NDE 4.0 Podcast | Transcript | How Cloud-Based Technologies Can Transform Nondestructive Testing | Episode 12

NDE 4.0 Podcast Transcript

Episode 12 — How Cloud-Based Technologies Can Transform Nondestructive Testing

Our Guests: Ajay Pasupuleti and Michael Turnbow from Ooga Technologies

Editor’s Note: In the interest of time, this transcript is still in rough format and has not been edited for proper grammar and punctuation. If you have a need for a fully edited transcript, please contact us.

[00:00:00] Ajay Pasupuleti: Welcome to the NDE Where we ask five questions for a NDE or NDT

[00:00:13] Nasrin Azari: expert. This is the show for NDE professionals where we dig into the big questions about NDE inspections and digital transformation. Every episode we ask a NDT expert five questions that can help you

[00:00:25] Ajay Pasupuleti: do your

[00:00:25] Nasrin Azari: job better. Hello, everyone, and welcome to today’s episode of Floodlight’s NDE 4.

[00:00:31] Nasrin Azari: 0 podcast, where we pose five questions to a variety of NDE 4. 0 experts and explore the benefits and challenges in this emerging field. Today, we are joined by Ajay Pazupileti and Michael Turnbow, two founders of UGA Technologies. I’m really looking forward to this discussion today, which will be focused on creating a more efficient and capable NDT workforce.

[00:00:55] Nasrin Azari: So first, let me introduce our two guests. Ajay Pazupileti is [00:01:00] an entrepreneur with over 15 years experience in image processing, data management, and archival technologies. He has successfully demonstrated that he can convert concepts and ideas into revenue streams and profitable companies. As founder and CEO of UGA Technologies, he strives to bring the worldwide NDT community closer through the UGA platform by improving efficiencies and ensuring knowledge is not lost through attrition and or retirement.

[00:01:28] Nasrin Azari: He received his Ph. D. in Electrical and Microsystems Engineering from Rochester Institute of Technology in 2006. An equally impressive and somewhat intriguing ability is his skill in discerning the subtleties of rare scotch, considering he has never even visited Scotland. That’s great, A. Turnbow.

[00:01:52] Nasrin Azari: Has over 30 years experience and non destructive testing and is one of the founders of technologies as well. He retired as [00:02:00] general manager of the Tennessee Valley authorities inspection and testing services group. He previously served as president and chairman of the board of the American Society for non destructive testing as chairman.

[00:02:13] Nasrin Azari: Of the E. P. R. I. N. D. E. Center Steering Committee for nine years as U. S. Delicate to I. S. O. For the development of international N. D. E. Standards for over 10 years, and he is currently chairman of the A. S. M. E. N. D. E. Personnel Qualification and Certification Project A. N. D. E. And A. N. D. E. Nuclear specific Industry sector committee.

[00:02:36] Nasrin Azari: That’s a lot of acronyms there, but it sounds like you two both have a lot on your plates. So we greatly appreciate you taking some time out to speak with us today. So welcome to the podcast, AJ and Michael.

[00:02:48] Michael Turnbow: Thank you. Thank

[00:02:49] Nasrin Azari: you. So as everyone knows, the format of this podcast is that I will pose five questions to AJ and Michael related to their expertise and to NDE 4.

[00:02:58] Nasrin Azari: 0. So [00:03:00] let’s start with a question to dig a little deeper into the background of OOGA, um, and, and AJ and Michael, can you tell the audience a little bit more about the OOGA platform and how you believe it relates to NDE 4. 0? AJ, let’s hear from you. Thank you,

[00:03:17] Ajay Pasupuleti: Nazrin. So OOCA technologies has been formed because we wanted to build a collaboration platform for the NDT level 2s and level 3s.

[00:03:28] Ajay Pasupuleti: Um, we we’ve learned and we’ve seen also that, uh, the average NDT level 3 age You know, if you look at the statistics, it’s around 48, 49 years. That’s the average, where if you take any other industry, it’s more like 40, 42 years. So there is a rapidly decreasing workforce in our industry. And that’s causing for [00:04:00] shortages in terms of giving back the inspection reports.

[00:04:04] Ajay Pasupuleti: So. The way we came up with this is almost like if I had to compare it, it’s Uber for NDT, where level twos and level threes around the world can come onto our platform, register. Once accepted can take on projects on a flat on a cloud based environment where they’re given the necessary computational power, the software that they need to do the inspections and the expertise is also available there for any company to come in and use these services.

[00:04:41] Ajay Pasupuleti: So from a 4. 0 perspective, um, if you look at how it is relates to NDE in 4. 0, there is a lot of automation happening in the front end. That is the data acquisition side of, um, The, um, part of the 4. [00:05:00] 0, but as that is great, that’s absolutely great. And that’s all we should be going towards because there is a lack of people again in the level one who are there to capture that information.

[00:05:13] Ajay Pasupuleti: Now where we come in is because of automation. The data is available, but the bottleneck now in 4.0 ends up to be the workforce that has to look at this data and approve or make changes. So that’s where, uh, uh, we are, we come in, that’s where we come in to say that, Hey, you, you have a part that is coming in.

[00:05:38] Ajay Pasupuleti: We can have our people. sit and do remote inspections for that particular piece of, um, uh, whether it could be a casting, it could be a weld. Um, we are encouraging remote inspections, but we’re also bringing in this expertise of, uh, that is there all around [00:06:00] the world into one location where people can use.

[00:06:05] Nasrin Azari: I can see how that could have been very, very

[00:06:13] Nasrin Azari: And I think that would COVID pandemic, where I know there was, um, situ, there were situations where people were limited from actually being in the field on site in some cases. And so a solution like that seems like it would have been very helpful.

[00:06:30] Ajay Pasupuleti: That’s actually a very good point because, you know, we as an industry in NDTR.

[00:06:37] Ajay Pasupuleti: Very slow to change. So from COVID perspective that people started to do remote inspections, they were forced to go into remote inspections. And now we are learning how we can do a lot more and a lot more efficiently, um, in this fashion.

[00:06:54] Nasrin Azari: Yeah. Cool. Well, before I move to the next question, let me find out if Michael [00:07:00] has anything he’d like to add, uh, to the topic of the OOGA platform and how it relates to NDE 4.

[00:07:05] Nasrin Azari: 0.

[00:07:07] Michael Turnbow: Your discussion just now about the COVID and the remote inspection thing, um, being part of the nuclear industry and, of course, the Electric Power Research Institute that you mentioned a few seconds ago. Um, we’ve been watching this for years because we would experience in our outages shortage. of personnel over the last five years, actually even longer than that.

[00:07:27] Michael Turnbow: So in trying to motivate the industry to grow the workforce, um, it’s pretty difficult cause that’s, that costs money and power plants, operating power plants just won’t let trainees in. They’ll only let qualified people. So it’s a catch 22. And so when I got involved with AJ and the other folks on this project, got real excited about the platform now serving.

[00:07:50] Michael Turnbow: As a place to grow this new workforce, as A. J. Is already discussing, and you have acknowledged Uh, folks like myself that’s retired. [00:08:00] We could get them, mobilize them, have them setting waiting for a job and fill in a gap for a while, right? You gotta need a group of folks that’s retired. And of course, there is an end to that.

[00:08:11] Michael Turnbow: So we’re working pretty fast to, uh, establish a qualification process. We’re working with the ASME that you mentioned a while ago, the ANDE. ASME has published a standard and we’re now implementing that standard. So I’ll stop there and just say all that’s coming together on the platform to help, uh, globally, not just the United States, everybody, if you touch base in any other country.

[00:08:34] Michael Turnbow: They’ve also got the shortage issue going on. And a lot of those are on our committees helping us build this certification process and wanting to be involved in the platform.

[00:08:44] Nasrin Azari: Yeah. I mean, as we’ve certainly learned over the last couple of years, it’s a lot easier to round people up if, if you’re, if you’re doing it virtually, then trying to get people in one place.

[00:08:56] Nasrin Azari: And sometimes, you know, it’s, it’s, it’s Like I said, it’s a lot easier to [00:09:00] mobilize and connect with people if you can, if you have the ability to do it, um, over a, over a cloud system. So I’m real excited about the potential that you guys are bringing to the market with UGA. Um, let’s go to question number two, which is when thinking about the new technologies.

[00:09:19] Nasrin Azari: that are coming along. Um, a lot of technicians are afraid of losing their significance or their own importance to to these technologies like A. I. Digital testing systems, etcetera. Um, A. J. What do you think? Um, what do you think about this? How do you How do you think of of how folks can adjust to that?

[00:09:42] Nasrin Azari: Situation or kind of trying to change their mindset?

[00:09:46] Ajay Pasupuleti: It’s a very good question. Actually, uh, we, uh, we had a series of conversations with level threes about this, something like this. And yes, there is an initial fear for [00:10:00] change. Um, and yes, the thought process that I can take over is there. But, um, as we talk to them.

[00:10:10] Ajay Pasupuleti: And as we discuss this concept of why AI is there. Um, I see this actually helping the inspectors. And I say this because, um, assisted defect recognition is a tool that can be there as your cheat sheet that you carry along. Um, at this point in time, I, uh, you cannot replace a human from the loop. And I don’t see that happening in the next five, 10 years, uh, where, um, you know, this golden piece of certificate, this level twos and threes have AI.

[00:10:48] Ajay Pasupuleti: What we are trying to do in the OOGA platform is that we are implementing an AI program where each. Uh, consultant on our [00:11:00] platform can have their own AI, um, that they train as they inspect. This is almost becoming like that. You can visualize that to be their clone, you know, Hey, Mike can have his own, uh, clone as he does his inspections.

[00:11:19] Ajay Pasupuleti: And that can be a tool that they could Say, hey, I’ll give you my AI. This is something that has learned from me. This is my tool. Now, you could, they could potentially commercialize that piece of it, AI as an ADR tool for their inspections. Now, I believe that if we as, um, industry give avenues for where there is comfort more from a technology perspective and revenue generation for them, the adoption will be faster.

[00:11:55] Ajay Pasupuleti: So that’s how we are looking at it and saying, Hey, develop your own [00:12:00] AI, keep it there. And you want to market your AI to ABC company, they can go use it. And depending upon how many years experience you have, your AI may be better than mine. And, um, that could be your first level run. And, uh, when you bid on a project, your AI does the job and then you look at it and you look at the, uh, false positives and the whole data and you do your sample.

[00:12:32] Michael Turnbow: That’s

[00:12:32] Nasrin Azari: really interesting, aj. I mean, it’s, it’s, it’s a different perspective than I’ve heard from, from other folks that are looking at ais and, and part of the, part of the challenge around, you know, humans building ai, um, and, and developing ais and the machine learning and everything is this concept of bias and that it, that bias comes from the humans who create it.

[00:12:59] Nasrin Azari: And in this [00:13:00] particular case, you’re actually. Wanting to put that bias in there, right? You’re saying this is Mike’s. This is Mike’s AI. So it’s going to have the same bias that Mike has, uh, versus other, um, types of AIs that are being built that try to consolidate perspectives from multiple individuals so that they can try to create a bias free AI.

[00:13:26] Nasrin Azari: So, so that’s pretty interesting. What do you think about that? Have you, have you had those conversations with folks?

[00:13:32] Ajay Pasupuleti: Yes, we did. And we, if you think about NDT, the way it is done, and, uh, you know, I’ll be the first one to admit Mike is a level three. I’m not. They, the way they inspect, there is a spec, but the way they, end of the day, when they go about doing each inspection is kind of subjective, but not subjective in the sense that They’re, they know exactly what they’re looking for, but the way they go about [00:14:00] it is different from individual to individual.

[00:14:03] Ajay Pasupuleti: Um, and that is the critical piece of this equation there as to how do you capture all the flaws and you can see a lot more than what you should see. But the question is again, going back to what is it that the spec calls for and how do you decipher those things? So these are the things that there is a human element that comes in.

[00:14:27] Ajay Pasupuleti: And that’s what I believe is what is the, uh, resistance from the industry for utilizing that.

[00:14:36] Nasrin Azari: Are you utilizing more generic AI?

[00:14:39] Ajay Pasupuleti: More generic AIs. Yes.

[00:14:42] Nasrin Azari: Michael, what do you think about that? I’m sure you’re, you’re, you probably have something to add.

[00:14:47] Michael Turnbow: It’s a, it’s a, it’s an exciting time for this, um, this technology to evolve.

[00:14:53] Michael Turnbow: And if you look back a little bit, when I got out of school, uh, in 74, got out of college in [00:15:00] 1968, ASNT published the first personnel qualification certification document, SND TC1A. It was the first. And there was nothing before that. Everybody just did whatever they could. And over its introduction over those first few years.

[00:15:15] Michael Turnbow: And when I got to, when I got to work there in 74, uh, it had had some experience and it wasn’t going well, there was a pressure vessel research committee, um, uh, Pacific Northwest laboratories, the NRC, um, some, some British studies, there was a whole lot of studies going on, trying to find out. Why was people missing flaws?

[00:15:38] Michael Turnbow: And it was a human element thing. Everything you’ve talked about, it was procedure wise, it was training wise. So I grew up in the middle of all that, and we worked on that in the code. Uh, the failures kept happening during the eighties and finally, in the nineties, we put together the Electric Power Research Institute, and it’s a performance demonstration initiative, PDI, that we started requiring the nuclear [00:16:00] guys to go through in 92.

[00:16:02] Michael Turnbow: And between 92 and 2012, we had data that showed all those qualified people couldn’t find 50% of the floss. To this day, that’s the way it is. Now, if you move away from nuclear, other studies, non nuclear studies, particularly the British folks did one, it’s called PANI on boilers. And I couldn’t believe it.

[00:16:22] Michael Turnbow: The guy called me as soon as they published it in 1999. He says, you ain’t going to believe this, Mike. We got 50% detection rates just like you guys. So we started the ASME thing to address it. AI is coming along and what you, you folks have been talking about, I won’t replay it, what AI is going to do for us.

[00:16:38] Michael Turnbow: It’s going to give that inspector, um, they’re getting data and they’re processing it. But there’s still something missing for, for them to not be able to get a better detection rate than 50%. Obviously, we think it’s to do with the training and experience some flaws in the certification process, which we fixed and we’re working on.

[00:16:58] Michael Turnbow: But I think at the end of the day, the [00:17:00] big bang for the buck that’s going to make a big difference. It’s going to be that inspector is going to have all this information is going to be processed for him or her that they finally, at the end of the day, get to make a decision on better than they’ve ever been able to make before.

[00:17:14] Nasrin Azari: Yeah, I mean, I think the biggest benefit and argument for AI and NDT that I’ve heard is that a lot of the process is repetitive and, you know, 80% of the material that you’re looking at is defect free. And if you can eliminate the amount of sort of clean, um, Material then it makes it makes the job less tedious, and you’re more likely to find the defects in the remainder.

[00:17:42] Nasrin Azari: So that’s kind of one of the arguments that I’ve heard that that makes a lot of sense to me on putting your human resources where it really counts. Yes. Yeah. So let’s change the let’s let’s change the topic over to to talking about data. [00:18:00] Because as we, as we talk about industry 4. 0 technologies and de 4.

[00:18:04] Nasrin Azari: 0 technologies, big data is clearly at the center of many of these initiatives like machine learning, big data is required for machine learning, it’s required for AI. required for digital twins, et cetera. Um, how do you see increased data, data availability and data analysis affecting the future of NDE projects?

[00:18:28] Nasrin Azari: Michael, let’s start with you.

[00:18:30] Michael Turnbow: Okay. Well, like I said, if you go back a ways, it was all ultrasonics.

[00:18:43] Michael Turnbow: And as time goes on, you’re, you’re, you’re now asking a question about what did you have to say is data overload, which obviously it is when we get into phase array and digital radiography and some, some of the new technologies, we have the ability now to take huge amounts of data and all those [00:19:00] data points.

[00:19:01] Michael Turnbow: If you could process them and get the information about each each point means against itself or another point and at the day it sums it up, it tells you what it sees. That’s exactly where we are right now. A lot of people, um, we have a lot of schools in this country and we started one here in Chattanooga a few years back.

[00:19:20] Michael Turnbow: Chattanooga State Community College in cooperation with the ASME. We now have an associate’s degree program here. And, and we’re, uh, really talking about this so that the course changes. It’s, it’s, it’s got the fundamentals, but it’s got to move more towards IT and the kinds of things so that these students can be, uh, informed, educated, prepared when they step out of school and get a job and their employer now says, okay, here’s what I want you to do.

[00:19:51] Michael Turnbow: They don’t get that deer in the headlights. Look, they know. Exactly what to do. So that’s a challenge for us. We messed up on this in the past, [00:20:00] uh, acoustic emission came out a few years ago and we fumbled it around and made a lot of promises it couldn’t deliver. Then phased array comes out. We did the same thing with phased array.

[00:20:11] Michael Turnbow: We’ve, we’ve, we’ve grown out of both of those. And, uh, we’re, we’re better prepared and UGA and the cause of being part of this group, uh, is one of my passions is to make sure. That we take care of that this time for the industry. And when it rolls out, it’s going to create people who knows what they’re doing, they can prop, they can understand this massive data.

[00:20:32] Michael Turnbow: It gets processed for them. They know how to get through it and get to a conclusion and we’re shooting. We’re shooting from 50% to 95.

[00:20:40] Nasrin Azari: Yeah, yeah, that’s, that’s, that’s, that’s amazing. I mean, I was surprised to hear the 50% number. I’m sure people, any technician out there would probably guess that their rate is much higher than that.

[00:20:53] Nasrin Azari: Um, so that’s a really interesting data point. Um, AJ, do you have anything to add [00:21:00] to the conversation around big data and how that would, will affect, uh, future of NDET projects?

[00:21:09] Ajay Pasupuleti: Just a couple of notes there. Big data, you know, data, in my other company that I’m used to, I work, uh, and I have, where we scan film.

[00:21:21] Ajay Pasupuleti: This is where we just digitize film. I, we generate about 8 million sheets of film every year. You know, there are projects that, that, uh, have upwards of 30 million sheets being just pumping out. So this is analog film converted to digital. That’s all we do there in that, um, world. And, um, so I understand big data with that perspective.

[00:21:49] Ajay Pasupuleti: Um, the interesting part of it is that, you know, most times, um, This data that is being generated in NDT [00:22:00] is being used more for archive. There is really not much being done with analytics on, hey, how can we do it? What can we do? What can we learn from that data that we have? Um, the primary reason is that that is mostly because it has been done, inspected.

[00:22:22] Ajay Pasupuleti: Somebody wrote a report and put it aside and, you know, unless something falls off the sky, you would never really go back to that particular thing. You store it for the number of years you need to store it and then toss it out. Now, um, now am I saying that we should go back and dig through? Probably not, but there is definitely.

[00:22:44] Ajay Pasupuleti: you know, more analytics that can be performed. And, um, even in bond digital data that is there, like the DR and the CT, that’s massive files. And, uh, it’s amazing for me when people run [00:23:00] around with hard drives and say, Hey, I have my data on this hard disk. And how are you going to analyze? And how are you going to retrieve it is not something that they look at.

[00:23:09] Ajay Pasupuleti: So that is a concern for me, how people handle data. I don’t think the industry is there yet as a full grasp in terms of what do we do with the data that we are keeping or generating. The question becomes, uh, I think it has to go back into economics in terms of what can you gain by the, uh, data that you have.

[00:23:34] Ajay Pasupuleti: And one of the things we are looking at is tying that to performance. Performance evaluation for the inspector. So if there is a way, we can say, hey, ABC did this particular inspection, and down to one year from now, you realize that this individual has missed something. you could potentially come back and say, Hey, [00:24:00] next time you come around for training, we will, let’s focus more on what are the things that you missed in the course of your inspection and train you more on those aspects.

[00:24:15] Ajay Pasupuleti: Um, and then say, Hey, now we hopefully you caught all of that. And now you’re a better inspector. So. Those are the kind of things we probably should be looking at from an analytics perspective.

[00:24:29] Nasrin Azari: Yeah, I think where I would definitely agree with you is that there is a focus today, and we’ve had some conversations with our own customers about the amount of data that they foresee themselves collecting and how to store that and how to manage it.

[00:24:46] Nasrin Azari: And we haven’t yet moved to the point of, okay, now what can we actually do with this data? And I think that the focus right now is I know my data is important, and I want to be able to collect it and have access to it. [00:25:00] I’m not quite sure the extent of what I can actually do with it yet. And I think that that’s kind of still Still TBD and it’s obviously going to change over time.

[00:25:09] Nasrin Azari: I mean, there are a lot of data sets and other industries that have been collected for one purpose, but then use later for other purposes to as you know, particularly in drug studies and things like that. So there’s definitely value in storing data. For how long and for what purpose is, is going to be up in the air, I think, right?

[00:25:30] Nasrin Azari: Absolutely.

[00:25:31] Ajay Pasupuleti: How long is probably dictated by the retention schedules in most of these industries. So they’re kind of forced to keep it in that sense. The question becomes, what else can you do with it?

[00:25:44] Nasrin Azari: Yeah. Yeah. That’s right. Um, let’s move to our next question, which is related back to the personnel side again.

[00:25:54] Nasrin Azari: As we think about these new emerging technologies changing the NDT landscape over the next [00:26:00] several years, are there any particular skills that you believe will be important for NDT practitioners to learn today to be better prepared for this unknown future? Michael, let’s hear your thoughts.

[00:26:13] Michael Turnbow: Okay, um, as we mentioned, and of course, uh, the, uh, the 50% detection rates, you know, it’s, uh, that’s a shocker, but those, those, uh, records and those studies are all public, so anybody can get to them, but, hey, our industry is small, and, and we’re about the only ones that worry about that or take a look at that, but, uh, those of us interested in trying to improve that, um, have studied those studies, and we find that it’s the experience that’s the weakness A long time ago, as we had a lot of manufacturing and construction, people that were in, in the training entry level of personnel had an opportunity to spend time in a shop or a time in a construction job.

[00:26:56] Michael Turnbow: The companies didn’t mind it, they kind of blended in, they could do some other things, they got their [00:27:00] OJT. As that’s all going away, we’re more in operations now, like I said earlier, nobody wants a trainee in an operating plant. They, they just don’t want that. And so it’s really, really hard to get that done.

[00:27:12] Michael Turnbow: So what I see in the future, what we see in the future, uh, we see simulators. Uh, we know the aircraft folks, the folks that flies, uh, our commercial airlines and particularly our military. And if you ever go to Disney, Disney World, uh, you spend most of your time in the simulator down there, which I just took my grandkids down there about a month ago, and I probably had the biggest time of my life playing with those simulators.

[00:27:36] Michael Turnbow: But the team, the Ooga team were already onto this long before I went to Disney World. We already knew that they existed. We got people working with us, and we will deliver in time an online simulator that allows Indie folks. To get a large portion of experience, particularly at fault flaw evaluation. Now, actually handling the equipment, all that will [00:28:00] still need to be done, can be done in labs or in schools or whatever.

[00:28:04] Michael Turnbow: But what we want to get to is, uh, every flaw known to man is my gold, our gold. It will be on the simulator. And so a person for the first time in our entire history is MDT. I’ve never got to see them all. They’ll all be there. We, we know people, we know companies, we know, um, sources that has blocks with these flaws in them, and we’re getting, we’re, we’re, we’re working with those folks so we can digitize those flaws, put them on this simulator.

[00:28:31] Michael Turnbow: So I’ll make this short story a little shorter though. The bottom line is it’s got to be experience. We have to prepare the new generation. With all the experience they need to walk out of the school, right into the workplace, throw a flaw at them, and they say, I know exactly what that is. And the Ooga platform, the other thing we’re going to offer people is we got our experts, uh, sitting online and this, this part, I just love, you do have a new trainee in the field and they’re [00:29:00] examining, and there’s a new failure mechanism that nobody’s seen yet.

[00:29:03] Michael Turnbow: We get those every now and then in metal. And so this person now gets to call up a friend, so to speak. Calls up level three, level three answers the phone. He now sends the flaws to this person on his computer. They’re both simultaneously looking at this flaw together and they get to go through the, the, the guy with the 40 years of experience and the guy that’s got four years experience and they, they work together to solve the problem.

[00:29:28] Michael Turnbow: And so I guess I’ll stop there and say that’s. That’s the not just the vision. We’re building towards having that available soon.

[00:29:37] Nasrin Azari: Yeah, that’s, that’s really interesting. I, I love that idea of having, you know, somebody, you know, a young, um, you know, just recently graduated technician have the confidence to be able to go into the field or go into a job and perform, um, because he’s got a job.

[00:29:58] Nasrin Azari: Yeah. He’s either [00:30:00] got simulated experience under his belt, or he’s got access to an expert that can help him out if he has any doubts. So I think that’s, that’s, that’s pretty cool. AJ, did you want to add anything to this topic?

[00:30:12] Ajay Pasupuleti: No, just one point here that I love this simulator idea a lot, and I, I wish, um, and we are looking into moving, transforming this towards the virtual OJT programs, including VR.

[00:30:28] Ajay Pasupuleti: Um, and AR. Um, where I’ve seen the industry, um, have these young kids who are super tech savvy and the older guys, um, are people in the industry, not at all interested in carrying a tablet around. So the young guys, everybody, but both generations love. Wearing a headset. I know Mike does wear a fight with his grandson to take his headset [00:31:00] and play those video games.

[00:31:01] Ajay Pasupuleti: So, uh, it’s, it’s, I, I think we should head towards using virtual, uh, reality. For training and I think that’s going to change things as well.

[00:31:14] Nasrin Azari: Yes. So so you may have already touched on on some of the answers to this final question, but let’s finish the interview by circling back around to the platform and close with this question.

[00:31:27] Nasrin Azari: What business and or industry benefits do you foresee with the remote workforce platform like UGA?

[00:31:34] Ajay Pasupuleti: As you said, we probably touched on all the things in this conversation, but I have to summarize that I would say it is in three points. One, small to medium sized businesses that do not have people or the IT infrastructure can greatly benefit.

[00:31:52] Ajay Pasupuleti: With, um, UGA because they can get on demand IT infrastructure and access to resources and [00:32:00] people, um, when they need, or when the job is calling for. Larger inspection companies can use us, um, or companies themselves, um, can use us from, uh, for the perspective of finding people, or from data storage, analytics perspective.

[00:32:19] Ajay Pasupuleti: that is from the AI side as well. And the, the third and the most important is consultants. This is their revenue stream and auxiliary revenue stream that, hey, you have a few hours a day in the night or over the weekend, you want to make some money, go for it. This is an opportunity for you.

[00:32:39] Nasrin Azari: Very interesting.

[00:32:40] Nasrin Azari: Um, Michael, any final thoughts before we close for the day?

[00:32:45] Michael Turnbow: I think AJ just summed it up. We want to be a platform. That industry can access for almost any need, uh, where it be tech, technology, equipment, software, personnel. The whole nine yards. [00:33:00] Great.

[00:33:00] Nasrin Azari: Great. Well, um, super interesting discussion today. Thank you so much, Michael and AJ for joining us.

[00:33:07] Nasrin Azari: Um, it was very, very relevant, interesting conversation. Um, so thanks for being here today, bringing us such great insights. Thanks also to our audience for listening to this episode. If you’d like to learn more about UGA, AJ or Michael, uh, you can follow the relevant links on our podcast webpage. If you have any feedback or would like to nominate an individual or an organization to be a guest on a future episode, please send a message to one of us here through the contact us form on our website at www.

[00:33:38] Nasrin Azari: floodlightsoft. com. Thanks again for joining us and see you next time. Bye bye.

[00:33:44] Ajay Pasupuleti: Thank you. For more expert views on NDT, subscribe to the Floodlight Software blog, Floodlight Soft.


For more expert views on NDE 4.0, subscribe to the Floodlight Software blog at floodlightsoft.com.

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