The Mistake Leaders Make When Hiring Their Team w/ B. Scott Swann | Episode 200

Speaker A: I think, uh, you can't be afraid to fail. I think if you fail, fail early, dust yourself off, get up and uh, keep moving, uh, set yourself a good vision. Most importantly, surround yourself with good people. You surround yourself with the right team, uh, and then when you get the right synergy, that trust relationship that I talked about, then I think good, uh, things will come to, to that.

Speaker B: Welcome to Software Leaders Uncensored. I'm Steve Taplin and every episode I get the privilege of sitting down with technical engineering leaders, founders and CEOs talking about what's actually working in the current times and what's not. And that this is actually our 200th episode of the show. And one of the fascinating things we've seen are the patterns that regardless of industry or focus of the business, when you're talking about technology, you're talking about software, data and AI. We're seeing the same challenges and a reoccurring pattern over and over. We recently uh, published our report called why Software Projects Fail. That was data based on the first 195 episodes. It is a great, no fluff report. Um, um, the data that we've been gathering from this show. We have more to come and that's really what this show is all about. Today we have a fun, interesting guest, Scott Swan, who is CEO at uh, ROC Iraq. And he is here to talk about as an engineering first company dealing in AI and biometrics, largely in the federal and government space. And the challenges that he's occurring. Scott, welcome buddy.

Speaker A: Hey Steve, thanks for having me.

Speaker B: You bet, you bet. Tell our listeners about your company and perhaps even tie in some of your background, which is fascinating. And what led you to this company?

Speaker A: Yeah, okay. You know, it's a, it's actually a really humbling story in some ways. I'm, I'm from West Virginia and went to school at uh, locally there and graduated with a business degree and I could not find, find a job afterwards. There was just not a lot of commerce in West Virginia and the FBI moved into my backyard and I got my first job delivering boxes, uh, at the FBI and working in the mailroom. But it wasn't long until I was worked in a professional track where I was working on their first biometric system. We were automating fingerprints prior to this. You've probably seen on, on um, you know, a lot of crime scene shows, you know, an investigative search, someone leaves a fingerprint in a crime scene. Before automation they actually used to ring a bell because they had to go search through millions of hard copy Fingerprints to try to find out who that was. We delivered the first system that allowed those kind of fingerprint searches to be searched within either two or 24 hours. It wasn't that accurate, to be honest with you. It was only about 50% accurate. But it was wildly successful because it was better than anything else that they ever had at that time. You know, you fast forward they uh, went on from the FBI to do some other roles there. But I, I worked at the, one of the largest biometric identity companies in the world and became their CEO of their national security group. But a couple of the guys who I worked with when I was at FBI had started a company in the US to address these kind of problems. And you know, this was a field that was dominated by foreign technology providers and, and I saw the opportunity to go to, at that time it was rank one computing. We refer to ourselves as, as ROCK right now. And we decided that we wanted to uh, to you know, build out a full biometric suite of capabilities. They had gotten their start as a face recognition kind of component software development kit type of a startup, startup company. Very, very successful in that space. But it had its limitations. We wanted to be able to build out fingerprint algorithms, iris tattoo objects from video and, and uh, also be able to address digital evidence type of capabilities. So that's what we're doing today. We are um, we're a, a computer vision company. We build ourselves a vision AI platform and we're addressing, you know, primarily government customers, but we also have a lot of commercial domain business as well, uh, both domestically and internationally.

Speaker B: That's, you know, when you think of a national security perspective, that's kind of scary that this field was dominated by all foreign entities, non US based entities. So that's pretty amazing what you and your colleagues have put together here.

Speaker A: People are always surprised when I tell them if we look at the way that we, we screen people within our own borders here in the United States. And I'm um, I'm talking about, you know, Department of Homeland Security, FBI, Department of Defense, all these national security. There's actually 19,000 criminal justice agencies also that use these biometric type systems. It's 100% powered by foreign artificial intelligence. There has just not been a US based company that can provide these kind of capabilities. And that's really the problem that we're trying to solve.

Speaker B: And you guys recently, this year IPO'd and uh, are listed on the NASDAQ, right?

Speaker A: Yes, we are. Was, it was a really humbling day. It was, it was my My best day in industry when we rang the bell that day, it was a lot of fun. I think for us though, it wasn't that we, you know, it was a major accomplishment. We felt like, you know, now we can kind of get started. We have a, we have a lot that we want to be able to accomplish as a public company, but we have the infrastructure now to be able to do that.

Speaker B: And so you guys no doubt have been getting great traction, having great growth, but you know, in the uh, in the current public markets environment, it's pretty volatile to smaller tech out there right now. And so that, that was a bold move. What, like what were the pros and cons that you guys were evaluating and doing this? It's one thing if you're Elon Musk and you're about to take, you know, SpaceX IPO for, you know, the biggest in history, but for the, the companies that are, uh, in their younger stage, it's a lot of risk.

Speaker A: You know, we have a couple brilliant founders. I'll tell you, I'm, I'm, you know, I came from government. I had to learn a lot of this on the fly as I became CEO and wasn't really what I thought we would do. When I started at Rock, it wasn't immediate that we decided we were going to become a public company. We actually kind of did things the hard way. We never took any capital, we were organically grown. We bootstrapped the entire business. So we never had any debt at all at the time that we actually went public. Uh, but we, we decided that to be able to compete, you know, against these billion dollar, you know, foreign technology providers, this provided us with a really great infrastructure. You know, we, we believe, of course, if we were just trying to go get money, we could have went to the, to, you know, private equity and we could have got money. It would have been a lot easier, we could have done it much faster. Uh, but we, we feel there are decade long problems that need to be addressed. We love the team that we put together. We think we were really well positioned to be able to address a lot of these problems. And so the IPO gives us access to the capital markets, but also just gives us that infrastructure for individuals too so that they can make their own decisions as we go along and uh, we can focus on growing the business. I'm still learning a lot about the capital markets as I go. I'm not an expert on it quite yet.

Speaker B: They're an interesting animal to say the least.

Speaker A: Yeah, I'm really just focused on growing the company. I think we'll be successful with that.

Speaker B: I, in my book, fail hard, win big. It's my story of having 30 ventures with 20 of them failing and 10 succeeded. And unfortunately, one of my biggest failures was a company I tried to take public in 2014 and was really close. And for a lot of reasons. Market conditions changed. My dog ate my homework, it didn't work out. And so I have, uh, having been through the process all the way till the end, short of ringing that bell, I realized it. The commitment, the challenges, the hurdles you have to jump through, to say the least.

Speaker A: Yeah, you know, we, we had a couple of scares along the way. You know, NASDAQ had changed the rules, they made it a little bit harder. And as they were going through this rule change, there was, there was a lot of delays, a lot more scrutiny for them. Looking at, especially in the micro cap space, these companies becoming public. We were actually the first ones to be able to break through the new rules and get listed. But you know, it was a lot of a, uh, lot of work, about 18 months worth of work for us preparing for this and getting ready to kind of put this infrastructure in place as a, as a public company. But kudos to you for all your success with 10 companies. You know, I certainly will, uh, will take all the advice I can get

Speaker B: from experts like you, I, I, and I, um, wish I could say there was a better way to learn than fail, but I have found when you have success and you're doing something good, you're not paying attention as much as you know why and you know what, and luck, luck, timing is always part of it with a ton of hard work. But when you have a failure and you could learn from those failures, which some people have that mental block that, oh my gosh, I failed and I got to crawl a hole. I'm, uh, a believer. You get 24 hours to be upset, then dust it off, get your ass up. What went wrong? Don't make that mistake again. And I've always found you learn so much more from your failures, your due successes. And, um, that's kind of the premise of my book. Now tell us more. On the tech side, what you guys are doing, your software platform, your engineering organization.

Speaker A: Yeah. So, you know, there's, there's a few areas specifically that we're focused in on things that relate around identity. And so you can think about your fingerprints, your face, your iris. You know, we build those core algorithms that let you match against large scale databases, uh, for identifying or recognizing or authenticating people. We also, in the video analytics space, we are able to identify a range of objects. We'll build the algorithms for that to, to be able to do that. In fact, the good story, the um, the founders of, of Rock. One of my last FBI assignments was the Boston Marathon bombing. And regardless of anything that you saw in the news, there was really no technology to support those investigations. When it came to, to video, this was the first time that the FBI opened up its tip line to the public to, to collect uh, video photos and, and videos and we were inundated. It was a bit of chaos to be able to get through that.

Speaker B: What I'm trying to think what year that was, that was early 2000s, right?

Speaker A: Yeah, it would have been I think just a little bit, maybe 2007 or so that happened. The, the irony of that was when I was, I did a, a stint a joint duty assignment in the Director of National Intelligence and I learned of these major issue studies where you really want to solve a problem, you throw a lot of money at it, you study it and you actually kind of create a roadmap and that usually opens up the ability to get additional money in the government space. The founders helped me run a major issue study. It was the first FBI major issue study. But we decided to do it on video. So we wrote the roadmap on video analytics for the FBI and now we're a technology provider in this space. And really the, I guess the last area that we're really focused in on is digital evidence. You know, we have a, a team of seasoned experts that's lived these missions. We have people that's been involved in every major event since and including September 11th. Every active shooter that's been out there, they were a part of collecting this digital evidence and, and being able to preserve that and, and be able to present that to both prosecution and defense teams. Uh, so we're building out capability in that space. It is important for us to note, you know, we got started as SDK component based. We are building out full stack applications right now and all these various products that we build all fit together within a uh, vision AI platform that we put together. It's just too common in both government as well as commercial oftentimes that these systems are stovepiped. The data doesn't talk to each other. You know, large corporations, large government agencies, they can struggle when they try to build out this stuff themselves. They're not so great at building all this middleware. So we're trying to make all that easy for the consumers of our technology so that they can have, you know, technologies where missions just really were meant for this data to coalesce. We can help make that happen for them.

Speaker B: So you guys are on the device side as well and the software side, which. That's correct. So for your solutions, you do, you guys are manufacturing the devices as well?

Speaker A: Seldom. Um, we consider ourselves more of a software company. We didn't win an innovation award out at the International Security Conference west in Las Vegas here just recently for our new face recognition access control device. Very small face recognition authentication device, um, that you might see to turn style, um, that's really intended to help people get into our vision, AI platform, take advantage of our video analytic capabilities, our rock access capabilities to be able to use turnstiles, that type of stuff. Mostly we're a software or algorithm company.

Speaker B: Gotcha. So tell us about your product and engineering organization. How many people you have, where they're located, your work model.

Speaker A: Yeah, you know, so as I mentioned, I'm from West Virginia. A lot of people don't realize that the largest division of the FBI moved out to West Virginia. Wasn't too long after that that the, uh, biometric capability for the Department of Defense moved out there. So there's this little hidden pocket of biometric identity excellence that's in West Virginia. You know, if you go try to build an engineering team in Washington D.C. what you see is you get a lot of attrition, have constant turnover. People are going to take jobs. You're competing with the Amazons and big techs and those folks there. So our engineering group is out in Morgantown, West Virginia, and we've accrued some incredible talent there. Our headquarters is in Denver, Colorado. Colorado. The two founders both moved out there. Um, this is where most of our algorithms are built. This is where we build our software development kit primarily. And then we have a large cadre of PhD scientists that came out of Michigan State University. So we keep a smaller office out at Grand Rapids for those folks. And we have obviously some people that spread around the United States as well, working remote.

Speaker B: Gotcha. So highly specialized team based on the, the uniqueness of what you guys do.

Speaker A: Most of our team have some kind of an advanced degree. You know, lots of PhDs, lots, uh, of advanced degrees here. Even our sales staff to tend to have engineering backgrounds. I was a software engineer myself prior to getting onto the business side of things. But I think in these advanced technologies, when you're trying to, you know, solve complex problems for our customers, it's just, uh, it makes sense. It Gives you better credibility to go in and be able to understand their mission sets, the kind of technical problems they're trying to solve.

Speaker B: So what engineering challenges and skills would you say are unique to your industry as opposed to other sectors?

Speaker A: How long's your show again? No, there, there's always a, there's always a lot of challenges. I, I feel like we've, we've done a really good job at, uh, being able to recruit positions. I feel like we have a good talent pipeline. That's something a lot of companies struggle with. But in this era of AI, things are moving really, really fast. And you know, our, our, our engineers, they're, they're needing to learn new skill sets for the development side of the house. You know, if you develop the legacy ways that, that you were doing it two or three years ago, you're going to, you're going to get passed pretty quickly. So we're learning a whole new, you know, way of building out our technology stacks. Uh, and then, you know, we're, uh, we're also, I think one of the things we're focused on is bringing up a lot of our operators. We got a lot of people, as I mentioned, that have this operational experience together with our PhD scientists that wouldn't necessarily have that kind of experience to just to make sure that we're building the right kind of capabilities. I think that's where a lot of software companies, very capable software companies can fail is they don't get access to the right sets of requirements to know exactly what to build. That's been a strategic advantage to us because we built a lot of these systems that we're trying to replace. And so that's another particular area where I think we've been able to, uh, to, to really kind of overcome some of those challenges that a lot of software companies have to struggle with.

Speaker B: So a lot of your business revolves around government work, state and federal. And certainly when you're dealing in government work, sometimes you're limited as to the extent you can use AI. Talk about that.

Speaker A: Yeah, you know, the government is, and for, for some good reasons, they, they need to put a lot of governance in place, uh, when it comes to AI. And, and they, they typically move a little bit slow. And they would, they, they move slow because they need to be methodical. And then obviously they, they, they are the government. It's sometimes harder for them to, to move. Most people realize that as much as we respect them, the, the AI that would be used within law enforcement, for example, they're going to need to Go testify to this in court. And so they're used to being able to explain exactly how systems work, how algorithms work. That doesn't always fit with current AI. You know, sometimes you just take a bunch of data and you train a model and you don't really know exactly what it's using to be able to do the magic that it does, but it does that. So, you know, explainable AI and, and uh, and deep learning aren't necessarily synonymous with being able to, to be able to, you know, support a real good narrative exactly of how the algorithms are working. And so, but there are things that we can do and so we, we want to be able to work directly with our customers to help them understand, you know, the, the entire supply chain, understand how these algorithms are built, understand how they're trained, so they can be able to modify the way that they talk about these algorithms in a certain way. So that's really kind of on the law enforcement, anyone who needs to testify in court, but even across the board, you know, I think you're talking about systems that are oftentimes supporting national security, homeland security, maybe have officer safety or lives at stake. And so any introduction of a new technology and AI is just one example of that is needs to be really carefully thought through. And, and we respect that. We want to be a part of helping them be able to, to really be good consumers of the technology because we know how much it's going to help their emissions. And sometimes the lack of governance that they've been able to put in place, it will slow things down.

Speaker B: So where does that balance come? And right. Rightfully so. All this stuff needs to be thought through to a higher extent. But with that said, sometimes the politics side of it is slowing things down for a reason solely related to the politics side of it, not necessarily for the best. Uh, that's a hard space to, to navigate in because you want to bring the best innovation and capabilities. And you know, let's free pretend for the most part they're, they're truly acting in the best state. But sometimes politics are politics. Like, what's the balance there?

Speaker A: Well, you know, I think, I think we try to stay in the middle when it comes to politics, but it's hard to, it's hard to say that politics don't factor into anybody's doing govcon work. You know, we're doing a lot of work for, uh, federal agencies and we saw 2025 where there was no budget passed at all. And so they operated an entire year on 24 levels. And then Then they had kind of a historical government shutdown for a couple years. So if you fast forward today, you know, 2026, in a lot of instances, the new money hasn't even made it to the entities that need to spend it. That said, the way that the fiscal year kind of rolls out there, you have, you have, uh, a need to obligate money before September 30th this year in the federal space. So, you know, the, the ability for Q1 and even to some extent Q2 awards, uh, to get made. It just takes time for that money to flow.

Speaker B: Ah.

Speaker A: So, you know, that, that's a, an example from the monetary side of, of how politics can play into things. Now when you start talking about system governance and being able to va, um, adopt things like AI hey, some of the, some of the technologies we work on, they, they can be considered a political lightning rod. You know, face recognition, for example, you know, it's not, it needs to be responsibly deployed. One of the things that actually attracted me to, to Rock, uh, you know, I'm sitting in a much, much larger, you know, company and I look over and Rock was the first one, first ones to put out an ethics statement on, on how face recognition should be used and consumed. And it was pretty bold for them to do that. But, you know, they put out kind of like, you know, here's the do's, the don'ts. And I look at some of the industry associations right now and I've seen, you know, some thought leadership that, that our, our company's been able to provide to them. You know, there's lots of legislation that goes through that is talking about these technologies, trying to ban these technologies in some instances, and sometimes they're trying to, you know, highly govern them on how they can be used for policing or other things. Um, education is important. Education doesn't always get you all the way there. You know, there's some entities that are pretty hard entrenched in their views on these technologies. Um, but I think, you know, for, for most reasonable people, if you can explain to them what the technology is, you know, how the technology is used and make sure that the right, you know, policy implementation guides and the right standard operating procedures and all those are put in place, you know, there's no doubt that these technologies can provide a very valuable uptick to the missions.

Speaker B: You have been the CEO of Rock for, uh, five and a half years, give or take, right?

Speaker A: Yeah, a little over five years.

Speaker B: All right. And you had a, uh, huge, compelling event this year of going public on the nasdaq. What's become harder as your company has grown?

Speaker A: Yeah, you know, we started scaling, as I mentioned. We didn't take any, any money last couple of years. We, we, you know, when you're operating organically, you don't really have any choice but to be kind of profitable or very close to profitable. We operated at a small loss only because we had enough money in the, in the bank account to be able to do that. We made a conscious decision to manage at the wire and reinvest everything back into the company. And then you fast forward and, and you know, all of a sudden we take our first, you know, you know, tranche of money as part of the ipo. And that was great for us. Allows us to grow, allows us to scale, allows us to put some things in place, but it doesn't, doesn't not present any, any challenges. You know, now we're bringing on a lot of new people. We're, we love the culture we've created here, and so preserving that culture is something we protect. So we're very careful about that. I think culture within a company, you know, a trust culture, can help everybody move much, much faster. Being able to, you know, stay in your lanes, understand exactly what you're responsible for. And, uh, and especially as you're, you know, we're still small as you're small, you know, if you can't operate with trust, then, you know, you, you can, can really cause some, some major problems with, with any future growth. So, uh, you know, I think we're, we're, we're managing through it, and I think we're doing a pretty good job at it right now. But, you know, I think that is first of mind for us is as we bring on a lot of people maintaining the culture that we love here and making sure that we don't lose that. And I think as, as we prove that we can do that, you know, people who love the mission, support, support the mission, work collaboratively with one another. Um, we're high intelligent group. We got a lot of, you know, a lot of really, really smart people at Rock, you know, and we got a lot of hard workers here too. We get that kind of pedigree of people in place that I think will be very, very successful.

Speaker B: If I'm going to touch on what you said of you, uh, weren't taking money and you were forced to be profitable, I actually see that when you see some young, smart people getting capital for ventures that have never been through that, of, you know, dealing with their own money, Making sure it's profitable. It kind of scares me how some of the companies will act out there. Um, I, um. For the most part, every company I've ever had has been focused. I've raised money in many circumstances, but the company was always profitable first. And then the capital was meant to certainly expand, but with that fine balance of maintaining the profitability. And it kind of shocks me, a lot of companies, uh, well, geez, look at, you know, outside of the biggest AI companies that are getting ready to do some of their biggest IPOs, you know, they've never operated in the profitability space. And, um, that's. That's a scary thing from an entrepreneurial perspective. Uh, unless you're Elon Musk and you could just pull, you know, 40 billion out of your personal and put it into the company.

Speaker A: But we. We've seen it. You know, I. We've looked around and, you know, there was a period of time where there's a lot of funny money out there, and it was easy to just get your hands on money and just, you know, and grow and, uh, you know, spend a lot of money on marketing and maybe not as much on the core infrastructure and things that actually would take you, you know, foundational stuff that would take you into the future. I think the way that we grew up with our organic RO roots, it just created, you know, a lot of frugal fiscal responsibility that we carry with us now that we actually have, you know, access to some capital market money. And, you know, as we, as we grow, I just really don't see that going away. I think that that is something that we need to kind of be, you know, good stewards of. We got a lot of new partners now that's. That's invested in us, and we want to be able to be transparent with them and tell them how we're deploying the capital that they spent at Rock. And, uh, you know, as we look around at a lot of these other companies that took that money, you know, that money became harder to get for. For a little while at least. And. And you see a lot of collapsing of. Of some of those companies that are. That are out there now as well.

Speaker B: If nothing changes and how you guys are going about your product and engineering process, what would be the first thing to break in the next 12 to 24 months?

Speaker A: Yeah, you know, I joke around that we got to the size we are right now through an aggregation of baby contracts. And that is, you know, when you're selling components, that's kind of what you end up with. Is just not large contracts frequently that have you know, really good annual recurring revenue. They tend to be more either point in time contracts or just small, you know, 20% maintenance on something that wasn't big in the first place. The uh, actual initial investment. But as you go into full product sales and one of the reasons we did this because we were actually doing the hard part, we're building these algorithms, we saw other people coming and making products around us and we're getting pennies on the dollar compared to what they're making. So we're laser focused on getting larger contracts, multi year contracts with annual recurring revenue. And if you look across the federal space right now, um, I can tell you every one of these federal screening systems are at some stage of acquisition, either in their market research stages or they are starting to really put out solicitations to need to get these technologies. So you know, we are well positioned I think to, to be able to uh, to be a player in each one of those areas. Also in, in the digital evidence space also we're, we're looking to, to grow there again large federal contracts. Uh, I don't want to just focus on federal, we, we hired someone to, to manage our channels for all of our commercial sales and we've gotten off to a really good start in that space too. So I think you'll see a lot of traction in the commercial space. We just won that uh, innovation award out at ISC west. And you know, one of the things I would say face recognition was how the company got its start. And it was, it was predictable that eventually that would probably become more of a commodity. There's a lot of people who play in that space, but there's not a lot of people who completely understand how the end users need to deploy, employ this kind of technology, can put those very specific use cases to that and that, that's a differentiator, that's also how you become sticky and you're able to keep and sustain these contracts even if the period of performance may be five years. You know, if you're doing a good job and you're performing, you know, these can turn into multi decade engagements with your customers.

Speaker B: All right, Scott, last question. But if you can give one piece of advice to fellow tech leaders out there who are going to embark a, ah, similar journey of what you've been through, what would it be?

Speaker A: I think uh, you can't be afraid to fail. I think you, if you fail, fail early, dust yourself off, get up and uh, and keep moving the uh, you know, set yourself a good vision. Most importantly, surround yourself with good people. You know, you surround yourself with the right team and then, you know, when you get the right synergy, that trust relationship that I talked about, then I think, I think, you know, good things will come to, to that. And you know, we always say, you know, bad news doesn't get better with time. Uh, kind of falls in, in line with that. You know, don't be afraid to fail. Um, you know, be an honest leader, be a transparent leader and uh, and surround yourself with good people. And I think that's really a recipe for success.

Speaker B: I love it. Scott, thanks again for getting uncensored with us today and for sharing some of your background on your journey and what you guys are doing with Rock.

Speaker A: Uh, thanks Ashford.

Speaker B: Thanks again Scott. That's it for this episode of Software Leaders Uncensored. If you're leading a technology organization and any of the patterns we've talked about here sound familiar, make sure to hit subscribe. Also get your copy of the why Software Projects Fail report research based on the first 195 episodes of this podcast. I'm Steve Taplin. See you next time. That's a wrap. Another great episode of Software Leaders Uncensored. Thank you guys so much for listening. I really appreciate it. It's a lot of fun doing this show. I love talking with great tech leaders. As a side note, if you're interested, check out my new book Fail Hard, Win big. This is the story of how I built 30 companies, failed on 20 and turned 10 into multi million dollar wins. The common thread with all of them was custom software. Also, if you need software development help out there, my company, Sanotify Technology. We deliver world class software development engineers out of Latin America. We have a solid track record of helping companies launch better products and faster. Thanks again for listening. Appreciate it and appreciate you.