Latest / The Adults In The AI Room / The Earley AI Podcast with Seth Earley - Episode #58 AI-Driven Manufacturing: Sahitya Senapathy Talks Data, Analytics and Technology Trends

Transcript

The Earley AI Podcast with Seth Earley - Episode #58 AI-Driven Manufacturing: Sahitya Senapathy Talks Data, Analytics and Technology Trends · The Adults In The AI Room

• 0:00
  SPEAKER_04: Good morning, good afternoon, good evening.
• 0:02
  SPEAKER_04: Welcome to the Early AI podcast.
• 0:05
  SPEAKER_04: My name is Seth Early.
• 0:06
  SPEAKER_05: And I'm Chris Feterson.
• 0:07
  SPEAKER_04: And today we're really excited to introduce our guest to discuss industrial products, industrial uh uh manufacturing, uh, product data, product information management, and how these types of uh approaches can be used, uh AI approaches can be used in that particular space, especially when we talk about improving uh product data.
• 0:29
  SPEAKER_04: So we'll talk about use cases for that, we'll talk about uh uh generative AI and data privacy, and we're also going to be talking about solution execution and deployment.
• 0:38
  SPEAKER_04: Uh so our guest today is an expert and visionary in the field of AI and industrial manufacturing.
• 0:43
  SPEAKER_04: He's currently serving as the CEO of Endeavor.
• 0:47
  SPEAKER_04: He has a remarkable career that includes both working with prestigious institutions such as the US Air Force and studying at the University of Pennsylvania and the Wharton School of Business.
• 0:57
  SPEAKER_04: His professional journey also includes students at AWS and Palentart, where he honed his skills in leveraging advanced technologies to solve complex uh problems.
• 1:08
  SPEAKER_04: Sahetia, uh, and I I don't I didn't ask for your last name, Sanapathi.
• 1:13
  SPEAKER_02: I got it.
• 1:14
  SPEAKER_04: I got it.
• 1:14
  SPEAKER_04: All right, Sahetia Sinapathy.
• 1:16
  SPEAKER_04: Welcome to our show.
• 1:18
  SPEAKER_04: Nice to have you.
• 1:20
  SPEAKER_04: Thanks for having me.
• 1:21
  SPEAKER_04: Really excited for this.
• 1:22
  SPEAKER_04: Terrific.
• 1:23
  SPEAKER_04: So, you know, we like to start off by uh trying to understand what are you seeing in terms of uh misunderstanding, especially in this industrial product space?
• 1:33
  SPEAKER_04: What is it that people are not getting or that you have to go in and explain?
• 1:37
  SPEAKER_04: Uh what are what are some of the misconceptions uh about AI, generative AI, and uh industrial data?
• 1:44
  SPEAKER_00: Wow, getting right into the meat.
• 1:45
  SPEAKER_00: I like it.
• 1:46
  SPEAKER_00: Um, you know, I think the really the really interesting thing is um, you know, folks just aren't aren't familiar with what generative AI is.
• 1:54
  SPEAKER_00: And what happens is this this causes a bit of confusion because people think chat bots are the panacea.
• 1:59
  SPEAKER_00: You know, they think that chat GPT is going to solve all their problems.
• 2:03
  SPEAKER_00: And so when we walk in and people are talking about use cases, people are like, hey, should we, you know, be talking to a maintenance manual?
• 2:10
  SPEAKER_00: Right.
• 2:11
  SPEAKER_00: And um when we think about you know why we actually want to implement generative AI, at the end of the day, in the industrial space, it really comes down to ROI.
• 2:21
  SPEAKER_00: How are you driving you know top line sales or how are you saving me costs and reducing my OpEx?
• 2:27
  SPEAKER_00: And um, you know, the reality is that chatbots aren't going to get you there.
• 2:30
  SPEAKER_00: And that's usually the first misconception that we have to handle.
• 2:33
  SPEAKER_00: How can we use generative AI to drive real ROI in real use cases?
• 2:38
  SPEAKER_04: And so, how do you go about that?
• 2:39
  SPEAKER_04: Where do you start when you uh go into an organization?
• 2:43
  SPEAKER_04: And so they they think that the chatbot is the answer, uh, but obviously there's a lot of stuff that needs to be done.
• 2:49
  SPEAKER_04: It's conversational interaction with uh with their data, uh using things uh using their own data as opposed to the the uh LLM itself or the generative AI itself.
• 3:00
  SPEAKER_04: But where do you begin that process when you start talking to companies?
• 3:05
  SPEAKER_00: Yeah, um, really good question because when you're dealing with enterprises, um it's often the case that you have a bunch of different stakeholders.
• 3:13
  SPEAKER_00: And so, you know, I'll I'll put it into two broad buckets of people who are going to be vital to this process.
• 3:20
  SPEAKER_00: The first is IT umation, right?
• 3:23
  SPEAKER_00: They're gonna be the ones that have the governance and understanding of the data that's around the organization.
• 3:29
  SPEAKER_00: And the second is the business case user, whether we're talking about the sales department or the operations department, the supply chain department that's in charge of procurement.
• 3:38
  SPEAKER_00: And you need both of these forces to come together to come to an understanding.
• 3:43
  SPEAKER_00: Because to really solve these problems in enterprise manufacturing, you're dealing with issues around siloed data.
• 3:49
  SPEAKER_00: You're dealing with issues around messy, unclean data sources and different ERP systems.
• 3:55
  SPEAKER_00: And what this means is that not only are you feeling the immense pain from a business use case standpoint, but you're also seeing the effects in the IT department that they're struggling to.
• 4:05
  SPEAKER_00: And so our process always starts with discovery with IT and with the business case users to identify, you know, where are you feeling the pain in your business?
• 4:13
  SPEAKER_00: Where can we improve your margins?
• 4:14
  SPEAKER_00: But what is the kind of data layer look like that is creating this issue for you?
• 4:21
  SPEAKER_04: And uh, do you want to provide maybe a specific example of uh where you'll go, you're where you will find those kinds of opportunities in a manufacturer?
• 4:30
  SPEAKER_04: And of course, industrial manufacturers uh many times are not at the leading edge of technology.
• 4:36
  SPEAKER_04: They're spending more time on manufacturing processes and making uh investments and in their operations.
• 4:42
  SPEAKER_04: Uh, and many times IT and uh technical capabilities lag a bit.
• 4:47
  SPEAKER_04: So uh tell me a little bit more about you know what you see on the ground and you know what those specific uh needs might be.
• 4:55
  SPEAKER_00: Yeah.
• 4:56
  SPEAKER_00: So um I'll kind of give you the IT standpoint and how this ends up manifesting in the business use case.
• 5:02
  SPEAKER_00: With the IT folks, you know, you have this accumulation of legacy softwares in the industry.
• 5:08
  SPEAKER_00: So you've got ERP systems, right?
• 5:10
  SPEAKER_00: Your enterprise resource planning like Oracle and SAP.
• 5:13
  SPEAKER_00: Then you've got the manufacturing execution systems, the MESs.
• 5:17
  SPEAKER_00: And then you introduce all sorts of different unstructured data sources.
• 5:20
  SPEAKER_00: And this is actually the long tail of unstructured data, is really where genitive AI comes into play as a massive infrastructure force multiplier for these companies.
• 5:28
  SPEAKER_00: And uh you've got email, you've got spreadsheets, you've got Word documents, you've got PDFs, and um every single manufacturer, whether you're talking about small mom and pop shop that's on the street across the corner, or you're talking about, you know, the biggest manufacturer with 30 plants across the world, they have this problem where different plants that have you know come together as a result of MA activity, have different ERP systems, they have different MES systems, they're collecting different sensor data on the shop floor.
• 5:54
  SPEAKER_00: Every person at the plant's got their own spreadsheets that they're doing analysis and Word documents.
• 5:59
  SPEAKER_00: And the first step is kind of just analyzing this.
• 6:01
  SPEAKER_00: And um, you know, for I'll give you a tangible example.
• 6:03
  SPEAKER_00: Let's say we're dealing with sales folks, right?
• 6:05
  SPEAKER_00: And we've identified, hey, you know, there's a problem with lagging sales volume for the last five years.
• 6:11
  SPEAKER_00: We want to start automating our sales workflows.
• 6:13
  SPEAKER_00: What this means is we start looking on a plant-by-plant basis, and the results might be hey, you have different product catalogs in different ERP systems.
• 6:20
  SPEAKER_00: One plant might be on SAP, the other one's on Oracle, and there are discrepancies in the product catalog and there's unreconciled master data.
• 6:29
  SPEAKER_00: How do we start to unify all of this data using genitive AIs at infrastructure accelerant, and then use that to start automating the workflow to drive higher sales volume?
• 6:38
  SPEAKER_00: And that's how that kind of process looks.
• 6:42
  SPEAKER_05: Yeah, I think you're on mute, Chris.
• 6:45
  SPEAKER_05: Yeah, that would be helpful.
• 6:46
  SPEAKER_05: So Sahiti, when you when you dive into it, I'm I can only imagine that also some of the misconceptions come from the you know, from the FUD, right?
• 6:54
  SPEAKER_05: The fear, uncertainty, and doubt with a lot of these because Seth, you brought up a good point around that this is not their core area of business technology.
• 7:04
  SPEAKER_05: Their core area of business, like you said, is manufacturing and industrial.
• 7:07
  SPEAKER_05: So, how do you take them from, you know, from understanding like their core aspects to, you know, they know they want or need or they have all this AI, you know, that's being articulated to them.
• 7:21
  SPEAKER_05: What kind of process do you use to actually take them from, you know, from basically almost a trust, but into an area where they're like, you know what, Sahitya?
• 7:29
  SPEAKER_05: Yeah, I need what you have, you know, and I know that I need this, but I don't know how and why.
• 7:34
  SPEAKER_05: So what's that process look like to get them to that point of, you know, pulling the trigger?
• 7:39
  SPEAKER_00: Yeah.
• 7:40
  SPEAKER_00: It's a really interesting question because um, you know, we feel the strongest pull from the types of manufacturers that have actually tried to build this in-house.
• 7:48
  SPEAKER_00: You know, there's this legacy of companies and kind of software providers that are out there that essentially do a custom development and then do a handoff to the manufacturer.
• 7:57
  SPEAKER_00: And this is kind of part of the problem in this space because you've got all these one-off legacy softwares that someone has handed off to a manufacturing enterprise, and they're not in the business of software for maintenance, right?
• 8:07
  SPEAKER_00: This is why we had the satisfaction of industry, because we had dedicated providers who actually maintain products.
• 8:13
  SPEAKER_00: You have all these point solutions that exist.
• 8:15
  SPEAKER_00: And so there's still people in the industry who are trying to build their own solutions in-house because they think it gives them an advantage because they can vertically integrate their software sector.
• 8:23
  SPEAKER_00: The reality is that it doesn't.
• 8:25
  SPEAKER_00: It's harder.
• 8:26
  SPEAKER_00: But when they kind of try building it, then they realize the difficulty of actually building and maintaining a generative AI product, let alone a traditional SaaS product.
• 8:34
  SPEAKER_00: Then they come to us and they're like, we understand the problem, we know the use case.
• 8:39
  SPEAKER_00: You know, you're just gonna be better equipped to do this sort of thing and maintain it for us.
• 8:43
  SPEAKER_00: So that's the first kind of layer there where it really accelerates the kind of process.
• 8:47
  SPEAKER_00: Now, at the second level, how do we actually get everyone comfortable?
• 8:50
  SPEAKER_00: Well, in terms of we finished Discovery before going into an annual contract, let's do a bit of a proof of concept or a pilot, right?
• 8:58
  SPEAKER_00: Whatever you want to call it.
• 8:59
  SPEAKER_00: Let's actually get this into the hands of users so that not only can the executives get confidence that, hey, this is gonna be a real ROI driver for us, let's actually get the people who are using it acclimated and comfortable with the idea of using this tool day to day.
• 9:14
  SPEAKER_00: You know, because there's no point in putting out a generative AI tool for the sake of it if it's gonna be shelfware.
• 9:19
  SPEAKER_00: Let's actually get something that people are using every day and feel like are adding true value to their operations.
• 9:26
  SPEAKER_05: Yeah, that's interesting.
• 9:27
  SPEAKER_05: So, you know, part of that too is just, you know, like we do with all most use cases with these different orgs is the crawl walk run, right?
• 9:34
  SPEAKER_05: Figure out what this looks like from the technologists that can implement it, help them understand, you know, what that is, drive the value work backwards from that solution, right?
• 9:44
  SPEAKER_05: And then actually, like you said, put up a proof of concept, some type of an MVP that makes sense so they can then you know continue to get you know additional investments.
• 9:52
  SPEAKER_05: So, you know, in terms of, I mean, that's pretty pretty standard right across the board.
• 9:58
  SPEAKER_05: Um, where have you seen, like whether in in industrial manufacturing where you know that's does it take a little bit longer in most in in some cases, or you know, what does that look like?
• 10:09
  SPEAKER_05: Especially, I mean, because you're dealing with these huge organizations, right?
• 10:14
  SPEAKER_05: And so, yeah, I mean, I'm I'm assuming that process is the same, but is there any nuances you know to this type of technology and help among over the hump of understanding?
• 10:24
  SPEAKER_00: Definitely.
• 10:24
  SPEAKER_00: Um, you know, there's kind of a lot of different layers to it.
• 10:28
  SPEAKER_00: I think the first is uh in manufacturing, you're dealing with enterprises that have lots of different plants or locations that uh have various processes and data sources from plant to plant.
• 10:39
  SPEAKER_00: And our approach is, you know, why don't we go piecemeal?
• 10:42
  SPEAKER_00: Let's start with two plants to start out with and then expand this across your organization.
• 10:47
  SPEAKER_00: And we've done this, right?
• 10:48
  SPEAKER_00: We started with two plants, and now we are deploying enterprise-wide.
• 10:52
  SPEAKER_00: And um, that's where the true benefit is, but it really helps them kind of slowly move into it.
• 10:57
  SPEAKER_00: And I think this is something very interesting about manufacturing um because of that dynamic, that MA dynamic that leads to these really large companies.
• 11:05
  SPEAKER_00: Um, you know, I think I think the second thing there is like can we accelerate implementation?
• 11:11
  SPEAKER_00: And um generative AI is is also a great internal tool for us as a company, right?
• 11:16
  SPEAKER_00: Can we, you know, develop proof of concepts for you faster, you know, by using generative AI to accelerate our software development process, which means that you can start seeing what the tool is like next week rather than you know three months or six months, which is you know traditional.
• 11:32
  SPEAKER_05: Do you do you find that it um, you know, especially manufacturing, because it's been the lifeblood of you know America for a long time, right?
• 11:39
  SPEAKER_05: And the US.
• 11:40
  SPEAKER_05: And um, you know, it's I think it's easier, you know, and my comment is you know, we'll preface a little bit of context, but it's easier for a lot of SaaS companies and technology companies to figure out what to do.
• 11:51
  SPEAKER_05: And like you said, I like on your site where it says, um, you know, chatbots won't make you money, but endeavor will, right?
• 11:58
  SPEAKER_05: And that, you know, that has to be part of like, you know, we'll get into a little bit more in terms of handling data, but you know, is there anything unique to maybe their their you know, their data?
• 12:09
  SPEAKER_05: Because we have, I mean, I'm assuming schematics, and we got, like you said, all their own structured data can be very, very complex and yet, you know, pretty simple as well, right?
• 12:19
  SPEAKER_05: And how do you how do you help them understand like, you know, what is this, what could and should this look like, right?
• 12:25
  SPEAKER_05: In terms of of, you know, actually let's let's let me scrap that.
• 12:29
  SPEAKER_05: Let me go back to back to something.
• 12:31
  SPEAKER_05: So, Carolyn, note the time of that one.
• 12:34
  SPEAKER_05: I think the one thing I want to, you know, key in fix it in post.
• 12:38
  SPEAKER_05: Yeah, exactly, fix it in post.
• 12:40
  SPEAKER_05: So the one thing I want to key into, you know, with this idea is um, you know, when we start talking about these, you know, the lead the lifeblood of of America for a lot of history and stuff, is like we talked about, it's not their core to be with technology, but they're forced.
• 12:57
  SPEAKER_05: Do you find that that helps accelerate or does that hinder the process at all?
• 13:03
  SPEAKER_00: You know, I think it's really interesting because uh to your first point, yes, you know, manufacturing and trade, you know, these are these are the core of the American economy.
• 13:14
  SPEAKER_00: And, you know, one of the things that we've seen over the last you know several decades is, you know, we've gone from being this manufacturing powerhouse post-World War II to now being more of a services economy, right?
• 13:24
  SPEAKER_00: And um, you know, it it's it's interesting because we've also seen global competition rise over these sorts of things.
• 13:30
  SPEAKER_00: And uh, we're based in Silicon Valley, Endeavor, and there's a lot of activity now around manufacturing.
• 13:36
  SPEAKER_00: And you have these you know new age manufacturers that are trying to produce, you know, missiles or or tanks, right, in the Silicon Valley style of things, move fast, break things.
• 13:45
  SPEAKER_00: Um, but but really the bedrock of the American economy is those traditional industries in the Midwest, like the steel, the construction, the automotive, right?
• 13:55
  SPEAKER_00: And um, you know, they are the companies where I feel like they can most strongly benefit from AI.
• 14:01
  SPEAKER_00: And you'd expect kind of from the outside looking in, you know, if you know they're based in the Midwest, you know, they're not on the coast, they're not on the West Coast, East Coast, there's gonna be less tech saturation, maybe they're more averse to technology.
• 14:11
  SPEAKER_00: But it's really interesting because you go into them and they recognize the problems and they understand the potential that technology has.
• 14:17
  SPEAKER_00: It's kind of just a question of how are they equipped to solve those sorts of things.
• 14:22
  SPEAKER_00: And coming back to that point, which is, you know, build versus buy, the folks are gonna try to build it, they're gonna try to build out their IT departments.
• 14:29
  SPEAKER_00: They might go to individual consultants, but in the long run, it's just better off if you go to a provider who is, you know, interested in the long-term maintenance of the software in actually driving the daily usage rather than you know doing a time and money implementation, because then they're just gonna drag it out.
• 14:44
  SPEAKER_00: And um, I think it's you know a cultural thing.
• 14:46
  SPEAKER_00: There's there's been so many manufacturers that have just been burnt by um, you know, industry 4.0, um, sort of, you know, uh, you know, uh, you know, what was the thing?
• 14:56
  SPEAKER_00: Digital uh twin.
• 14:57
  SPEAKER_00: Digital twin was the big thing a couple of years ago.
• 15:00
  SPEAKER_00: And uh the reality is that it hasn't especially delivered quite yet on that ROI thing I was talking about.
• 15:07
  SPEAKER_00: Has it delivered top line revenue or cost savings?
• 15:10
  SPEAKER_00: And you know, I think we would be hard pressed to find out, you know, if there's a single company that did that especially well.
• 15:16
  SPEAKER_00: And my hypothesis and you know, why we founded Endeavor was because you know, the problem isn't analytics, you know, the kind of technology behind analytics has not really fundamentally innovated uh, you know, in the past 10 years, but the problem is data.
• 15:32
  SPEAKER_00: It's actually, are we using the right data?
• 15:35
  SPEAKER_00: Um, and the question is, can we bring that unstructured data in and actually apply the kind of technology, the analytics, to really deliver on this digital twin and the industry 4.0 technology?
• 15:46
  SPEAKER_00: And the way we can, I'll give you a concrete example of this in manufacturing is there's a you know, a lot of these manufacturers, again, in the Bad Rock economy, have 10, 20 years worth of data.
• 15:56
  SPEAKER_00: And uh they should be using it.
• 15:58
  SPEAKER_00: And um, you know, they've got this data that that should be mined from their emails, their requests for quotes, their bill of materials that's stuck in spreadsheets.
• 16:06
  SPEAKER_00: And you can start to do better forecasts, you can start to do better analytics, you can start to do better planning by mining this unstructured data and using it in a combination with the structured data they've been using for their existing planning.
• 16:18
  SPEAKER_00: And that's how I think we really get to amazing solutions for analytics, for forecasting, using generative AI in this manufacturing space.
• 16:26
  SPEAKER_05: I think, you know, Seth, this is near and dear to Seth, right?
• 16:29
  SPEAKER_05: In terms of knowledge management, knowledge graphs, you know, um, knowledge infrastructure, which I would argue in that space is probably pretty immature, right?
• 16:41
  SPEAKER_05: And so, you know, I mean, I've just in the organizations I've met, they're just barely getting to, okay, we've got our data warehouse up, all fact and dimension based, that we've been driving off our transactional system.
• 16:53
  SPEAKER_05: And yet, to your point, all that unstructured data that's sitting there and the decades and decades worth of information that's either NoSQL, unstructured, or is structured, and they aren't doing anything with it to try to find those patterns, you know.
• 17:06
  SPEAKER_05: And I would argue too that maybe even not even not knowing the right question to ask of the data is probably more indicative of you know what to do with it.
• 17:16
  SPEAKER_05: Right.
• 17:17
  SPEAKER_04: So I have a question, give me a sense of a capability.
• 17:23
  SPEAKER_04: Um, and it's those are good points, by the way, Chris, you know, in terms of uh the knowledge infrastructure and and so on.
• 17:30
  SPEAKER_04: But you know, give me a sense of what a capability would look like that you leave a customer with.
• 17:36
  SPEAKER_04: Like what are they able to do differently at the end of the day?
• 17:40
  SPEAKER_00: Yeah.
• 17:41
  SPEAKER_00: Let me kind of walk you through like a journey of of what an enterprise customer might experience with Endeavor.
• 17:47
  SPEAKER_00: And we think about it in without making it a commercial.
• 17:51
  SPEAKER_00: Yeah, yeah, of course.
• 17:53
  SPEAKER_00: Um you know, we think about it in three phases, which is you know, the first layer is data.
• 17:58
  SPEAKER_00: What are you able to accomplish with data?
• 18:01
  SPEAKER_00: And um, what you're able to do is bring in your silo data sources.
• 18:05
  SPEAKER_00: You're able to start unifying, you know, data that's in different schemas, different uh databases, different formats, and um in PDFs and and and and spreadsheets in a structured format, which has historically been you know impossible to do um without generative AI, right?
• 18:22
  SPEAKER_00: The ability to tap into different formats and unify them and standardize it.
• 18:26
  SPEAKER_00: Now, what you can do using that data, the data is just a means.
• 18:29
  SPEAKER_00: It's start automating.
• 18:31
  SPEAKER_04: It's costly and it takes some work.
• 18:34
  SPEAKER_04: Um, I mean, we did a project for applied materials where we integrated 14 different data sources and gave us unified uh uh front end to that, a search-based application.
• 18:45
  SPEAKER_04: And it takes a lot of work, uh, but you also have to have good content and and data uh lifecycle management.
• 18:51
  SPEAKER_04: But in any case, uh Gen AI gives you some new capabilities in that space.
• 18:56
  SPEAKER_04: But uh continue, carry on.
• 18:57
  SPEAKER_00: Sure.
• 18:58
  SPEAKER_00: And um, you know, I think what you can do using the kind of data is start to automate, right?
• 19:03
  SPEAKER_00: And um, that's immediate time to value, which is you take this data, now it's standardized.
• 19:08
  SPEAKER_00: Can we replace you know something that's labor intensive for us so that our existing personnel can focus on higher value tasks?
• 19:15
  SPEAKER_00: So, for example, you know, you have a massive team of account executives that uh both interface with the customer, but they also are in charge of quoting.
• 19:24
  SPEAKER_00: You know, can we shift their focus to doing more customer FaceTime, work on order improvement, product recommendations, and reduce their menial workflow for generating quotes using the generative AI technology?
• 19:37
  SPEAKER_00: Now, the final layer there is the analytics piece, which is really what everyone wants at the end of the day.
• 19:44
  SPEAKER_00: As an executive, what you want to understand is uh, you know, what does my sales forecast look like?
• 19:49
  SPEAKER_00: You know, what products are gonna be high in demand and what are customers gonna request?
• 19:53
  SPEAKER_00: And now you can tap into all that data that you've automated, you've structured that unstructured data in the PDFs and email and spreadsheets.
• 20:01
  SPEAKER_00: Now let's start driving insights and analytics so that executives can make better decisions.
• 20:06
  SPEAKER_00: And that's kind of the three-step phase that we think about.
• 20:10
  SPEAKER_04: Okay.
• 20:11
  SPEAKER_04: And so again, when you come back to what's going to be different, can you give a specific example?
• 20:17
  SPEAKER_04: I mean, in terms of um you mentioned sales forecasting.
• 20:22
  SPEAKER_04: Uh uh, so you're saying that it would be easier to do sales forecasting with fewer steps.
• 20:27
  SPEAKER_04: Um what else would uh would there be an impact on the customer experience?
• 20:31
  SPEAKER_04: Would there be an impact on the employee experience uh in terms of you know, again, integrating these different structured and unstructured data sources?
• 20:39
  SPEAKER_04: So, you know, is there a uh service uh uh uh component of this?
• 20:44
  SPEAKER_04: Uh is there a self-service component?
• 20:47
  SPEAKER_04: What are the kinds of things that you're seeing um really get traction with customers?
• 20:52
  SPEAKER_00: Yeah.
• 20:52
  SPEAKER_00: So at the automation layer, um again, it's immediate time to value in the sense that for quoting, right?
• 20:59
  SPEAKER_00: You can immediately push out these quotes, which means traditionally your customers might have to wait a week or or you know, four days before they get a quote.
• 21:07
  SPEAKER_00: And they're kind of just twiddling their thumbs trying to get, you know, what is this gonna cost me?
• 21:12
  SPEAKER_00: Now you can start to immediately respond and almost get a frictionless, touchless experience that your customers are really gonna appreciate because it's faster.
• 21:21
  SPEAKER_00: Same for your inside sales reps, right?
• 21:23
  SPEAKER_00: They're gonna be able to interface with the customer and actually get more FaceTime so that your customers are talking to people instead of talking to a machine.
• 21:30
  SPEAKER_00: Um, now the analytics piece is a bit different because it's it's not only the ability to forecast better, but it's the ability to amalgamate all this information you hadn't been able to do before.
• 21:40
  SPEAKER_00: Because, like you said, you know, you can do it, but it'd be very costly to start using that unstructured data.
• 21:45
  SPEAKER_00: And now you can do so in the same sense of traditional analytics, where you can do big data analytics, but you can do so with unstructured data in the same way that you'd be mining numeric data.
• 21:55
  SPEAKER_00: Now with the non numeric.
• 21:57
  SPEAKER_05: Yeah, because that time series data is going to be super critical.
• 22:00
  SPEAKER_05: Critical for planning and for predictive, you know, predictive planning, but you know, the supplier piece.
• 22:06
  SPEAKER_05: I think what's interesting too, Seth, you know, just in terms of a lot of this is, and so hit you centered around, I feel like especially in a lot of ERP and a lot of supply chain, a lot of these types of technologies, these classic legacy technologies, they over-index on planning, they over-index on potentially logistics, right?
• 22:28
  SPEAKER_05: And things like that, like supplier planning, you know, um, you know, uh goods on hand, all that kind of stuff, but they don't on the actual real-time manufacturing piece.
• 22:40
  SPEAKER_05: Like, you know, what does that look like in terms of the flow, what's happening, you know, real time, what's going on right now.
• 22:47
  SPEAKER_05: And then how does that give me the ability to predict what my output is going to be?
• 22:52
  SPEAKER_05: And at the same time, look at it from the perspective of supplier management coming in, looking at goods on hand from their perspective and what that can give me.
• 23:01
  SPEAKER_05: Because I think we get like the planning right, but it's all those other aspects and the connective tissue between them that lose focus.
• 23:07
  SPEAKER_05: Is that a fair assessment or is that, you know, like, am I missing it?
• 23:12
  SPEAKER_00: No, I think that's absolutely right.
• 23:13
  SPEAKER_00: You know, I think planning is the end-all be-all.
• 23:16
  SPEAKER_00: Everyone wants to do better planning.
• 23:17
  SPEAKER_00: It's just a question of do we have the right data?
• 23:20
  SPEAKER_00: Yeah.
• 23:20
  SPEAKER_00: And it's about it's about bringing those different processes, those siloed data sources and connecting the different ERP systems really hard because of the lack of standardization, using the unstructured data on hand to drive better decisions.
• 23:32
  SPEAKER_05: Yeah, because you have all that real-time stuff hitting the conveyors and coming through in a you know a classic, let's say, you know, supplier type of perspective.
• 23:40
  SPEAKER_05: But then, you know, when that gets into the actual manufacturing floor and you know, and then you like you see your combons decreasing, and then that, you know, adding to the you know, the output of the products that you're that you're doing.
• 23:53
  SPEAKER_05: Part of that's, you know, I mean, you're you may be the best part about this is you know, your suppliers are feeding you, and you may be a supplier for something else.
• 24:01
  SPEAKER_05: And part of that too is you know, making sure that predictability for your downstream is also there.
• 24:07
  SPEAKER_05: And then Seth, to your point, you got all of the you know, HR and employees and you know, engineers, and you know, everybody that's centered around you know the gravitational pull of what the core product is to actually you know generate all this stuff and then you know get answers quickly, right?
• 24:23
  SPEAKER_05: Because it's you know, the most critical thing is hey, what in the hell is going on within my you know company right now?
• 24:30
  SPEAKER_05: And I have no idea, you know, so I need to be able to see that.
• 24:33
  SPEAKER_05: Um, but anyway, yeah, yeah.
• 24:36
  SPEAKER_04: And and you know, when you think about what you're trying to do there, you're trying to speed up the information flows throughout the enterprise and get people the right information that they need when they need it, the old mantra of knowledge management and personalization and contextualization.
• 24:51
  SPEAKER_04: How do you how do you go about um fixing that data?
• 24:54
  SPEAKER_04: You know, you talked about the fact that it needs to be standardized.
• 24:57
  SPEAKER_04: What does that process look like?
• 25:00
  SPEAKER_00: So we do what's called a forward deployed motion, which means we embed very deeply with the client.
• 25:06
  SPEAKER_00: Um and often we go in person to visit our enterprise clients.
• 25:10
  SPEAKER_00: And um, I say we don't do a deal without actually visiting our clients, and it's it's very important to actually go meet the folks that are in charge of the data to view the data, to actually kind of like, you know, start, you know, experimenting with it, and then look at the schemas, look at the columns, understand the process, and then start unifying it into one thing that is standard.
• 25:29
  SPEAKER_00: And then you can really accelerate the process of this schema standardization, this data cleaning using generative AI, which I think is new, right?
• 25:37
  SPEAKER_00: Um, it's laborious to do without, but uh, you know, you have a long history of these ERP companies that focus on standardization.
• 25:45
  SPEAKER_00: But if you can now start to actually have a semantic understanding of a process and of a data table and the associated schema, well then now you can start to reconcile different tables when there are variations from plant to plant and location to location business divisions.
• 26:03
  SPEAKER_05: It's interesting.
• 26:05
  SPEAKER_05: So do you see that across like you know, the, you know, more relevant across, let's say, the sales quoting side of things or material planning or you know, just pricing in order to entry.
• 26:17
  SPEAKER_05: Where do you see that as the most applicable?
• 26:20
  SPEAKER_00: I we really see that sort of thing on the operations side for manufacturing.
• 26:26
  SPEAKER_00: You know, there are going to be variations on the sales side for sure.
• 26:30
  SPEAKER_00: You know, there's gonna be different product catalogs, there might be different processes, but for the most part, ideally you're you're quoting the same way from plant to plant, at least 80, 90 percent.
• 26:40
  SPEAKER_00: But when you get to operations, you know, it's kind of the wild west.
• 26:43
  SPEAKER_00: Because again, these companies have probably been around for a long time.
• 26:47
  SPEAKER_00: They they're almost like you know, you have four or five different companies under the same umbrella.
• 26:51
  SPEAKER_00: Yeah.
• 26:52
  SPEAKER_00: And they've been doing things for the same way for the last 30 years, and they're still operating that same way.
• 26:57
  SPEAKER_00: And when you look at a different region, you might look at you know the division and the West Coast and the East Coast.
• 27:02
  SPEAKER_00: They may have completely divergent strategies for producing a particular good, which means you know, figuring out the materials that you need for it, figuring out how to actually use those materials to produce good, then scheduling them on your shop floor.
• 27:15
  SPEAKER_00: Um, and uh the the idiosyncratic processes that exist from plant to plant and location to location is really where the kind of fractured systems come into play and the lack of information.
• 27:26
  SPEAKER_05: I would argue how many of those are just managed by a single spreadsheet in each one of them, right?
• 27:31
  SPEAKER_05: With their own process, their own models, whatever, all done in a in a spreadsheet and with the worst file system ever, email.
• 27:39
  SPEAKER_05: Yep.
• 27:39
  SPEAKER_05: Right?
• 27:40
  SPEAKER_05: Yep.
• 27:40
  SPEAKER_00: I I would probably guess 100%.
• 27:42
  SPEAKER_00: You know, I would be hard pressed to find a single manufacturer that that does not use Excel.
• 27:48
  SPEAKER_05: Which you have these huge systems like SAP and Oracle, and yet everything's still sitting in Excel as their day-to-day work productivity tool, right?
• 27:58
  SPEAKER_05: And that's what they're going through, which is you know, is scary at the same time as that's just what you know how things have been done.
• 28:05
  SPEAKER_05: So continue to do that.
• 28:07
  SPEAKER_00: Yeah.
• 28:07
  SPEAKER_00: Well, it's funny because you could probably just ask someone, uh, you know, in in Silicon Valley here, hey, what do you think is the most used software in manufacturing?
• 28:15
  SPEAKER_00: And they might be like, hey, is it Oracle?
• 28:17
  SPEAKER_00: Is it SAP?
• 28:18
  SPEAKER_00: But the reality is, you know, you know, I'm from Texas, you know, we've actually been out there in the real world.
• 28:23
  SPEAKER_00: It's like it's Excel.
• 28:25
  SPEAKER_00: It's actually Microsoft Excel and Outlook.
• 28:28
  SPEAKER_05: And then all reports are done through PowerPoint.
• 28:32
  SPEAKER_05: Yes, exactly.
• 28:33
  SPEAKER_05: Yeah, that's interesting.
• 28:35
  SPEAKER_05: So um, you know, I don't know if it's a it's it's a good time to get into it because I think one of the other aspects to you know, set that we often, you know, kind of dig into with these is, you know, do you see um or or maybe the better question is with a lot of these organizations that are also still, you know, that have trepidation about AI, um, what do you see about them in terms of insecurity around data sharing and data privacy and all those kind of things?
• 29:06
  SPEAKER_05: Because I'm yeah, they kind of go hand in hand, right?
• 29:09
  SPEAKER_00: That's that's a really great question because it always comes up on that first conversation.
• 29:14
  SPEAKER_00: In fact, I think it's not even about cost, it's about data, you know, like what's gonna happen with our data because you have all these horror stories.
• 29:22
  SPEAKER_00: For example, like Samsung, you know, employee puts in some proprietary data into Chat GPT and there's leakage there, right?
• 29:29
  SPEAKER_00: It's not protected.
• 29:30
  SPEAKER_00: And uh, you know, the first thing we get is like, you know, who owns the data?
• 29:34
  SPEAKER_00: And our answer is you own the data, right?
• 29:36
  SPEAKER_00: We're not in the we're not a Facebook, you know, we're not in the business of reselling or sharing other people's data.
• 29:41
  SPEAKER_00: You own the data that goes into the model.
• 29:43
  SPEAKER_00: And um, we don't do any data sharing.
• 29:46
  SPEAKER_00: And uh, you know, and usually that's that's the biggest concern people have.
• 29:49
  SPEAKER_00: Are our insights, the learnings from our data, going to improve my neighbor's business?
• 29:55
  SPEAKER_00: Because we don't want that.
• 29:56
  SPEAKER_00: And we don't do any of that, um, even though you know there are other companies that are in the business of doing that, you know, doing a you know, grouped pool of data and improving their models based on that.
• 30:05
  SPEAKER_00: But you know, our systems are architected such that you don't need to.
• 30:09
  SPEAKER_00: The second layer of that is your traditional you know, security and data management best practices, like you know, do SOC2 type two, you know, make sure that you're hosting everything through you know a good cloud provider, make sure that you are hosting things in the right data centers and doing you know the right kind of encryption at rest and in transit and hosting data in the right kind of sources.
• 30:27
  SPEAKER_00: And um, you know, without those sorts of best practices that we have a great lineage for, uh it doesn't really make sense to be in the business of enterprise sales.
• 30:36
  SPEAKER_05: So define irony that we're worried about our our data and data security.
• 30:41
  SPEAKER_05: However, we haven't touched that data in 20 years.
• 30:44
  SPEAKER_05: Yep.
• 30:45
  SPEAKER_05: Right?
• 30:45
  SPEAKER_00: And uh it it's this, yeah, go ahead, go ahead.
• 30:48
  SPEAKER_00: No, go, you're good, you're good.
• 30:50
  SPEAKER_00: It's it's interesting because this also leads to the next question of prem, right?
• 30:54
  SPEAKER_00: Which is everyone wants yet.
• 30:57
  SPEAKER_05: So yeah, because now it's not only is it like you said, not only is it we haven't touched it, but we're super we're super insecure about it because we don't want anybody to touch it, but it's sitting in in a bunch of prem systems, going back to your original point of who the hell is going to be there to manage all of that and keep it updated and it's still on you know, disk, hello, maybe maybe it's on you know magnetic tape somewhere in in Iron Mountain, you know what I mean?
• 31:22
  SPEAKER_05: And they don't know why they do it, but it was the best practice of the day, right?
• 31:27
  SPEAKER_00: Yeah.
• 31:28
  SPEAKER_00: You know, when I think about it, there are definitely some areas where it makes a whole lot of sense to have prem, right?
• 31:35
  SPEAKER_00: If you're if you're you know one of the big auto companies and you've got some proprietary designs and you really don't want anyone leaking those designs, yeah, like like keep that prem.
• 31:45
  SPEAKER_00: If you're Coca-Cola and you've got your secret recipe, obviously it makes sense to lock that up.
• 31:50
  SPEAKER_00: But if we're talking about you know ordinary data, you know, having it on-prem and then trying to deploy a local model like a llama or a Mistral and bring that in from a prem standpoint because we you want the generative AI, but you still want to maintain your data sovereignty in your own hardware is just extremely challenging.
• 32:10
  SPEAKER_00: And you know, the kind of compromise that we often get to is hey, let's deploy this in your VPC, in your virtual private cloud, right?
• 32:16
  SPEAKER_00: It's a hybrid prem solution.
• 32:18
  SPEAKER_00: You can deploy a language model in their cloud, but they can maintain the kind of network and security that's associated with it.
• 32:25
  SPEAKER_00: And that's a happy medium for these large enterprises that care about maintaining the kind of data privacy and sovereignty.
• 32:32
  SPEAKER_00: Yeah, that's yeah.
• 32:33
  SPEAKER_04: I mean, at the same time, you know, there's so much that's in the cloud that um people are kind of used to the fact that you have to secure your your data.
• 32:42
  SPEAKER_04: I mean, you're using cloud providers, you're using you know software as a service.
• 32:47
  SPEAKER_04: And uh just in a way, no different, right?
• 32:50
  SPEAKER_04: Because you still have to you still have to have compliance and security standards.
• 32:54
  SPEAKER_04: Um you had a very interesting uh set of experiences.
• 32:57
  SPEAKER_04: Sorry about the cat in the background.
• 32:59
  SPEAKER_04: I don't know if you can hear that, but uh no, you can't.
• 33:02
  SPEAKER_04: Okay, it's really loud.
• 33:05
  SPEAKER_04: Good good microphone.
• 33:07
  SPEAKER_04: Um so uh you had some interesting experiences in terms of internships and uh early in your uh in your education.
• 33:14
  SPEAKER_04: You want to talk a little bit about that?
• 33:16
  SPEAKER_04: What did what did you kind of uh go through when you were uh a teenager and uh you did some interesting internships?
• 33:22
  SPEAKER_04: Let's uh just reflect on that a little bit.
• 33:24
  SPEAKER_04: You want to tell that story?
• 33:26
  SPEAKER_00: Yeah, happy to.
• 33:27
  SPEAKER_00: Um I started coding when I was 11, and uh nowadays this is not entirely uncommon.
• 33:33
  SPEAKER_00: But uh, you know, unlike you know a lot of my friends who got started early and you know, built a video game or a mobile app, the first app I ever built was for FEMA, the government agency.
• 33:43
  SPEAKER_00: And let me I can tell you the story here.
• 33:45
  SPEAKER_00: You know, we had a tornado rip through Texas um from Dallas, and uh, you know, a lot of tornadoes come through, and uh the roof of our house got ripped off.
• 33:53
  SPEAKER_00: And uh 11-year-old me is you know, talking to my parents, like, hey, why haven't they fixed the house yet?
• 33:58
  SPEAKER_00: You know, what's going on?
• 33:59
  SPEAKER_00: And they're like, oh, you know, like FEMA, whatever, X, Y, and Z.
• 34:02
  SPEAKER_00: So, you know, I took my parents' computer and I emailed the guy at FEMA, uh, and I was like, hey, you guys got to fix our house.
• 34:09
  SPEAKER_00: And I had my parents drive me, you know, 40, 50 minutes down from Dallas to Denton, and I went and pitched the guy at FEMA.
• 34:16
  SPEAKER_00: And um, I think there's like some news article about this, but I was like, What did you pitch to him?
• 34:21
  SPEAKER_00: So I pitched him, I pitched him, I was like, you know, why is it so slow?
• 34:25
  SPEAKER_00: And the guy's like, you know, all of our processes are running on paper.
• 34:28
  SPEAKER_00: You know, we have a lot of phone and communication overhead, and I'm like, you guys should digitize this.
• 34:33
  SPEAKER_00: And so I built them, you know, the first thing I ever built was this.
• 34:36
  SPEAKER_04: Do you walked in the door as an 11-year-old?
• 34:38
  SPEAKER_04: Like usually you hear you, usually you hear, you know, they didn't know how old they was.
• 34:42
  SPEAKER_04: We never did anything on video, they just saw email.
• 34:45
  SPEAKER_04: But you actually walked in there and they see a little kid or usually your age.
• 34:49
  SPEAKER_00: There's a picture, if you search my name up, with me at uh, I don't know, some like four feet tall, and this this actual adult uh FEMA chief.
• 34:59
  SPEAKER_00: And uh no, it's a it's a crazy picture.
• 35:02
  SPEAKER_00: Um and I go in there and uh, you know, it it it's it's you know, one of the most incredible starts of the journey there because that's how you got started coding.
• 35:10
  SPEAKER_00: Yeah, that's why I'm interested in traditional industry.
• 35:13
  SPEAKER_00: I don't know if you've got that picture pulled up, um, but you can I don't.
• 35:16
  SPEAKER_04: Um if you want to pull it up, uh it would be great.
• 35:19
  SPEAKER_04: I will.
• 35:20
  SPEAKER_04: I'd have to spell out your name here.
• 35:23
  SPEAKER_04: Oh, here it is.
• 35:23
  SPEAKER_05: Uh let me just yeah, I got it.
• 35:26
  SPEAKER_05: You got it.
• 35:27
  SPEAKER_05: Yeah, sixth grader helps develop uh a plan for first responders.
• 35:31
  SPEAKER_00: Yep, yep, that's the one.
• 35:33
  SPEAKER_05: Yep, that's great.
• 35:35
  SPEAKER_05: You know, we did not unlike you know other government agencies and stuff, I was pulled into pandemics, which again, you know, in 20, I think that was 2007 or eight, all a paper process, like you said.
• 35:50
  SPEAKER_05: And it was amazing to me that that's what was acceptable.
• 35:54
  SPEAKER_05: You know, how do you, you know, part and parcel to this is how are you able to respond real time to needs of people, which arguably now we're into the emergency aspects with using paper and um and phone, you know, tag that is processes that's that could have started in the early 1900s.
• 36:16
  SPEAKER_05: And this is what's acceptable, right?
• 36:17
  SPEAKER_05: And I would I would argue probably still have some of these processes in place today, but anyway.
• 36:21
  SPEAKER_00: Oh, definitely, especially in those, you know, DOD and defense realm, even in manufacturing.
• 36:26
  SPEAKER_04: So you had a team, you had a team of six six graders, it looks like.
• 36:31
  SPEAKER_00: Yep.
• 36:31
  SPEAKER_00: Me, me and a couple friends just built this application.
• 36:34
  SPEAKER_00: Um, and it was great.
• 36:36
  SPEAKER_00: And uh, we actually won, you know,$5,000 grant from the army.
• 36:39
  SPEAKER_00: And I never got an allowance as a kid, so that was the most money I'd ever seen in my life.
• 36:43
  SPEAKER_04: So then what did you do?
• 36:44
  SPEAKER_04: What did you do next?
• 36:45
  SPEAKER_04: You said you got this something for the army.
• 36:48
  SPEAKER_00: I so I got this five thousand dollar grant from the army and uh you know, educational stipend, and I got pretty into math.
• 36:54
  SPEAKER_00: And you know, I started math Olympiad prep, and uh I I became a math Olympiad semifinalist when I was in middle school around this time.
• 37:02
  SPEAKER_00: Um, and uh, you know, naturally, you know, that lends itself to to military.
• 37:07
  SPEAKER_00: And when I was 16, I joined the Air Force Research Lab uh at Kirtland Air Force Base, where I worked on a team of PhDs doing deep reinforcement learning.
• 37:16
  SPEAKER_00: And I put out a paper as well, um, that's out there on uh, you know, how do you apply uh and deep reinforcement learning at the time was all the rage, you know, Alpha Go, right?
• 37:26
  SPEAKER_00: Uh, you know, you have you had autonomously working chess and beating uh Lisa Tone, who's the who's the Go grandmaster.
• 37:33
  SPEAKER_00: And uh, you know, of course, the military was interested in like how do we apply this to our autonomous drones, right?
• 37:39
  SPEAKER_00: How do we actually have uh you know cool tracking software?
• 37:42
  SPEAKER_00: And I got to work on that, which is awesome because I grew up watching Star Wars.
• 37:46
  SPEAKER_00: Um, and I'm a prequels guy, by the way, not a sequels guy.
• 37:49
  SPEAKER_00: Prequels get too much hate.
• 37:50
  SPEAKER_00: I love Revenge of the Sith, but uh, you know, I I'm working at this Air Force Base and I worked my way up to having a sequel of a clearance, and I got to work on what I would watch movies about.
• 38:00
  SPEAKER_00: And um, you know, that's how I really got into ML from software.
• 38:05
  SPEAKER_04: Neat.
• 38:06
  SPEAKER_04: That's great.
• 38:07
  SPEAKER_04: And then what did you do after the uh you did stuff with the Air Force?
• 38:10
  SPEAKER_04: And then what did you do after that?
• 38:12
  SPEAKER_00: Mm-hmm.
• 38:13
  SPEAKER_00: So, so decided to actually go to college um after spending a lot of my high school just around a bunch of grown-ups.
• 38:19
  SPEAKER_00: And I uh I went to Penn and uh, you know, I uh what I like to say is my dad's a businessman, my mom's a scientist, wanted to make both of them happy, so I decided to do the Wharton degree and the computer science degree, um, so that neither one of them got too upset at me, even though I think they both wish I was a doctor still to this day.
• 38:37
  SPEAKER_00: Um while I was at Penn, I entered at AWS and I got to work on Amazon Q actually, and uh that's where I got to work on LLMs, and you know, we were working on this before Chat GPT even came out, and uh was working on LLM infrastructure, was building some of the core infra in Rust for semantic search, which is really interesting because I'm a Python guy.
• 38:58
  SPEAKER_00: You know, I've done ML research at the Air Force and uh you know interpreted languages, and then you go to the low-level Rust and it's like a completely different ballgame.
• 39:06
  SPEAKER_00: But uh decided I didn't want to be a core software engineer after that, so went to Palantir.
• 39:10
  SPEAKER_00: And uh that's where I got into the manufacturing space.
• 39:12
  SPEAKER_00: Um, you know, at Palantir, I worked with a Fortune 200 manufacturer, um, you know, did one of the first generative AI implementations there, was going around the country to different plants, and uh that's where I really fell in love with the space, you know, uh, because there's so much in the manufacturing industry.
• 39:28
  SPEAKER_00: We talked about it.
• 39:29
  SPEAKER_00: It's core American GDP, it's the real world.
• 39:32
  SPEAKER_00: Um, and that's what kind of led me to start starting Endeavor.
• 39:36
  SPEAKER_04: What kind of manufacturer were you working with?
• 39:38
  SPEAKER_04: Um for AWS?
• 39:39
  SPEAKER_00: Yeah, I don't know.
• 39:40
  SPEAKER_00: Oh, for so I wasn't working with the manufacturer for AWS, but I was at Palantir.
• 39:44
  SPEAKER_00: But I don't know if I'm allowed to say.
• 39:46
  SPEAKER_04: Um I have to say, just the kind just the feel.
• 39:48
  SPEAKER_04: Like what did what did they ballpark?
• 39:50
  SPEAKER_00: Like what kinds was it diversified or it was um, I think it was is a process manufacturer.
• 39:57
  SPEAKER_03: Oh, okay.
• 39:57
  SPEAKER_03: Yeah, yeah, like a chemical plant or something like that.
• 40:00
  SPEAKER_03: Yeah, that type of thing.
• 40:01
  SPEAKER_03: Yeah, pharmaceutical.
• 40:02
  SPEAKER_03: Yeah, very interesting.
• 40:03
  SPEAKER_00: And it's smelled when you go to the plant, the smell just sinks it just sinks in.
• 40:08
  SPEAKER_03: Right, right, right, right.
• 40:10
  SPEAKER_04: Um, so what do you do for fun?
• 40:12
  SPEAKER_04: What else uh outside of work?
• 40:14
  SPEAKER_00: Good question.
• 40:14
  SPEAKER_00: Um, I recently picked up water sports, and uh, you know, it's funny because I was even though I grew up in Texas, I was born in Michigan, so uh you'd expect me to be a big skier, but I was I never really got into it.
• 40:25
  SPEAKER_00: But I picked up water skiing, and I was telling Chris this the other weekend, but you know, we were I was water skiing and I'd never done it before, so I kept falling over and the skis kept falling off.
• 40:34
  SPEAKER_00: But eventually, you know, I think I got it, and the boat's taking off, and I'm right behind it, holding on, and the guys hold on and like hold your arms straight.
• 40:42
  SPEAKER_00: Uh, don't let go, and the skis come off, and I'm like, okay, well, don't let go and hold my arm straight.
• 40:47
  SPEAKER_00: I'm just flailing behind the boat like this, and it's dragging me around the lake.
• 40:51
  SPEAKER_02: Oh no.
• 40:51
  SPEAKER_00: Um, yeah, no, I mean, incredible experience though, because eventually, eighth try, I got it, and it's it was unlike anything I've ever done before.
• 40:59
  SPEAKER_00: You know, I've done I've gone skydiving before, which is awesome, but you know, water skiing different, it's like in the moment, you know.
• 41:05
  SPEAKER_00: And uh, you know, I'll be doing a whole lot more of that, and maybe I'll get get into skiing as well.
• 41:10
  SPEAKER_05: That's great.
• 41:11
  SPEAKER_05: Yeah, we're gonna we're gonna try to you know get him on to the other water sport, which is snow skiing, you know.
• 41:16
  SPEAKER_04: Yeah, brother than water sport, yeah.
• 41:19
  SPEAKER_04: Well, this has been great.
• 41:20
  SPEAKER_04: I've really appreciated your time, and uh it's been a lot of fun uh chatting with you.
• 41:25
  SPEAKER_04: Uh thank you for participating and uh joining us on uh today's podcast.
• 41:31
  SPEAKER_00: Thanks for having me.
• 41:32
  SPEAKER_00: It's a pleasure and uh love talking about what I do.
• 41:35
  SPEAKER_05: You know what, yeah, your your energy is palpable, my friend, and it's it carries through.
• 41:39
  SPEAKER_05: So yeah, it's so fun to hear the excitement in your voice around.
• 41:43
  SPEAKER_04: Just thanks uh to folks uh tuning in to today's podcast.
• 41:48
  SPEAKER_04: Um uh people can find you on uh on LinkedIn uh and then uh endeavoraai.com is the name of is the uh uh URL of the company, E-N-D-E-A-V-O-R-A-I.com.
• 42:02
  SPEAKER_04: And uh thank you again.
• 42:04
  SPEAKER_04: Uh yeah, it's great to have you.
• 42:08
  SPEAKER_04: Thanks, I thanks for having me.
• 42:10
  SPEAKER_04: Thanks, everyone.
• 42:10
  SPEAKER_03: All right, cool.
• 42:12
  SPEAKER_03: Okay, fine.