway, I mean, I think those are some things that we can learn from So, well, Daniel, I've taken up a pretty good amount of your time today and I appreciate it. I want to give you an opportunity just to share anything else maybe that was on your mind that I didn't hit on. So was there anything else that you wanted to mention?

59:06 - Daniel
Yeah, this is going out to everybody in our industry. The data that you're creating on a daily basis is more valuable than any of us truly understand. And be very careful what you do with your data. Even like that Read AI example that you were giving where you were telling, hey, these were all the lessons learned that I have. I think we live in like our AEC bubble and like the people that we interact with on a daily basis, they understand this kind of stuff. But I think what I've been doing lately is getting out of this AEC bubble and by talking to like tech people, they have no idea what we do. They don't know how we do it. And any bit of data that you can give them, like what you just told me about having, hey, you need galvanized steel outside so it doesn't corrode. That's not something they think of And that's not something that's like intuitive and native them and that piece of data that you have now created from your experience as an engineer is really valuable. Now you say, okay, how is it valuable? How do I turn that into a monetized revenue stream? Doesn't currently really exist. Something that we are working on here is like the tokenization of people's data. So that way, instead of it just getting stolen, you can protect it. And now if you want to go ahead and hand it over to Silicon Valley, they're going to have to pay you something for it. It's not for free. There's no reason why we should be handing over all of this data, all of this all of your time and energy and effort that you've been putting into your drawings and into your calcs, why are we handing that over for free? So that way OpenAI could just train the next language model on that and now they no longer need you because they've taken your data and they've replicated you in cyberspace. So I just tell people to be very, very weary of what they're doing with their data. It's so valuable. And what we do in the AAC space is like the bedrock of all civilization. So I think we have the most valuable data that's out there and available. So be very cautious of that. And treat it like it's gold, because it is.

1:00:47 - Andy
That's great information to have, Daniel. And yeah, it sounds like we need to have some more conversation around that. I would recommend it.

1:00:59 - Daniel
I would look internally and say, hey, what are we doing with our data? Are we just uploading it to like chat GPT? Is that wise? I know that there are people, so just like I've been trying to gauge what the landscape of AI looks like in the AAC industry. And what I could tell, there's like a top 1% that is actually buying like GPUs, like processing units to create their own large language models. So they don't need the Googles or the Gemini's of the world. Again, cause they're really worried about their data and they don't wanna hand it over for free. I don't think it's really possible for everybody to do. It's really hard just to be a structural engineer. Now you're gonna ask me to be a large language model creator as well. Like it's a hard sell for sure. Then there's about that's like using AI as like a email polisher. They're using like a Read AI within their meetings and stuff like that. And then there's a vast majority. I would say it's like 65 to 70% in the industry that are really not interfacing with this technology at all. One, because it's not really made for our industry. So it's kind of like, how do I use this? That question definitely needs to be answered. And what we're trying to do here is one, educate the industry, but then also, like I was saying before, we come up with like really great data and Silicon Valley is going to make trillions of dollars off of our data. Why are we giving it away? For free. I think we should really have that conversation. I know a lot of the code issuers are having that right now, and I think all the firms that work in this space should be having it as well, too. So just keep that in mind.

1:02:19 - Andy
Yeah, that's a great point. And just one quick tip, I guess, for the audience, because we might have a few that are in that camp. I mean, what if I've never gotten, like, used chat GPT or AI? I mean, A, why should I use it? And where do I get started? Just download your app.

1:02:40 - Daniel
No, I wouldn't. I mean, I wouldn't just recommend downloading my app. We actually don't allow people to just download our app because we tried that at the beginning and we realized that our failure rate was really high because people didn't know what to do with it. So now what we do with our customers is we sit down and have like a white glove onboarding process where we sit down with them weekly. We talk about our platform. We talk about their problems. We talk about how our platform can be used to solve their problems. And we really handhold for a few months on how to like educate people and how to use it. Because I think education is probably the biggest curve right now. For the people that have never used AI, like, why should you use it? The best way I could describe it is when you're looking for information, like, in your servers or your drawing sets, I would equate that to, like, writing a letter and, like, sending it through the mail and how long it took to get from A to B. It took a little bit of time, depending on the postal service that you were using, and equate that to, like, how email is. Like, what's the value of me being able to go from point A to point B with a message? I mean, it was very, very valuable. Now everyone's email boxes are like overloaded and over-inundated with data, and it's hard to even like make your way through them. But I think that's like the initial value prompt is time and effort and energy that it's going to save you doing a lot of these manual mundane tasks. And I think it's something that you should really educate yourself on. And in order to do that, how do you educate yourself in that space? Well, you can obviously reach out to me. I definitely just do EDU on calls like this, like free of charge. I think it's really important for our industry. I'm not gonna charge you with an arm and a leg, just like teach you how to use ChatGBT or Gemini. But I think what I've noticed is people that, like a lot of our customers, they've used like Microsoft Copilot at an enterprise level and they got really bad results. So they're like, wait, we need like a more custom solution. And then they come to us to help us build it. I think that's kind of like the sales life cycle of how this stuff works. Like people become aware of what AI is. They use like the commercially available products and they realize this doesn't really work, but some of them see the value that if it did work properly, what they would get out of it. And then they end up coming to like a company like this or any of our competitors that are out there. And they kind of find the best one, or they're trying to find the best provider. So I would say ask a lot of questions, look for people that want to help you, because it's hard to learn all this stuff by yourself. And a lot of people that have tried this already have a lot of invaluable experience of doing things that didn't work. We've done a lot of things, they haven't really worked, and we've learned from that, and hopefully you don't have to make that same mistake in the future.

1:04:51 - Andy
Yeah, I like that analogy. I mean, you could be a naysayer, I suppose, but I mean, I don't see this technology going away. It's here to stay. And all these different technologies are here to stay. So we might as well learn how to use them and really be the forerunners, be ahead of the curve. I mean, the way I'm looking at it. So I'm doing everything I can as a company, as a company leader to do that. And I would, I don't know, I think you would probably agree that we should be doing that as company owners. We also have a lot of owners that listen to this podcast.

1:05:33 - Daniel
Yeah, for sure. I think it's kind of like your responsibility as someone that's owning the company. You want to steward the company in the best direction possible. And if there's any sort of technology that could help you do that, I think that you should do the time and take the time to do the due diligence to make sure that you're looking at all possible options when you're trying to steward your company.

1:05:54 - Andy
Thanks, Daniel. How do people find you?

1:05:59 - Daniel
Yeah, for sure. My name again, Daniel Calabro. I'm a co-founder now at Ferris. It's me and my other co-founder, Thad. He's more computer science native than I am. He's been helping me out with a lot of the development stuff as we get more advanced. You could find us at our website. It's askferris.io. And then you could find me, Daniel Calabro, on LinkedIn. I post there pretty regularly, not as much as Andy. Andy's kicking my butt in that respect, but he leads from the front on the LinkedIn side, but it's always good to see him. But you can find me there as well.

1:06:25 - Andy
Yeah, I'm just OCD, I guess.

1:06:29 - Daniel
You're a beast.

1:06:32 - Andy
And is Ferris, is that a, is that like a nickname? Is that an acronym?

1:06:37 - Daniel
Okay, so yeah, the name Ferris, where it comes from, I'm a big believer that we stand on the shoulders of giants.

1:06:46 - Daniel
Like all this incredible engineering that we get to do, we do it because people before us figured it out.

1:06:51 - Daniel
The school that I went to, Rensselaer Polytechnic Institute, engineering school in upstate New York, we have like two really notable alums.

1:06:57 - Daniel
One is Washington Roebling and the other is George Ferris.

1:06:59 - Daniel
Washington Roebling designed the Brooklyn Bridge and George Ferris designed the Ferris wheel.

1:07:03 - Daniel
So when I was thinking of a name, I wanted to name it after one of them.

1:07:06 - Daniel
Something like how like Elon named Tesla after Nikola Tesla in a way.

1:07:10 - Daniel
I think Ferris, it's like gender agnostic since it's the last name and it rolls off the tongue really well.

1:07:14 - Daniel
So that was the first reason why I chose it.

1:07:17 - Daniel
And now our logo is like a Ferris wheel and it's slowly developing like to be almost like an analogy and a metaphor of our company and what we're building is like Ferris being that center hub and then using like that spoke in hub analysis.

1:07:29 - Daniel
Every one of those spokes that go out is like another software application that an engineer uses on a daily basis.

1:07:34 - Daniel
And what we're trying to do is connect all of them and to create like that one centered brain here where all your technology is talking to each other and everything is in the loop and keeping the engineer in the loop of what's happening in all these different areas that we work in.

1:07:46 - Daniel
So it's kind of evolving into that.

1:07:47 - Daniel
That's kind of where Ferris is.

1:07:50 - Daniel
You're really building on that, really building on that analogy.

1:07:52 - Andy
I like that.

1:07:53 - Andy
So yeah, that's awesome.

1:07:57 - Andy
I forgot to ask you about that earlier.

1:07:59 - Andy
So thanks for sharing about that too.

1:08:00 - Daniel
Of course.

1:08:01 - Andy
I guess it rolls off the tongue a little better than Roebling sounds a little bit better.

1:08:06 - Andy
I like with Verisounds.

1:08:08 - Andy
Maybe your next entrepreneurial thing could be that.

1:08:11 - Daniel
So my, my structural engineering firm, when I get to it, I'll call it Roebling engineering.

1:08:18 - Andy
Sounds good.

1:08:18 - Andy
Well, Daniel, Good to talk to you today.

1:08:20 - Andy
I'm sure we'll talk again later.

1:08:22 - Andy
Take care.

1:08:22 - Daniel
Andy, it's a pleasure.

1:08:23 - Daniel
Thank you very much for having me on and best of luck and continued success.

1:08:26 - Daniel
Look forward to seeing it.

1:08:28 - Madeline
Hey everybody, thanks for listening to today's episode of Enhance and please leave a like, a subscribe or a follow and we'll see you next time.