Why Are Successful Enterprises Embracing AI Disruption?

Narrator: Welcome to the CEO Project podcast, where Jim Schleckser, author of the best-selling book Great CEOs are lazy and the founder of the CEO Project, provides ideas and tools to help CEOs of mid- to large-size companies grow and optimize their businesses. Now, let's get started with the show.

Jim Schleckser: Welcome everybody to the CEO Project podcast. My name is Jim Schleckser. I am your host. Well, you know, we've got a bit of a theme going here, which is AI.

Um, we've done a couple episodes on it recently and you know, maybe you can't spend enough time on AI given the transformative nature of what it uh is going to do to your business. And you know, really in 5 years there's going to be two kinds of companies. Those that have embraced it and those that are out of business, unfortunately. Although there may be a few kinds of businesses that don't need to embrace it, but generally this is the case.

One of the questions that we've run into is the question of how do I have some visibility or understanding or control over the utilization of AI within my company? Do I just let this go like wildfire? Roll your own. Let's see what happens.

That sounds like a bad formula. Well, our guest today is going to help us think through that and he has some solutions in that area. Pete Foley is the co-founder and CEO of ModelOp with over 25 years of executive and entrepreneurial experience in enterprise software and a track record of successful business exits. Pete Foley's leadership gives ModelOp customers, partners, and employees a high level of trust and confidence in the company and its future.

Uh prior to founding ModelOp, he held a bunch of different CEO roles. I won't go through all of them, but believe me, highly experienced individual. Um, in addition, he was the executive chairman of Graphite Systems, a low latency flash-based big data appliance that was acquired by EMC. Uh, and he's a graduate of a very fine Ivy League institution in New Haven known as Yale.

Welcome, Pete.

Pete Foley: Yeah. Thank Thank you, doctor.

Jim Schleckser: Fine introduction.

Pete Foley: It means I've been doing this a long time.

Jim Schleckser: Well, when I tell people I've got a lot of experience, I'm like, that means I'm old, right? Um, so, okay. So you went to Yale. Were you in one of the what?

Eating clubs, dining clubs. Skull and uh Yeah.

Pete Foley: Well, we had we have two two things. We had a what we call residential colleges, which was effectively your dining club. And so absolutely. And uh and then we had what I'm thinking you're probably referring to the secret societies.

But as you know, I tell you, but then I have to kill you.

Jim Schleckser: Yeah. There you go. Although Bush told everybody from what I can tell you.

Pete Foley: I mean, he was running for president, so Yeah.

Jim Schleckser: You can't really kill him. So what are you going to do?

Pete Foley: Well, no. He's got he's got secret service details.

Jim Schleckser: So, exactly. Um well, this really is a big problem. I mean, let me go back one step, you know. Um how did you determine that this was a problem that needed to be solved?

The question of call it AI governance is really what we're talking about, I think.

Pete Foley: Yeah. So, so that's a great question. Um we started a company really focused around helping large companies. So again just to separate we are an enterprise software company meaning we sell to businesses p primarily large scale enterprises.

So what we found in early on we were doing some consulting work with a lot of large enterprises around trying to help them develop AI or at that time just analytic models right how to make their business run better right and every CEO at that time was saying look we need to be the predictive business right we want to know what's going to happen you know not just next quarter but certainly five years out so in doing so it was kind of a realization that one of the big bottlenecks of these AI or model assets was their ability to get in production. M so organizations spend you know obviously you read we all read the paper right not just billions now but trillions literally on AI assets whether it's developing their own purchasing them through organizations like Amazon right or open AI or bringing them in through other software vendors products like Salesforce Service Now others or that are utilizing AI assets underneath you know their holistic solution sets. So um we recognized that look we were hitting this literally brick wall of we think these you know whether it was a financial services company that was using our models for uh municipal bond trading saying look alpha you got to get these into production they're saying look we've got this hand process where we've got model validation PhDs we've got uh you know actuaries and we've got quants that need to review these assets before we can actually put them into production and then there's a technical process of matching those models to data, understanding the security framework that needs to be surrounding these models. Um, we found that it was typically taking anywhere from 9 to 12 months for a model and you know highly to your use your term earlier transformative piece of software.

Yeah. To be able to get into production. Um, and that's just not sustainable especially at the pace now that I was going to say too too slow, right?

Jim Schleckser: Too slow. So the question I think is what got us in into this business and then why now?

Pete Foley: And you're you just said it too slow. So in today's environment where not only is it the CEO who is you know out there with a strategic plan saying look we're going to you know reduce cost we're going to increase revenue and we're going to increase competitive positioning right our the mode around our business utilizing these assets and it's it's it's been that CEO CTO chief transformational offer chief digital offer there's a whole bunch of new titles you and I didn't grow up with right it's so Um so you look at that it's like you know the kid in grade school now says I want to be an astronaut and a chief trans transformational officer.

Jim Schleckser: Yeah. Yeah.

Pete Foley: So, we found that, you know, not only was the CEO looking at that or that executive team, but now you're getting at the board level, right? In these large public uh organizations and you've got the you know, government obviously as well, but we really had that the this board level push of look, I'm on other boards or, you know, I'm involved with my own primary business and we're utilizing assets at this pace, right? We're we're using these AI assets and models at this pace. So there's a real push in competitiveness to your point there you know in our world our customers are only going to be two types right there are going to be the people that are utilizing AI assets to make their business again still you we let's stay on that theme of transformative or not and it really is a market definition and in our eyes a clear definition of what happened when the internet first came out right web-based pro web-based assets versus, you know, um, Sears.

Jim Schleckser: Yep. So, so we really we really do see that.

Pete Foley: And by the way, another So, that example is I think very close to what will happen as organizations start to realize the benefits of AI assets. So, this board level, this the executive team level is saying, look, we recognize this. You know, you can't open up the Wall Street Journal, right? is dedicated and I'm on I'm involved in National Association of Corporate Directors and trust me there's a huge amount of AI education going on for board members like here are the questions you ask these are the answers you should be getting you know yeah absolutely forming committees the budgets are being allocated for these assets but eventually somebody says um look we need to take advantage of AI y then somebody raises their hand and says and how are we going to put the guardrails on it right because there is this still whole you know Um there's still a little bit of that uh uh you know uh transformer type you know what can happen if the machine turns on us right.

Jim Schleckser: Yep. Yep.

Pete Foley: So we so that really has caused this kind of um a very cautious move for these large enterprises who really have built you know hundreds of years in some cases built a brand. So it really has been an opportunity for organizations like us for platforms like us to not just look at the governance side of this but there's two aspects of really the value proposition that a company like model provides and the primary is the idea of being able to manage those assets. So our software solution we offer a software solution it's a full platform basically from the model development side. So once the organization purchases, develops or as I said before brings in other other AI assets from their software vendors from that point in time to how do I get a report that says we're we're we're generating 20% more profit or revenue or cost right that full life cycle.

Jim Schleckser: So I mean this is almost like a product development product launch management system. Absolutely. But specifically for AI for specifically for AI.

Pete Foley: That's a that's a great point and it is specific for AI because of kind of two two aspects of AI. Number one, AI is a unique technical asset. When organizations looked at, you know, the kind of the first life cycle management applications were all around applications, right? How do I how do I manage you know hundreds or thousands of applications within these enterprises, right?

So some great products that do that. The difference is AI is a technical asset very much like an application but it is highly technical and it has enormous risk associated with it because if you are forecasting a trade or a price for a municipal bond and you're wrong or if you are forecasting you know the next uh tariff impact right from an a competitor in China or a competitor in Europe it can be detrimental to a business.

Jim Schleckser: I mean literally. Yeah. No, big because you got to think about the business cases I'm going to deploy AI into first. They're going to be my high impact business cases.

Absolutely. Which means if I screw up, it's also high impact, right? It's Yeah.

Pete Foley: I like you, the interviewer can say that.

Jim Schleckser: Well, I just did. So, there you go.

Pete Foley: If I screw up and so there's been this hesitation of look, how do I not only take advantage of AI? How do I how do I create some competitive differentiation between myself and the competitors? But then most importantly is because we all can look at that model that you know um created a pricing advantage is I you know in the in the uh uh you know pricing advantage in a stock trade and just use a simple alpha right which is probably you go another one airline airline seat pricing.

Jim Schleckser: Yeah, absolutely. Beautiful example, right? Load management in the hotel, load management airplane. I mean, exactly.

Pete Foley: But then but then how do you do that across, you know, an entire organization? How do you scale that? And that is really our primary value proposition is our ability to help organizations scale those investments and then and technically how we do it and you know how we do it in integrating with you know all of their environment and their ecosystem is all underneath the covers but in the end we're finding that our customers are not your chief transformational officer or your your CTO as much as they are as strategic leadership within the organization. Yeah, the guys and the men and women who were pounding the table and saying h how do we get AI at scale, right?

You know, if you remember when when the internet started and everybody said, "Look, we know we have to be digital native or Amazon's going to drive a truck through our warehouse, right?" Yep. And I always cite just as a Chicago guy, I always cite W.W. Grainger as the flag the poster child for how to adopt, adapt and actually effectively compete in an aggressive manner, right, with some of these digital natives. So I do think you're seeing it, you're going to see it in banking, right?

You see it in retail, CPG, we see it in transportation to your point. Um because you know what AI is going to change the vertical natures of the organizations. organizations are become much more horizontal.

Jim Schleckser: Yeah. So, well, and you know, this question of go faster because we've got our you know, I'm a big fast cycle time guy, right? And the trick is, you know, I don't need to outrun the bear. I just need to outrun you.

Um, but that's right. I mean, as long as my cycle's inside your cycle, you're toast and I win eventually, right? I just got to give it enough time. Yes.

Pete Foley: countered by your chief risk officer or your board who have a risk responsibility to say love that how do you not blow up the company at the same time.

Jim Schleckser: Absolutely.

Pete Foley: So that's so what how do you do that right and that and that goes now now we're now now we are moving into some of the technical okay aspects of our of our of our product and some of the value that transcends from being able to own and manage that full life cycle going back to what you'd mentioned earlier in the introduction the ability just to see what assets we have in production or see what assets that are on the shelf that we want to have into production right because every department every geography within these large organization is saying I've got an AI model. It's going to change my business. Yeah. And you know, and again, listen, you know, you know about employee scorecards.

I want my scorecard to be impacted by this model, right? I'm going to reduce chat. I'm going to reduce, you know, customer service time. I'm going to increase profitability on our pricing.

You to your point, dynamic pricing. Everybody's got that opportunity and that killer application in the model within those departments, right? How do we know just you know from a from a visibility from a comprehensive inventory what's in there? I mean how many models do we have and you'd be amazed doctor I mean there organizations have thousands of models in development all the time.

Jim Schleckser: Yeah. Oh yeah. Well look you got you've got anybody under 30 years old in your company model. They've got bunches of them.

Bunches of them. Every kid coming out of school knows how to program in Python and open source and has bunches of models, whether they're doing real estate analysis. I've got nephews and nieces. Every one of them has is building their own models now.

Sure. And it's amazing.

Pete Foley: So I think that the first thing is about visibility to your you know, you'd mentioned earlier is from an executive side, from a risk, from a compliance understanding, you know, what do we have in inventory, right? Forget about production yet. Let's just understand and let's get some risk assigned to that. Yep.

Y I mean is it is it a is it a model that's that's going to cost our uh Glassdoor rating to go down you know 0.4%. That's okay. You know maybe we do take a risk on that. Or is it a model that potentially is going to uh price our Christmas travel right overseas.

We probably should assign more risk to that right.

Jim Schleckser: Yeah. Yeah.

Pete Foley: Second piece of that then is all about what is the organization what are the policies within the company around risk and compliance? what are these models allowed to do? What are they supposed to do? So, it's not only kind of making sure that you're staying within an organizational a government and or a community-based compliance, right?

Like a FINRA, right? The SEC going going higher level or what's going on in EU, right, with the whole idea of look, we've got different levels of compliance requirements and just just think of them as policy requirements. Yep. The second piece of that though that people don't necessarily think of is the efficacy side of that.

Jim Schleckser: I was going to go there.

Pete Foley: or how do I know it's actually doing what I think it's how and if it's not if you don't understand that model don't have those models in a comprehensive inventory with risk assigned use case assigned along with that risk how do I know how do I know if you know to you know not only is you know take it to the far extreme of retiring that model right or do I invest in that model so efficacy and then staying within your regulatory requirements and then and then this there's this whole concept of being able to monitor those and that goes back to the full life cycle management. Monitor those on a real time basis because you know literally if you if you think about the transformer machines it only takes seconds for them to take over. So it's you don't need a lot of time it is generative right? Yep.

The idea is look we need to con have a constant and consistent monitoring opportunity you know monitoring solution in place that provides real time reporting.

Jim Schleckser: Sure. Because it's a it's a moving target.

Pete Foley: It's such a moving target. Right. So and what we found is we have reporting that goes up to the CEO. Yeah.

Right. All the way back to the model developers because the other thing is look you're still dealing with a culture of an organization. You still have the highly paid PhDs who are developing models, right? Or working with other companies models and they want to see their products in production.

There's got to be nothing prouder for a data scientist than to see a an a business impact based on a model they developed.

Jim Schleckser: Yeah. No doubt. Get stuck in if it gets stuck getting into production. Boy, that's got to be that's frustrating.

You know, I you're making me think of is an old programming book. It's uh The Cathedral and the Bazaar if you probably read that one but I do know you know you know it and basically is uh software development in hierarchical versus bazaar sort of y you know random movement of people and the speed of those and so how do we deal with sort of deploying this infrastructure in place and making people run through the channel versus this the sort of the impact of let's have a million monkeys try to write Shakespeare and you know is there a speed event because I'm I'm I'm thinking like what some entrepreneurs are listening, they go, "That seems like a lot of stuff and is that going to slow me down?"

Pete Foley: Absolutely. Um and that is that the number one um objection is well there's two objections. One is do I have the right value of models or the right volume of models? And I'll get to your question here in a so organizations and you know we have some public case studies.

Fidelity is an example, right? Value is amazing, right? because they've got insider trading models, they've got uh, you know, bad actor models. Um, yep.

That brand that we talked about, boy, that's a that's a first class brand, right? We want to make sure those models are in compliance, right? We want a full life cycle on that. Second is volume.

So now you get organizations like in the insurance business, right? Who are consistently pricing, consistently estimating, complic consistently, you know, have doing working alongside their actuaries, right? understanding risk, right? Thousands of models, right?

So, there's that volume of models, right? So, so that's an easy going back to your original question. Absolutely. If we can get to scale with those either one of those, get a get a value model into production faster, get get, you know, a number quantitatively a quantitative number that's assigned to how many more models we're using to estimate risk, big win, right?

But you're exactly right. The challenge is when you when you know you've got a organization that is built around the data science community it says look to your point you know and you know our president today we don't want a lot of uh harnesses on AI right now right we do want them to run the challenge is when you start looking within the enterprise though and I mean the true enterprise not the digital native not your startup community not your entrepreneur I'm all about that I think that's great. You know, let's test and try and get out there, you know, with a dev dev product or in a dev project and try and fail. But if I book my ticket on American Airlines and I see a $17 ticket to London, call your friends.

Call your friends. Calling your friends, right? And so that's that to me is, you know, that's the difference. So that's when I qualified early that is not as a challenge within an organization an established enterprise organization because again you talked about slow moving I mean the even the adoption of AI within those large organizations is so fundamentally um slower I'll use that word right than than your digital native or then your your early stage or even your midmarket because there's so more at stake.

Jim Schleckser: Got it.

Pete Foley: So, there's value, there's brand, and there's there's business impact that goes way beyond and they don't have that ability to change quickly. So, so think about us as kind of that air traffic controller, right? We know every plane that's in flight, who's taking off, when when hey, if a plane's uh not performing, you know, take that plane out out of the out of the system, right? um if that plane is too expensive.

I mean those are other aspects and we can talk certainly about the value of those models, right? And we're all familiar with you know we all wish we had Nvidia stock because the cost of those models is enormous. Yeah. Right.

And there's never been really to your point earlier about um you know innovation and you know running wild and understanding that and that's what organizations have been doing on the development side like look we'll spend $50 million on GPUs we're going to hire 100 data scientists we're going to go attack you know 20 use cases right and that's great you know it's like sitting around a high high frequency trading right and who can come up with alpha first because we know if we can get alpha boy it's going to pay for all of this investment

Jim Schleckser: So this feels like startup community, you know, Silicon Valley, they don't care about this. This is just innovate or die. Go fast. The issue is people with more to lose.

Pete Foley: Absolutely. I've got a brand more to gain and more to get both.

Jim Schleckser: Yeah. Because they're scaled, right? Yeah. That's where this really plays.

So I think early stage they go not interested, not interested, not interested. And then they have this moment. They go, I just lost control. I don't know, right?

I don't know what I have. I don't know what it's doing and I got a problem on my hands.

Pete Foley: The way the best way to do it too until you get to scale as an organization as well. So that those model developers really are charged with the governance and the life cycle management of that as well. If you're not at scale, look, I've got a model I just developed. I'm now have I have access to the data source.

I have access to the security posture. I can test that model myself now, monitor that, and then put it into production. you know, sign off on it almost myself. We My VP of product is a great saying.

It's like when you get to the enterprise, you can't have the school children grading your own papers.

Jim Schleckser: Yeah, that's my favorite. You can't grade your own paper. Yeah.

Pete Foley: Papers, right? I love that. And so the way that it's done historically is with people, you know, actuaries, PhDs that come out, these full hundreds of people, hundreds of people in these teams of what they call model validation, right? just to be able to validate that model's been tested.

meets the rigor of the organization and that's not scalable when you start talking about even and we're not even talking about agentic because that's so early within the enterprise but back half of next year those thousands of models we're talking about they're talking about millions of models I mean literally organizations have so many use cases and rightfully so and it is back to what we you said earlier you know there's a binary there's really is a binary profile of organizations in the next three And I truly believe that. So that's the why now. Going back to what I had originally stated.

Jim Schleckser: No, that's that's perfect. You know, you talked about, you know, compute. By the way, there's a whole now conversation around compute and the depreciation rate of the compute. Yes.

People putting literally trillions of dollars into GPUs and so forth. Yes. And it's not like laying a railroad that's good for 50, 100 years. It's like you got about three to five years and that's not going to be useful anymore.

So that investment is renewable and it's not quite disposable but it's renewable and I think the other impact is model efficiency is going to come into play here. Yeah, right. Historically just thrown we threw GPUs at it and but eventually people go we need to build better code that's takes less compute because the economics don't work anymore.

Pete Foley: But if you if you don't assign if you don't have the real- time metrics and monitoring capability that you really have to start with that baseline and that is goes back to doc what you said before is like you know even the large scale organizations go this way then they say oh you know what you know we just we just had a model failure we need a solution like yours. Yeah. Our pitch is look start now. Going back to your railroad analogy put the tracks down.

You know you're eventually going to go out to those other other cities. Put the tracks down now. Right. is so much easier than trying to, you know, declare eminent domain and put tracks down later.

So, yeah.

Jim Schleckser: Well, my point I have that epiphany that, oh my god, I just lost control. Now, we're going to go solve it. That's a little it's a little too little too late. Yeah, no doubt.

What about and this is sort of off the topic of what you guys do, but um using AI models that use your data to train. So, I know there's some options around this, but this is a big concern of CEOs on the risk side is I start putting the company golden goose into my AI and I just trained all my competitors the same thing. How do you think about that? Or is it just as simple as either buy an enterprise version or set the settings right that it doesn't train on your data or is there something more sophisticated here?

Pete Foley: Um, well, we first of all, we that is uh proprietary to the organizations. So, we do not offer data for training. We certainly can offer lab environments to test our product, right? But no, I mean that in the end too is what my you know when I when I tell organizations all the time, look, you can go and sign a contract with GCP, you know, Google, right, or Amazon and have them do all of this for you.

Yep. But boy, do you lose your proprietary edge and it goes back to exactly what you said because you have hundreds of years of data. Yeah. um and it is extremely extremely competitively differentiated for organizations especially in some of these large enterprises.

Um, so I think that is uh you know that to me is the biggest reason why you want again I go back to this uh if you call it a model factory right regardless of what model type that I'm going to introduce because those are going to change dramatically right nobody was talking about now that you know small language large language even I get confused sometimes yeah agentic you need that model factory that this model life cycle ical solution that says it doesn't matter what we put into the factory into the top of the funnel, we're still going to get all of this value of visibility, policy enforcement, both internal and external and monitoring with reporting capability. Yeah. So everybody feels comfortable in the middle of night that you know while the sun's up on the other side of the globe, we're pricing tickets at the right right value or we're trading at the right right value.

Jim Schleckser: So yeah, the policy element of what you guys do has that's still human involved.

Pete Foley: It's very human involved, right?

Jim Schleckser: Right. You can't have AI set the policy. A human has to set the policy or you can use regular AI tools.

Pete Foley: You use regulatory environment like to use your FINRA example like we need to comply with these regs, right? Yeah. You know, SR 11-7, right? In financial services, there's insurance regs, right?

There's state-level regs. So we do provide out of the box policies just to get an organization going. And then there's a number of uh you know GRC vendors right your your vendors that do provide those those policies as well that are that are again out of the box. Um and then but the specific policies within the organization there actually is a good use doctor for AI in taking those those uh that unstructured data right and applying it into policies that can now be read into templates within a within a solution like ours.

Jim Schleckser: Right. So it's it's a just to think you know again air traffic controller we've got a you know a new policy on airspace over Missouri. Yeah. Sure.

or different space between plans on a landing because of some reason or whatever. Exactly. You know, you talked a lot about model control, model management, but data is the new gold, right? And you kind of referred to this like hundreds of years of data that is proprietary and it gives competitive advantage.

How do you as a board member I worry that too. I worry data sources, data access, corruption of data. I worry about all that. sort of below agent but still super critical because everything's built on it.

How do you engage in the data conversation or how should people be thinking about this?

Pete Foley: We truly are model-centric so we don't touch your data governance um your Okay.

Jim Schleckser: But they but they should be thinking about it, right?

Pete Foley: Oh, absolutely. Yeah. Great. Yeah.

I mean that you know there's so many good data governance tools out there as you know um and more coming you know every day because there's an explosion obviously of data. Um what we do is again it's all policy based you know access control to which data where um who who where and what right so yeah um but no it's it's it's um and because that is the you know that's the commercial right I think Salesforce and uh Snowflake and everybody else will tell you that the assets are in the data so they are and because we finally you know the models have consumed the entire internet Now what?

Jim Schleckser: Right. Yeah. Is where how are they going to get trained? Um maybe just business question.

You guys private equity funded or VC funded?

Pete Foley: Uh VC funded. Yeah.

Jim Schleckser: VC funded. Okay.

Pete Foley: So at some point Chicago-based Baird Capital fantastic firm here.

Jim Schleckser: Good firm. Yeah. Good good Midwestern firm. Yeah.

Pete Foley: Absolutely.

Jim Schleckser: Particularly since you spend time in the valley. You appreciate good Midwestern gals.

Pete Foley: Yeah. We have some valley investors too. But it's it is good. I mean and this is a unique and u as I said solution because we really are just focused on the large enterprises.

Jim Schleckser: So yeah, absolutely. Well, but there's an exit or a transaction at some point in the future because these guys aren't in these things forever, right?

Pete Foley: Sometimes.

Jim Schleckser: Okay, there you go.

Pete Foley: I think this is so early. We're so early in our market, doctor. I mean again it just goes back to when when when we talk to people on the street everybody I know my family friends they all assume that AI is pervasive everywhere. Yeah.

And you go into large organizations you go into pharma you know who probably has the most to win by getting AI to scale. It's still really early.

Jim Schleckser: I know you hear these cases of you know protein folding and we figured out a new drug you know but I think they're sort of irregular oneoffs. It's not systematic yet, not completely.

Pete Foley: The clinical side is one-offs. You're you're exactly right. Yes. Um but where they're stepping in because I have pharma customers is on a lot of the operational side.

Marketing, right? I believe that. Yeah. Ton of marketing data.

Yeah. Awesome. You know, a lot of the big farmer are marketing machines. So, yeah.

Jim Schleckser: Marketing marketing companies parading as drug companies, right? Um well, this was great, Pete.

Pete Foley: what uh if somebody wanted to engage you probably a little bit larger enterprise what would be the best way to get a hold of your organization and talk to you about whether there's a fit or not what's the best way to do that yeah I think just the website's simple right we're as you can imagine we're early stage we're going to get back to you anytime you can always reach out to me on LinkedIn again it's Pete Foley and that's really short for model operations right so you think about Like I said before, kind of this whole concept of life cycle management.

Jim Schleckser: So and you got ModelOp.com.

Pete Foley: We do.

Jim Schleckser: How the heck did you get that?

Pete Foley: We're early. I tell you, you go back, we you know, we literally started as a consulting organization nine years ago.

Jim Schleckser: Okay.

Pete Foley: And we've had a product out for four years. And if you look on our website, we our dev partners were, you know, two Fortune 100 companies. So it's it's an extremely scalable solution. And I've done a lot of early stage companies.

um as you mentioned because I'm old.

Jim Schleckser: We're both old though. You're in the club, man.

Pete Foley: But it's uh but the enterprise uh the enterprise capabilities of this of this product is unbelievable.

Jim Schleckser: Awesome. I encourage people to watch videos first and then and then ask to be contacted.

Pete Foley: Love it. And we'll put you on the waiting list. Um thank you.

Jim Schleckser: Um thank you Pete. This was great. Thank you for the conversation and the time.

Pete Foley: All right.

Jim Schleckser: Thank you for organizing and uh for all of you that are listening, thanks for coming along. We'll see you next time on the CEO Project Podcast.

Narrator: Thanks for listening to the CEO Project Podcast. We'll see you next time. Be sure to subscribe to get all the future episodes and check out our website theceoproject.com.