Oil and Gas Startups Podcast

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1:12 What's going on, Wildcatters? Welcome back to another episode of the oil and gas. I'll start us a podcast here with Matt Loberdorfer from Embassy of Things. What's up, man? Hey, thanks, Jake

1:24 We've had a great conversation before we started recording, so we just need to recap a few things and then clue everybody in our conversation. You came in from out of town. Let's start off really

1:34 quickly. Just kind of walk us through what is Embassy of Things, and then I think you have a fascinating story. Let's go back into all the things that you've been through, the successes, the

1:43 failures, the challenges of an entrepreneur. We talked about a lot of that, and I'm really excited to get back into it of things or EOT is

1:53 a software company we do basically. we call it the industrial data fabric. So it's basically the Lego blocks that you need to, kind of have in place to build a digital twin, specifically

2:03 industrial digital twin, industrial data lakes, cloud historians. And the company really focused on starting in oil and gas. So some of our biggest customers are big oil and gas companies, such

2:16 as BP. We have a lot of the companies locally here in Houston as our customers, and now the company is actually reaching out into other industries, such as mining adjacent to it, as well as

2:31 manufacturing. So did you guys - did you guys have people from the team that were from oil and gas originally? Is that why it was like a target? Or was it like we've identified this problem, but

2:41 oil and gas is probably the one who suffers from it the most? Let's look at that. Yeah, so very good question So the origin story for this combination, they are especially like two core concepts.

2:55 The first one, which is the more fun one is, that I had two exits, successful exits with other companies that I've founded before, and I was thinking about doing the next thing. I was sitting

3:07 together with some friends, and we said, Well, let's start a company together, and the main objective is to do it with people we really like working with. So that's it. That's it. That's the

3:20 whole idea. We will figure out what we're going to do, but we just have to make sure that we just actually get people on board to accommodate it. We actually, when you have calls, stand-up calls,

3:29 and whatever, that you actually like the people. So that's kind of a fun environment.

3:37 The people who founded the companies are all serial entrepreneurs who had exits before and all success. So it was more like, Hey, we really actually like hanging out to it with each other. We all

3:47 have done a successful exit. So let's figure that out The second part is that, you know,

3:55 I went to the dark side for a while, becoming actually a VC versus being a zero entrepreneur. And one of the most overlooked problems that all my startups went into was to actually get data out of

4:11 industrial environments into their industrial analytics and AI platforms. Everybody wants to do AI. I mean, now with JDPD, it's like,

4:22 you cannot do one link in swipe or whatever was bombarded with AI. Everybody has that even in their title. So that's sexy, that's cool, and so on. And for me as an entrepreneur, I always try to

4:37 focus on something that everybody who is going with the trend is gonna need. It's kind of like a developer is gonna develop a new

4:48 bunch of houses, just basically, you don't wanna be the fancy house there. you want to be the plumbing, you want to be the stuff that everybody's going to need. You want to be the toilet seat,

5:01 right? Someone pick access to the next goal, right? Exactly, so basically that, right? So for us, and for me, what I figured out is that typically what happens when startups are, you know,

5:12 AI analytics, startups that go, it's between oil and gas and say, Hey, we have a product that you can do plan job optimization or, you know, drilling bit target algorithm or whatever Some sort

5:25 of stuff, right? That's all with AI.

5:29 As soon as they go to a big company and they say, okay, well, we need data for it. You need a lot of historical data to train your machine learning model. You need a lot of historical data to

5:37 even just do analytics stuff, right? And the big guy like that, gas companies say, nobody touches our data because it's secluded. It's air gapped from the internet for good reasons. It's society

5:51 and cybersecurity. here is we're gonna, if FTP you some data, and we're gonna give you some data, maybe on a thumb drive or whatever, right? So startup goes off with some drive or FTP data and

6:05 they're coming back and says, hey, we just figured out to give you 2 of production increase or whatever, good news, you know? Or our POC would be successful. And the sad news is that the POC

6:19 hell is placid with bodies of successful POCs You're all in hell. Yeah. Because even though you proved that your analytics, your AI thing, will actually help the company. When you go back and

6:34 says, okay, let's put that into production, you still have to get the data from the operational systems continuously, probably in real time,

6:46 microphone just magically moved, in real time into a production.

6:53 Oh, hello.

6:58 So you have to get the data moving, not just one time, not just a steady thing, but you have to continuously move it in real time into the cloud. And that is not easy, first of all, because it's

7:10 disconnected from the cloud in the first place. But also,

7:14 the industrial companies, they buy asset, they develop new world for the end of life, other ones It's not like an organism that grows and goes down when there's a new wave of oil and gas, they

7:26 just, oh, if the price of the oil goes here, then we actually stop getting, even producing from these wells and so on and so forth. So at that point, you don't have data, you don't need data.

7:36 So you have to have a solution that actually brings the data. And so we built, and it was the second part to actually find in this company, basically the plumbing between what's in the oil field

7:49 And what's in the cloud, we basically built a data pipe. that you can switch on, switch off. You can actually grab data with certain intervals. You can make and transform and enrich it so that it

8:00 actually is palatable and usable by AI and machine learning algorithms because these algorithms typically don't actually want raw data. Raw data is messy and has data losses and all that stuff. You

8:15 have to kind of prepare and clean it up. And in a clean state, it's probably also enriched with stuff So you build basically that system that can't do that. So it's not the first thing that you

8:27 think about, hey, you don't want to support AI and analytics. But it's a very important piece. And the truth is that everybody will love you. The AI and analytics company will love you because

8:37 they can then share, we can put it in production. Obviously, the cloud vendors will love you because you put a lot of data in a cloud that was not there before, it's billions of records per

8:48 company per day that has to be consumed, processed, and so on and so forth. and the customers themselves will love you too, because now they can actually do something in production they were not

8:57 able to do before. So that was the second part. It was kind of this missing, overlooked little problem that it's not as sexy as machine learning and AI that had to be solved. That's kind of. How

9:10 do you overcome? So I was on a call with the guy who is at a data management company, it's a software platform. And so they come in with EMPs or whatever and help them really kind of wrangle all of

9:22 the information. Some more concepts, not similar in what you actually do, but similar in kind of a high level, right? And he's like, man, I'm the biggest barrier that we have is when we're

9:30 coming in and talking to some of these companies, there's like a lot of these willing to ask companies just like to build a lot of these IT teams and they blew and they like to build everything

9:40 themselves. And he was like, you know, I can get buy in from the production department and from operations and even finance. And it's like IT's there and they're supposed to be the enabler, but

9:48 they're the ones who are actually like killing those floors because they're like, well, we want to do all this work. We want to build it ourselves. So I'm kind of curious what your experience is

9:55 building there. Absolutely. How do you overcome that? This is a great point. So typically, our ideal customer is exactly a company to try that and field. Like literally. There's a lot of hope.

10:07 It's very good. So here's what happens in this. This is not particular the focus also on oil and gas, but also on manufacturing or mining or any of the other industries

10:21 In order to build a system that actually gets data in from OT to IT, if they try to build it themselves, what's going to happen is, first, let's say you have some IT data engineers that start

10:35 building something in Python or whatever, well, after a year or so, two things are going to happen. First of all, that IT guy is probably going to move on. The code is not, there's nobody who's

10:48 going to further develop the code literally technical death. it becomes technical debt, right? And then what also happens is suddenly the requirements change and say, you know, they say, well,

10:58 instead of every hour, we need this thing to run every minute or every second or, you know, oh, we changed the naming convention for the tags over here. Oh, some stuff happens. And the whole

11:08 thing is suddenly, it's still working, but they are like, we need to upgrade this, right? And since, you know, the people with the knowledge left, or they move somewhere else, they're like,

11:19 I don't want to touch the same, I'll please take it off my hands But they're looking around for another solution. So if we come in and say we have a no-code platform that can do exactly what you

11:27 need to do, and we can also scale it up to much more stuff, and we can do all these additional features, and not only you are customers, but also all the other oil and gas customers are doing the

11:36 same thing with our software, which means you have a super safe, secure, and simple way to make sure that all the new stuff that comes up in the future, in terms of technology advancements, are

11:47 also plugged into it, because you just basically receive, you know, a new version, an update that has new features in it, every month if you want, but depending on how fast you want to upgrade.

11:59 So it kind of moves from a tech debt, we implemented ourselves to, there's actually an application, a system that is a kind of industry-wide used application that they can plug in. And then it

12:13 becomes mostly a pricing discussion, because they can say, oh, we can also, maybe in IT, they have like, we have three data engineers that could do this for, I don't know, in three months.

12:22 Like, okay, and then what? I mean, it becomes more like,

12:28 what's

12:29 better like long-term? What's the OPEX-like? What's the

12:34 CapEx? They need to actually invest upfront. Yeah, we talked about that for years of just, well, companies are good at pumping oil and gas, and I should focus on that, and should not build as

12:43 many things internally as possible. And there's use cases where, I was actually on a call with a bunch of guys, Everybody's nerding out on the solutions that they kind of built internally. But

12:52 yeah, there are certain use cases where you can very quickly spend something up, but I think it makes sense for them. But for something as complacent as what you're talking about, it's like it's a

12:58 no-brainer for me to go with somebody like you guys as opposed to hiring this massive team to support this project that may or may not work. And a lot of times it doesn't work. Yeah.

13:10 And also it doesn't scale, meaning if you implement something to requirements, and then if it's successful and it works for a while, then there will be other departments and business units coming

13:21 out and say, hey, we want this data from over here, we want this thing from there, oh, we want to build an industrial data lake, and we want to build it all and put it all in here. And then

13:30 suddenly that Python code, either you turn the Python code in your own application, you build an internal application, or you actually build a value, actually buy something. So that's what I need.

13:43 That's for us ideal. Because if you are talking to someone who's in the middle of building something, We are basically saying, okay, let's just wait for six months or nine months. I mean, we are

13:52 not in a hurry, right? The oil and gas business is moving at a glacier speed. It's now going, you know, I would say the last three years, the glacier has become faster. Yeah, but it's still,

14:06 you know, it takes a lot for them to give us a lot. So you guys primarily like the foundation of just Tate, you've got all this massive amounts of data, let's wrangle all of it

14:09 so

14:15 that you can go and do things with it. You guys have any things out of the box that you're doing in terms of whether it be visualizations, digital twins. Yes, so we follow through that. So, you

14:25 know, we followed kind of from a product perspective really kind of concepts that were true already, I think 10, 15, I mean, 20 years ago where we basically thought about what's going to be our

14:42 end game, our product offering. We've started with one thing that is the data wrangling part. But if we can be the ones that actually are the industry standard to actually get data in from OT into

14:54 IT, and we have all the bells and whistles to be kind of the air traffic control system, and there's no coding involved, and we can just roll it out to administrators. Great. What's next? The

15:04 next thing that we did last year is we added another solution to it, which is basically taking the asset hierarchy data, all the metadata that's attached to it, if you take an artificial pump or

15:20 maybe tanks and plungers and whatever, some all sorts of stuff, how is that

15:29 asset data represented on the OT side, because you have skater systems, you have skater tags, you have historians and so on, and you have maybe an asset management system. That is not in IT

15:44 either. But then in IT, you also have an SAP system and that has its own hierarchy of how the asset is structured. So we built basically an asset management system that continuously moves in that

15:56 asset hierarchy information from OT and you can kind of merge it up with what you have in IT and you can build new graphs. And I'm talking about really hierarchical, cyclical type of graphs that you

16:09 can manage. And these are visual, like you can, it's kind of like a photo structure that you can kind of click through your assets and say, I want to see this pump in route 15 in areas, premier

16:20 and whatever, right? So you can click on that and then get the data from that. And you can see in the attributes metadata that comes from a historian or an asset management system in OT and right

16:33 underneath is an SAP system or an EEM system that's there too. So we added that because

16:42 it also seems to be a major pain for a lot of those companies, that they have these two differences. There's no single source of truth, right? And then the second part that we added was a product

16:55 that actually does visualization. And tons of companies and also open-source systems are out there like Raffana

17:04 and there's obviously also commercial other applications out there. But what we did do is we set ourselves actually a goal to be

17:15 a visualization system that's so simple that it can be used to create new dashboards and apps for the OT guys. They can do it. They can just upload a GIF or a BMP picture and say, that's my pump

17:30 and boom, boom, boom, boom, boom. And at the same time, it has the functionality of Raffana so that it can put in data sources like from Databricks and Snowflake and all these commercial systems.

17:41 And you can build a coherent dashboard that basically allows you to

17:51 interactively push or interactively allow the visualization graphs to be populated from different type of source data systems based on where your context is, if that makes sense. It's basically kind

18:04 of a context-driven visualization layer that in the back end really goes down to OT systems and IT systems and allows to show it together. And it's super simple. So it's got a Power BI, Grafana,

18:18 and some

18:21 skater visualization thing in one. So we added that. And that kind of concluded our whole product, because now we have really building blocks that people - and they are completely independent

18:34 software products. So if you already have visualization, you don't need to buy obviously. Or as I mentioned, so you can just use them as Lego blocks to kind of build your industrial, you know,

18:44 digital twin. Are you guys seeing more traction with certain parts of the market, like either upstream, whether it be with operators, it'll be the woeful service side. Upstream, upstream.

18:53 Upstream is the most active, I would say. Are there certain kinds of use cases that are - Yeah, I mean, mostly, I think what happens there is mostly production optimization, production

19:06 monitoring kind of applications. I mean, we do, I think we support over 450 different use cases from the daily morning report, how was production yesterday? Which is very simple, but everybody

19:20 wants it. I mean, I guess company, because it says, that's how we did yesterday. And then, to production forecasting, right? To tank monitoring, which is kind of, in very kind of low

19:33 frequency, to real-time drilling, which is sub-second data, where you have, you know, literally billions of data points coming in per day, right? So, yes. It's just done so different in this

19:44 case, but I would say the majority is really about optimizing production. Yeah, that makes a lot of sense. This podcast is brought to you by EnergyX. Are you tired of paying huge rates through

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20:50 is going to be digitalwalkcutterscomenergyx. Let's talk a little bit more about your background. You and I were talking about Felicia recording. So you were, you were living out in San Francisco.

20:56 Yeah. Yeah. So walk me, walk me through some of these, tell me some stories about these first startups. So, you know, my background is, you know, I always wanted to be an entrepreneur. And I

21:08 have been since, you know, I was 17 out of my parents' house. And start my first company, my mom was like, where are these checks coming from? What are you doing? I was selling software, right?

21:20 So,

21:22 but in Silicon Valley, when I was in, in, in school, in college and study computer science, I was said to my colleagues, say, I'm going to one day, I'm going to go to Silicon Valley and do my

21:32 own startup and, you know, do the whole like spiel. And I did, ultimately, you know, I had my, my, um, company on Willow Road, which is basically really the road that goes out to Meta,

21:45 where I think 20 years ago with Sun Microsystems, I think it was the original starting office for Cisco, small little office that we were in there. And I raised funds from billion dollar funds.

21:59 And

22:02 ultimately, I think this is my seventh startup. Some of them failed, some of them were successful And so, one of the things for me is as an entrepreneur, I just learned a lot and some of it was

22:17 the hard way. Yeah, I think that's how you have to learn it, right? Oh, you know, if I could do it differently, I would give myself some advice, like for instance, one advice I would say,

22:27 in your first series, a price drowned, okay? Always go and try to get as much possible money, legitimately, right, you know, that you can. Meaning if it says, okay, it's my serious A, I

22:43 don't want to give away so much of my company. I don't need 10 million. I only go for 25 million. I would suggest you go for 10 million. And because of the following reason,

22:55 in your first round, you are in control of your entire company. It belongs to you. You have no other person there. Secondly, that's number one. Secondly, probably all or most of your

23:08 forecasting and your pitch is projections of future, which is mostly a dream. It's a promise. It's a promise to what you're gonna do. So there's no real valuation. I knew some companies, they

23:22 raised 30 million at their CSA. So it depends on what you're promising like. And so you are in that driver's seat. And thirdly, very important, any normal investor, I mean, normal being, you

23:37 know, average typical investor.

23:40 will want to have 20 to 25 of your company for the first CSA. And it doesn't matter if they give you 10 million dollars or 2 million dollars or 1 million dollars, they still want 20 to 25. So if

23:53 you get 10 million for 20

23:57 to 25, you got to lose the same 20 to 25 for just getting 1 million, right? So I really suggest that in

24:05 your CSA, if you can wait

24:08 and actually self-funded bootstrap it until you get to a point where you get a higher valuation or you get a term sheet that allows you to raise more money in your CSA, do that, right? Don't try to

24:21 close a CSA fast and say we go for less money because we want to not give away so much, you know, of the company so much equity. So that's one. Do you learn much about who to raise money from and

24:33 certain? So the other thing, and you know, we talked about this earlier, is

24:39 Ultimately, as an entrepreneur, you have to think about an exit in where you, you as an entrepreneur will want to

24:47 exit. What I mean with this is, let's say, you know, you would be happy with a 50 million exit. So that means you're going to sell your company with 50 million for 50 million. And that's fine.

24:59 You don't want to be a billionaire. You don't want to buy your own super yacht, whatever, right? You just want to know sports teams, you know, no basket or anything, right?

25:09 And you think, okay, if we sell it for 50, you know, maybe it was a tag after Texas, I would sell whatever, 20, I end up with 10 million or whatever. And you're like, I could live with that,

25:22 that would be for me, probably, right? So you kind of make your plan. What do you think is something that you want? And of course, then you think, okay, now, how does that help me with my

25:33 investors? So my, and this is just my personal experience, suggestions, only raise funds. from investors that actually where the fund size is matching or smaller than your exit. So if you want

25:47 to sell the company and you're happy with the company of 50 million, go to a fund that has 50 million fund size or less. They can also have 20 million. If you

25:56 think that you're happy selling 150 for company the million, go for a fund size that has 150, 150 million or less. Now, why? Why is it important? It's because when the time comes for this exit

26:12 and you raise money from a 20 million dollar fund and you sell the company for 20 million dollars, that fund will be really happy. If you raise money for a billion dollar fund, they will not want

26:26 you to sell it because 20 million is a job change. It's like not even showing up on the third zero after the comma or after the dots. So for them, their job and this is their job as we see is to

26:41 actually return a billion dollar if it's a billion dollar fund. Their job is to make billion dollar unicorns. So, if you want to sell it for 20 million, they are not dead. If they are on the

26:53 board, which they will be because they are VC, they will not want you to sell it for their price. And there might be some exceptions, but that typically is the case. So, as an entrepreneur, if

27:04 you're raising money, I would suggest thinking about that first, go to those investors that actually match your expectations in terms of exit and make sure that when the time comes and there is an

27:16 offer that they will be happy and you will be happy and the world is a better place. The second part of this is that if you look at, and you can Google that and ask JetGP also,

27:30 what's the distribution of companies and how much did they sell for? So, you have basically a e distribution where you have a lot of companies that are sold for smaller amounts for 10 million for 20

27:43 million, right? There are many, many, many more companies. There are only very few that are sold for 2 billion, right? So as you pick your number, also be aware that the probability of you

27:54 being successful in exiting goes dramatically down, right? You can pick a billion dollar exit because you have a great idea and it's going to be a billion dollar, you know, play, or you do a pick

28:06 and shuffle play, which is basically at some point acquired to be like tucked into some other piece in that 50 billion dollar, right? These are the higher chances because somebody's going to

28:18 probably have to pick you up because otherwise the competitors will pick you up and stuff like that. So I think that's, you know, some very important decisions do you have to make as an

28:28 entrepreneur. So you said you've had two successful exits. What kinds of companies were those and then And what did you learn kind of throughout that process?

28:38 So these were those two, yes. So

28:45 I think the experiences here is, first of all, an exit doesn't happen

28:53 by itself. You're not gonna just sit there and somebody knocks on your door and say, You wanna buy your company. Hey, what are they gonna give you, what? Even though I have to say it, I

29:03 literally get emails every day that says, We wanna buy your company And I'm like, typically putting in a trash can. Literally, I get those emails. But no real deal is to be working. What happens

29:18 is that there is some event meaning

29:25 a company is, you're getting so competitive to another company that they are like, You know what, we better buy those guys because the money cost us to compete with them is higher than. the price

29:38 would actually have to buy that mic. Or,

29:43 you know, there's a, you know, two companies doing the same thing, they actually wanna collaborate together and because they augment each other. They're not competing, but they augmenting. And,

29:54 or you could be, their companies are there, that actually just buy other companies to kind of put them together, almost like a PE, play where you actually just buy different pieces and they all

30:03 together become a solution So, there's always a dynamic and there's always a event. And so, the tricky part is, as an entrepreneur, and for me this happened now a couple of times, is that if you

30:13 are able to kind of

30:18 create this event,

30:21 so you don't wait for it to happen. It can happen kind of naturally because you're so good and you're competing with another company and eventually they're like, okay, we gotta figure this out Or

30:32 what you also could do is you could say, okay, well, I'm raising another round, okay. And let's see, you get a term sheet and say, okay, well, with this new round of money, we are going to

30:46 this additional market or we are going to build this additional product or we're going to penetrate this particular customer or whatever. And if any of those steps are basically stepping on the toes

31:01 or another company, it's bigger than you. And they hear about it, they have a choice to make You're forcing a decision. You basically say, OK, well, this company here is going to make that move,

31:13 that chess move. So if we let them make it, and they have which they might not know, but if they get some idea, if they have that amount of money to compete with us in that space that they're

31:28 going to go into, and they already have a track variety of being successful in this space, then it becomes pretty much an MA decision. do we want to actually buy them? Or do we let them

31:42 basically, do we just head on and compete with them and so on? So if you built that momentum and you kind of create this event, then in a way you kind of built a scenario where an exit kind of

31:58 happens. It's not a bulletproof recipe, but just from experience, I've seen that work well. Yeah. So do you feel like

32:13 you come up with these strategies kind of early on or do you just kind of see opportunities and you're like, well, we know an exit's on the horizon? That's a very good question. So I think that

32:22 you cannot foresee the future. So early on strategies might be you can

32:27 kind of

32:30 play, you can play head games and think about, what if that happens and that happens, that happens. But typically, What you cannot foresee is dynamics in other companies and how they react to the

32:40 market. So there might be a company that has a, let's say a digital twin strategy and suddenly that becomes the big thing for this company and they now have to change their own strategy internally.

32:53 And that means suddenly there's these kind of gaps in their offering

33:00 or in their services or whatever. And then they are starting to look around, right? So typically at the very beginning of a company, when you are really starting, you don't know that yet. You

33:12 really, the exit really becomes interesting after you became successful and somewhat, you know, recognized in an industry. Then if you do then a step, then at that point,

33:26 that step will be or should be tied to what you actually at that point see in the market But in some way. back to your question, it's contextual and it's time sensitive,

33:40 but it will still take some time just because you say, okay, I'm going to do this right now. It does not have an overnight. So whenever you actually get to that point and you're like seeing kind

33:49 of the matrix,

33:52 then

33:55 an accident or an acquisition still will take months, because there's this whole process involved of how two companies actually get to that point where when it says, okay, we're going to like join

34:10 forces. Yeah, I think there's another scenario that I've seen just kind of followed closely. It's like really relevant to us. You see acquisitions like pin gaming buying varsity sports, right?

34:22 For distribution, you see, you know, HubSpot come in and buy

34:29 the hustle, right into the hustle had a massive. News leaders he business insider buying morning brew right so you see these tech companies that have products right where they're looking to Get more

34:43 distribution so you can have a more of a negative customer acquisition cost through people who've already built out but Massive networks right and so it's just kind of like a bolt-on acquisition to

34:53 help but because now when you're looking at expanding Yeah, it's like do I want to go out and just keep spending all this money on? You know Facebook and Instagram and and LinkedIn ads Or do I want

35:03 to go to where the people that I'm trying to reach? Anyways are over here on this newsletter or this podcast or this you know video show or whatever it may be and just go Buy the audience bolt it on

35:13 and then it's a it's a build-up. I do you want to build this all yourself? Well, you just buy some money who did it already. Yeah, what a meeting. Yeah Is there anything you feel like you've

35:23 you've kind of learned about yourself as an entrepreneur? Maybe things that you and we talked about a little bit, you know, I kind of some misconceptions about what I thought I've wanted to do And

35:32 then the longer I spend my time in the trenches, I think I've really,

35:38 yeah, I just understood, I guess I have more self-awareness now to where I know kind of like what energizes me and like where I want to spend my hours in my day and like what I want to do. Yeah.

35:48 If there's anything you've like learned about yourself over these years of, yeah, being in the trenches with all these startups. Yeah, I mean, for my side, I'm really, I don't just with

35:60 building companies up to a certain size. What I mean with this is as a small company, and we talked about it a little bit earlier, it's a small company you, have one strength, which is speed.

36:14 You are basically in a speed board and you gotta have an A team on board because you outmaneuver everybody else, right? So that's your strength. All the bigger companies have more money, more

36:24 people, more resources that they can throw at stuff, but they're gonna be slow. They're like oil tankers or bigger ships.

36:31 So the second part that you need to do is be like a laser-focused sniper. If you do a marketing campaign, you cannot just throw like, oh, again, we're gonna do a big, you know, marketing launch

36:47 and blah, blah, blah. You know, we're gonna go to this show and have a big booth and to this show go to big booth. Even if you have a lot of money, but there's one thing I also learned, you

36:57 know, when I raised, you know, a lot of money from really big funds, if you then go and say, oh God, we're gonna have a big booth, you know, with our own setup, design, whatever, right?

37:12 Not effective, I would, as a startup, even if you have money raised, I would not do it. I would really literally look is where is your ideal customer? It might be a small little seminar room,

37:24 you know, in Houston where they discuss the, with petroleum engineer, petroleum engineers, something versus. having a booth at the Offshore Technology Conference that happened just a couple of

37:35 weeks ago, right? Where if you buy a booth there and there and you spend 100, 000 on that freaking booth, you might get way less customers than if you go to one of the smaller things. So you have

37:46 to be extremely focused and think about how you get

37:52 there in terms of, so back to this for me, that is something that inspires me and where I'm feeling passionate about. Because when I have a team and work with customers and also partners where we

38:09 can build that up,

38:14 you're really a close knit group of friends or people that are in the same adventure. Once it reaches a certain size where the company is, let's say, beyond 50 people, 70 people, it becomes more

38:29 like a process where you. this department and that department and this department and outsourced agencies and outsourced people here and here and here. And you kind of lose the speed and you kind of

38:38 transfer into or convert into kind of like a big company, but on a small scale. And that then starts growing and growing and growing. That's for me at that point where I'm like, okay, you know,

38:54 this is

38:56 a, it's a different life, day of a life of an entrepreneur, because suddenly you're not really, you know, sitting at a speed board, you're on a bigger boat and you have to be more strategic and

39:07 you have to, you know, kind of,

39:11 you know, organize all your people that they do the things correctly, which you also have to do in a small team But at some point, the

39:21 agility goes away. And so, for me, that's when it becomes kind of less interesting, that's all.

39:31 I think we've been very much in this like zero to one phase for a while for for wildcatters And it's cool. Now we're kind of starting to get our legs beneath us and kind of started to like go into

39:40 like growth And it's it's cool to see how things have like transformed over time You know for so long. It was just like a handful of us You know kind of shooting from the hip doing the cowboy stuff.

39:50 Yeah, it's like yeah, we got like systems and processes We got more people. Yeah, and then we're seeing this is the coolest thing I think because ultimately this that's a foundation that all the

39:59 rest will run on right? That's when you set the culture of the company. Yeah, you know, that that's when you bring key people on board. It's so so so critical for Hiring the right people in the

40:11 early phases. I mean if you have 50 people you hire one person more You know, it's it's a 50 50 first percentile right or one out of 50 percentile or 51 percentile. So It becomes you know in terms

40:26 of what can go wrong stiffened and if I held it

40:30 10th employee. That is if it's a, if it's the wrong choice, then a 10th of your company is invested in the wrong thing. And so it's,

40:42 yeah, it's a big deal. Well, Matt, I know you're going to run into this next meeting, but I really enjoy the conversation both here and then prior to us getting on the mic and enjoyed having in

40:51 town. And thanks for making it. And I would love to catch up with you later on the road and see how things are going for you guys Workpeople checking you guys out, what's the website?

41:02 So, simple website for the company is embassyofthingscom. You can also go to industrialdatafabriccom if you're interested in the solution. Yeah, that would be it. Awesome man. I appreciate it.

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