Doyl Burkett — Integrity Growth Partners | AI Won't Break Private Equity, It'll Sort It

Doyl Burkett is the founder and Managing Partner of Integrity Growth Partners, an $800M+ AI-enabled growth private equity firm in Los Angeles. Most GPs think about what AI does to the companies they own. Doyl is equally focused on what it does to the firm itself, and he's been building Integrity as an AI-native investor since 2018. In this conversation he argues that AI won't compress private equity evenly, it will sort it, and he shows what that sorting looks like for companies and for the GPs who back them.

This is an episode of Proof of Work: AI Value Creation (formerly Just Curious), the podcast from Pluris on how AI is actually being adopted in the mid-market.

You'll Learn:

• Why the "AI kills software" narrative is too simple, and the three-bucket framework Doyl uses instead (AI-native, defensible-moat, undifferentiated)
• The six kinds of moat he underwrites for in the AI age, from proprietary data to regulatory to human-in-the-loop
• How an AI-native firm actually runs day to day: CAPE across sourcing, reporting, and operations
• Why GPs themselves are about to be sorted, and what separates the firms that can still raise from the ones that can't
• How Integrity used its own AI sourcing engine to find the AI acquisition that nearly doubled a portfolio company's revenue line
• The AI-adjacent decision he'd most want to redo, and why market reaction can matter more than the technology itself
• Why there's no AI quick fix, and how to tell a real capability from a wrapper

Chapters:

00:00 - Intro: what makes Doyl's seat unusual

01:04 - Who is Doyl, what is Integrity, and why "AI-enabled" from day one

03:06 - The one thing to walk away with: why "SaaS is dead" is too simple

04:24 - Bear case 1: does tech risk become existential, not manageable?

07:02 - What defensibility actually looks like: the six moats

12:03 - Bear case 2: did PE buy the disrupted, not the disruptors?

14:16 - Leverage as an amplifier: how AI changed the capital-structure posture

18:38 - The three-bucket framework for how AI sorts the asset class

23:43 - The Adobe parallel, and how to tell AI-native from AI-marketing

27:14 - What an AI-native firm looks like: building CAPE Sourcing

32:55 - The payoff: doing more with fewer, getting there first, winning on trust

35:53 - CAPE Operations: codifying the playbook and the CEO network

38:59 - The portfolio conversation: AI as opportunity and threat, every time

41:20 - Prop-tech case study: using AI sourcing to find an AI acquisition

44:46 - Fund-level AI: CAPE Reporting, automating DDQs, the future of investing

50:28 - "I want to be the wheat, not the chaff. Where do I start?"

52:26 - What not to do: the AI quick-fix trap (Ferrari vs. Ford Focus)

57:05 - Closing thoughts

🎧 Listen on Spotify: https://open.spotify.com/show/3OlTNvh2FGlE4VJyCrMJVE

📺 Watch on YouTube: https://www.youtube.com/@JustCuriousAI

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Proof of Work is the podcast of Pluris, a platform connecting investors and operators with the world's leading applied AI experts. Each episode features the builders, operators, and investors who've actually put AI into production, turning it from buzzword to bottom line through sharp case studies and practical conversations. We explore how AI is used to grow revenue, expand margins, improve operations, and create measurable value inside real businesses.

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Transcript

SPEAKER_00 0:00

Today we're joined by Doyle Briquette, managing partner of Integrity Growth Partners, a Los Angeles-based growth private equity firm with over $800 million under management. What makes Doyle unusual is that most GPs are focused on what AI means for the companies they invest in. Doyle is equally focused on what it means for the firm itself. The lazy version of where we are is that AI breaks PE. Doyle's view is more nuanced. AI isn't going to compress the asset class evenly. It's going to sort it. Companies will end up in one of three buckets, and the same sorting is going to happen to GPs themselves. He's running that experiment right now at integrity. He calls the framework CAPE, and it runs across sourcing, reporting, and operations. Today he walks us through where he diverges from the consensus, what he's built inside the firm, and the AI adjacent decision he wants most to redo with what he knows now. Doyle, thanks for joining us. Thanks, Stuart. Happy to be here. For those meeting you for the first time, who are you? What is integrity? And what's distinctive about your seat in this conversation?

SPEAKER_01 1:06

Yeah, Doyle Briquett, uh founder, managing partner of Integrity Growth Partners, a growth private equity firm here in LA, as you mentioned. Um we invest in software and tech-enabled services that are founder owned and operated, bootstrapped, so they haven't raised capital, they've skipped the venture model. Um, so they're cash efficient. And I've been investing in growth and buyout private equity for almost 30 years now. And so it's been my my entire career. In terms of what makes me and us different, is I don't know how many of your listeners will know what growth private equity is, but uh what the the thesis of it is you try to create your own deal flow by hiring a bunch of young people to go out and and contact these CEOs. And when I ran Kane Anderson's growth equity practice for almost uh for a total of eight years, our CEOs would tell me, Doyle, we don't like talking to 23-year-olds, and what you're doing isn't differentiated at all. And this and the young people would turn turn over because it was super manual. I joked that my 13-year-old would probably last maybe two or three months. And so we started integrity with the idea to use at the time early AI predictive analytics to automate most of the mundane stuff to source with a more senior team. And then we'll get into it here. But we've now hired somebody, we've blown through that and have now our AI-enabled one, and we increase that to other things like reporting and operations. So we are basically an AI-enabled growth equity firm and have been from the start and feel like we're, you know, kind of maybe not light years, but way ahead of almost anybody, and certainly way ahead of the people that we compete against in the lower middle market.

SPEAKER_00 2:37

Yeah. I'm excited to get into what you do and how that serves your portfolio companies and how it serves your investors. But before we get into it, what's the one thing you want listeners to walk away with from this conversation?

SPEAKER_01 2:55

Yeah, I'm gonna not to be a a a bad guess, but I'm gonna say there's two things. There's the general one and the selfish one. So the general one is, you know, the narrative right now, it's less so than it was, you know, four or five months ago, but is that AI is gonna eat all software and you know, SAS is dead, the SaaS apocalypse, whatever you want to talk about that. And and you know, you and I have talked before whether it's, you know, that that tends to be more of the the headline grabber, but that's not the case. I'm not saying there are no risks, there absolutely are threats and up and and as well as opportunities, but we think of that as more of a peanut butter approach spread evenly, and it's uh a lot more nuanced than that. You know, there are certain uh types of businesses that are very well insulated, there are certain ones that are probably worse than having eat their eating their lunch. They're going to, you know, vanish fairly quickly. So that's the general one, but the more specific one's kind of the one I just mentioned earlier, which is you know, we've been doing this for eight years. We we didn't see how fast AI was going to happen, but we did see it coming. And so we have set up our business accordingly and our investments accordingly. Like we haven't invested in a per seat software business in years. We only focus on a few defensible motes. And so the selfish part would be, I think, that we are a differentiated firm, GP and firm within the private equity landscape. Yeah.

SPEAKER_00 4:11

Let's talk about that narrative that AI can break private equity. I I ran three specific bear cases by you. You partially agreed with two and pushed back hard on one. I'd love to walk through each one and you know, kind of capture that that perspective and that nuance. The first, I guess, is that tech risk becomes existential and not manageable.

SPEAKER_01 4:40

Yeah. So on that one, you know, I think it's this is the one I or one of the ones I I partially agreed on. And the way I look at it is it depends on what type of firm you're in. And one of the things you didn't mention there, but was involved was uh underlying leverage on some these companies, right? Because what we're seeing now in in kind of the bulge bracket, maybe software tech investors, is this gets back to market sentiment, a lot of the lenders are saying, I'm too heavy into tech. So I'm not going to, if Stu's business has got uh, you know, I don't know, 100 million of debt, we're not gonna re-up that debt. It's coming to maturity, and that puts the business at risk if they're also facing headwinds. But specifically, why I partially agree to it is I think it's depends on where you're focused. So we're focused on, like I said, founder owner-operated, bootstrapped, lower middle market businesses that we were already AI forward or AI thoughtful. They have many of these businesses have defensible moats. And so in some cases, I'm not gonna say we're not worried at all. I think every business should be thinking about threats and opportunities related to AI. If they're not, they're they've got their head in the sand. But we feel like we're pretty darn well situated. Where I would feel very poorly situated is if I'd did a huge LBO, big on the L, a lot of leverage, had not been as thoughtful about underwriting future tech risk, and was just relying on the fact that it's a software business with per seat, you know, recurring revenue, and that is a leader in the space, and it's never going to get disrupted. And so I do think it it ends up being maybe not a barbell, but again, uh there's a continuum, a bell curve of of how uh tech exposed you you are, really based on I think how well you potentially foresaw what was coming. Not as nobody saw how fast it was coming, but how much you've been thinking about. We've been looking at AI underwriting for three or four years actively.

SPEAKER_00 6:39

Yeah. And what does that look like in practice with software and technology-enabled businesses as your focus? Those are businesses that people would view as more at risk. You mentioned you're looking for businesses that have strong modes. Give us a sense of like when you see a when you see a company or looking for a company in this space, like what are you looking for from a mode perspective, a defensibility perspective, an integration into workflows perspective, et cetera?

SPEAKER_01 7:09

Yeah, we basically have, I guess, a half a dozen um and and this may continue, it has to continue to evolve. I think that's the the point. But right now, there's six. Um our last two investments are AI native businesses, one's in ad tech and one's in cybersecurity. Now, again, I don't think you can rest on your laurels and say, hey, we're AI native, and so we're forever, we're ever defensible, but that is a moat currently, and as long as they continue and invest in it, um we believe that will can that will continue to be a moat. One and second one is uh proprietary data. So we have a company in the post-resident account receivable world, which is a lot of words for when people move out of apartments and owe money, these folks go out and recover that. And I don't know, you know, you've got kids, that maybe some of the listeners have kids. The best way I can explain what they do is if you've ever taken your kid to an ER urgent care on vacation, about almost I'd say five, six times out of ten, I get a bill or excuse me, a nasty letter from collections a year later that I own fifty bucks or a hundred bucks. That's what happens when people move out of apartments, and these guys solve that. They're the only company in the space solving it. We just crossed a billion dollars of receivables recovered. They've they have eight over eight billion in their system, and their AI is learning from their proprietary data. They have, you know, uh propensity to score calculations that are going on. They're able to A-B test on making certain offers with discounts. You know, if they say to Stuart, I'm gonna offer you a 40% one-time discount versus Doyle, I'm gonna offer you, you know, a payment plan. And so they're the only ones learning from that proprietary data. And so who owns the data in that case is very much of an insulator for them. The the third one is is, for lack of a better term, you know, regulatory, you know, moat. So like we have a healthcare IT business that has a billion scans of lungs and other of other ancillary uh, you know, uh modalities. And because of HIPAA in the United States, there's other things elsewhere, like that data cannot be shared. So our AI is learning from that. And that's you know, FinTech has regulatory modes, et cetera. We don't have anything in fintech currently. The next one would be um I've got an aura ring here. So we talk about hardware enabled. So AI can't get into that aura ring. We have a business right now that's in the healthcare space from my parent, my dad's got heart failure, my mom's got high blood pressure. Our software pairs with the devices. People's AI can't get into our devices, but we can learn from it. And there's a current example, we're looking at a come, I'm rebuilding my house after the Palisades fire, as I think you you know. And we're putting sensors in the concrete and the walls to test the efficacy and the of the building. You can't get AI into those places. It's truly impossible. And then the fifth one would be my most recent investment I led, which is human in the loop. It's a it's a defensibility. I won't go belabor it, but it's basically in the pest control space. And we partner with pest control providers to offer owners of single-family homes that they rent out in mass, basically taking the pest control work off their plate, and it gets somebody like me, like as I'm renting right now, a pest control provider, the right pest control provider there faster. You can't, at least for the next five or ten years, take the pest control person out. Maybe a robot will do it in five or ten years. But even if you do take that person out with a robot, you can't take the owner of that business out. And these guys have partnered with all the leading pest control businesses across the country. So that's a human one where AI can't displace that. And the last one, I you know, listened to your last uh podcast, it really got into what the Reich CEO talked about, which is being that, you know, we dumb it down uh to be like Excel for analysts, if anybody's ever been, you know, an investment banking analyst, something you live in every day. That is, it is the system of life for you. It's your lifeblood. You cannot live without it. If somebody came to you and said, you know, this is something that we, you know, I'm gonna take away, your day would change. And that can be in, you know, it bait vertically, vertical SAS um in certain instances can be that, but not all. Um you know, payments can if you're involved in the payment stream, but things like that where you really can't.

SPEAKER_00 11:18

Yeah. Let's go back to the uh bear cases. Um another one would be that private equity bought the uh and this is kind of related, brought the disrupted, not the disruptors. You push back hard on that, it's obviously a uh a provocation.

SPEAKER_01 11:33

Yeah.

SPEAKER_00 11:33

What do you think?

SPEAKER_01 11:35

Yeah, for that, I mean, I think it's just I I think I said before, I think it's a little too simplistic in that you know, there's so many different types of businesses. I'll take one extreme, right? Like if you take a manufacturing business that is reliant on certain aspects of human work that currently cannot be done. And I know China's way ahead on on robots, but that's currently probably not gonna have much of an effect, and as long as they invest in robotics will. If you look at, you know, a knowledge-based business, you know, maybe like not to pick up like a fact set or something like that, that's gonna be incredibly displaced. But when I think about what we do in general private equity, and I'm not talking about like the killer venture firms that are looking for the decacorns or the big you know, bullish bracket buyouts that need to invest in the category killer or winner. That's where I think what you're talking about does work. But what we're looking for, specifically what a lot of firms are, we invite invest in businesses that have 10 million of ARR and we grow them to 40 or 50. They may be in like Salesforce was a was a category winner in CRM query, whether that you know continues going forward. But we've had successful investments that sell into that space, and we don't need to be the biggest disruptor. We're not gonna change the industry, we're not gonna be the thing that the business that everybody goes to. We don't need to build the decacorn. But if we can grow a business from 10 to 40 or 50 and sell up the value chain to another strategic or private equity firm, there's a lot of money to be made there. And on that continuum, I could say the same for like my old buyout firm, where if we invest in a business at a hundred million dollar revenue and grow at three or four X, like you don't, it's not a winner or winners take all. That's that's really my pushback there.

SPEAKER_00 13:24

Yeah. When it comes to the bear case, but also uh the bull case, leverage is an amplifier. Um, how has your posture on the capital structure changed because of the potential opportunity and risk from AI?

SPEAKER_01 13:42

So ours on the cap structure hasn't changed, but let me describe what we've always done. So we never put debt in to a growth investment when we start. We like to crawl, walk, run. So after we've been married to the management team in the company for maybe six months to a year, two years. And let's say we are cash flowing very nicely, we might use our debt capacity there, our our EBITDA or groups will lend on ARR to make an acquisition. Or, you know, maybe if things are going incredibly well and there's not an acquisition, uh, even though it's kind of can be a dirty word in private equity, but maybe we do a recap and send some money back to our investors. But we do it not at the outset, and we do it later and at a lower l lowercase L in the LBO world than the then so what you know, one of my best mentors talked about debt takes you fat places faster than you otherwise would get. It's a little bit like your, you know, your home. One of the ways people in America and across the globe, I assume, make money is you only put 20% down. So the 80% is what allows you to really ratchet that profit up. Well, the problem with where we are right now in this bear case is that's all predicated on valuations going up. If they go down, you can end up in a scenario where the debt is consuming almost all or all of the value, and then you get into debt service issues if the business is facing headwinds due to either maybe it's simple as it's per seat pricing and seats are going down, or maybe AI is making more inroads for whatever reason, or maybe they just hit a couple stumbling blocks. If you combine a heavy debt load with headwinds, now you can get to a point where, you know, I forget who it was. I think it might have been Tilma Bravo recently handed over the keys to the banks on a you know multi-billion dollar investment. Those two things combine. You know, the high, the high debt load combined with headwinds can put you in a position that's completely untenable, and then that's where the thing falls apart.

SPEAKER_00 15:36

Yeah. And so like understanding that you don't put a lot of leverage on these businesses initially, you see how they are performing and you might walk after you crawl. Are you taking a more conservative posture, kind of on apples to apples basis? Or you know, are you always sort of looking at this conservatively and it's one AI and an existential threat is just one of the inputs that you are evaluating when you're deciding how much uh leverage a business can take on?

SPEAKER_01 16:06

Yeah, we are we are definitely taking a more cautious approach because you know you mentioned earlier, you know, private public markets, but public markets can influence private markets. So what we don't want to be in a position is one where we do crawl, walk, run, but we end up getting to a business where, you know, maybe five years in, we do have a decent amount of of leverage, nothing, yeah, nothing huge, not like five, six, seven turns like some of the the bigger players would would use, uh, five, six, seven turns of EBITDAW. But maybe we're at three or four. And because the market sentiment is they don't want to, they, the lenders, have as much exposure to tech because they're getting pressure from on high from their investment committee or their board, that they're not going to re-up our business, you know, when the debt comes due, and that puts us in a potential difficult situation. If others are taking that view, because that's the view of the of the day, month, week, or day, week, month, I guess I should say. And so, yes, we are taking a more conservative approach. It's not a key component of what we do. So I wouldn't say it's significantly affecting, but where I would where I would go is, you know, if we were, we did a deal a few months, a few years ago where we funded the entire add-on acquisition. We had zero debt and we funded the entire add-on acquisition with 30 million. I think if we did that today, we probably might fund it more with 20 million of debt and maybe 10 million of equity just to be a little bit more conservative.

SPEAKER_00 17:31

Yeah. Let's talk about the asset class. Uh in my intro, I spoke to uh how you don't believe uh AI is gonna compress it evenly. Uh it's gonna sort it. Uh you mentioned to me three buckets. And so um yeah, I would love to walk uh through like that three bucket framework.

SPEAKER_01 17:53

Yeah. So I guess I'll start hit hit hit them briefly and then go into depth. So the three kind of and these are again, it's a simplistic view, it's been no, but it's it's a real easy one uh to kind of remember and it hits most of the points. So the first bucket would be the AI native AI forward businesses. Uh second one would be the defensible motes in the AI age that we talked about uh earlier, that I talked about earlier. And the third one are the ones that are you know really don't have those defensible motes or on the the losing end of this AI transition. And so on the first one, what we're seeing is that is where you're you're getting multiple uh pressure up. So, because people are looking at these businesses, it's the new hot thing. And so if you're looking to invest and I'm looking to invest, and everybody's kind of scared that, oh, you don't want to miss out on this AI forward, AI native business, that is one that pushes the multiples higher, which then makes your return harder to get. I think it is more defensible as long as you continue to invest in it, but that's kind of the the yin and yang of of that one, right? It's not it's not perfect. You know, a lot of what happens in our world when people don't make a great return is that they've overpaid for things. And I think the risk with with that bucket is is overpayment, and the other risk is relying on what is AI forward or native today, which in three or four years, if you don't do anything to continue to develop, may not be then. Um the second bucket is what we look at, we look to have each one of our businesses have one or more. So we'll do AI native and AI forward, and that's one of the defensible modes. But then the other defensible motes are all the other ones I mentioned, and we look for them to have, you know, at least one or two of those. And what we're seeing right now in the market is a kind of a status quo-ish. You're seeing the public markets push multiples down for those businesses, which impacts our exit for the ones we have, and it impacts our entry for the ones we're coming in on. So we're on both sides of it. But we're not the only ones, you know, I'm never gonna be, and my team is never gonna be the smartest people in the room. I think we're pretty darn bright. But like other folks are starting to flock to those businesses. And so that is pushing those multiples up because there's scarcity value there, and it's somewhat a status quo between the market pressure down and the further interest in those businesses pushing them, pushing them up. Um, you know, it's a real-time example. We have one of our businesses, our healthcare IT business that has grown when we invested at 7 million of contracted ARR is now mid-30s, should be kind of 40 by the end of the year. And we've had multiple groups try to preempt at, you know, double-digit ARR multiples. So it's a strong, defensible one. Like you would not know that this AI SaaS apocalypse has changed anything because those are the same types of multiples we would have seen, you know, a year ago. Maybe they would have been higher without the market pressure. You know, that's a tough one to tell. But uh, and then the third one is is then less defensible or completely not defensible, tend to be more with pricing on a per seat basis. And and a per seat pricing is not, you know, a death knell. You can change those as I'm I know you've talked to some prior guests who you can get into, you know, value add, you can do a take rate, you can do flat pricing, for example, you know, just hey, this is what you what you pay. But if they've got significant headwinds and are don't have any of those, uh, because of AI, don't have any of those moats, we're seeing significant downward pressure on mold there. And so on the negative side, I think if you've got those assets, it's gonna really hurt your exit because if you bought it, I'll just throw out like eight times ARR and you can only sell it five or six, it's hard to really make a great return. On the flip side, I do think there's some opportunity there that we see similar to 15, 20 years ago, or maybe 10, 15 years ago, when people moved from a licensed software pricing to SaaS, which hurt your cash flows for reasons we don't, you know, too long to get into here, but timing wise, there isn't a there isn't an impairment to cash flow by changing your per seat to another model. It just takes time. Time to do it and there's some risk with your customers, but I think there's opportunity there to buy a at a much lower price, maybe change the, or not maybe change the pricing model, improve their position as it relates to defensibility in the AI space. Maybe it's through acquisition, maybe it's through changing some of the business model and actually making a really great return. So those ones have both sides of it as well.

SPEAKER_00 22:21

Yeah, it's interesting. I remember when Adobe transitioned their model from selling a license on a one-off basis to the subscription and the uh stock cratered. Obviously, five years later, it turned out to have been a very wise decision. It enabled a better business model. So I'd be curious to see whether that may happen here too. How do you how do you evaluate first of all, I guess, how do you define an AI native or AI forward company? And then how do you evaluate it from the outside? Given today, I would imagine that a lot of companies that are tech enabled want to position themselves as AI enabled and AI forward and get those multiples and get out of the other buckets. Yeah, how do you think about that?

SPEAKER_01 23:03

So look, we're not technologists, but we've been doing this for myself almost 30 years, my partner Ryan, you know, 20 years. So good, I would say the maybe the 75, 80% of it we can do by pattern recognition, diligence, talking to the companies and and their team, especially on the tech side. But we need the the real experts to come in for the for the rest. And so we have a group that we've done third-party tech due diligence with for over a decade. And they come in and evaluate all aspects of the business, but they're and we're not the only one that use them. A lot of other private equity firms use them too. But what we've always liked about them is they're comparing our lower middle market, you know, $10 million ARR business to the most well-funded private equity, venture-backed, you know, public company that they're working with. And one of the areas that they're going to focus on is how integrated AI is into their product and their offering, and what are the further risks and opportunities there? And so that's the latter part of your question. The first part is, you know, and maybe give some examples. Like one of our businesses that we just invested in is a cybersecurity business focused, basically built on AI. And it is a leader in the space, although it's a small leader, you know, it's a it's only got seven or eight million dollars. I guess now it's up to 10 or 11 of ARR. But it was proven in our diligence talking to customers, which is one of the best ways. If you really want to know, you know, your last guest that I listened to talked about knowing your customers, the best ways we can figure out one of the best ways, in addition to our third-party tech to deal with, is talking to these customers and the decisions they made evaluating our portfolio company or potential portfolio company versus what else is in the market. And so they'll give you great feedback on is this root real AI, is it defensible? And the second one we invested in, the founders of uh Keeler.com built an AI forward ad tech business, and they're on the cusp and the one of the leaders in what will eventually happen. I don't know about, you know, my kids don't Google anymore. They're always in, you know, ChatGPT or Claude. Ads are going there, and these guys are well ahead of that and are per are positioned, I guess, to take advantage of that. And so it's a combination of our pattern recognition as well as cons significant third-party tech due diligence, which we which we rely a lot on that, and then uh talking to customers. But I'd say the middle one, the third-party tech due diligence, is probably the one we hang our hat on the most.

SPEAKER_00 25:35

Yeah. Let's talk about the investing business. And I want to really go into what you do uh and how you do it and and whether it's sourcing or operations. But I want to get your thoughts on the business, I guess, at a higher level. Like what is it, what does an AI native private equity firm or growth equity firm look like? And what does the private equity industry look like three or five years from now, given uh what's going on with AI today, but also you know, what will happen if things continue to um you know compound?

SPEAKER_01 26:09

Yeah. The second second part's harder, but I'll start with the first part. Uh so how we look is, you know, so sourcing, we started our first generation of sourcing, we built on early stage AI, which was predictive analytics. And it was myself and my team uh who worked with me and my prior firm and we founded the business together. And I kind of make it equivalent to a uh or equate it to a uh a muscle car. It was built on know-how and spit and you know, elbow grease, and and that's as much as best we could do. We automated about 60, 65% of the sourcing tasks. We knew technology was changing, AI was coming, we didn't know the speed at which it would happen. So we brought in a gentleman, Will Nunez, who is our principal in data science, engineering, focus on AI, and he built something similar, maybe not as robust for a prior job, a company he worked for, worked with the Fed, really big brain, and he built our second generation, which is our AI-enabled sourcing. And what that looks like for us is he evaluated six or seven of the leading data providers. We used to have like one or two. Um, used to come into our CRM, which was Salesforce, and the only reason we used that is ironically, it worked, it integrated with an API with the algorithm scoring system we used, and we knew how to do we didn't have to create that. So he basically blew that up and has these six or seven data providers come into our data lake, or basically our CATE, which is comprehensive AI process enhancement, everything lives there. And we took that term CATE from we made the acronym, but one of our COs talks about AI being a CAPE that ever makes every man or woman can be a superhuman, superhero. Um so that's kind of why we adopted it. And so that data comes in and he fuzzy matches it. So let's say, you know, you know, Plurus was a uh, you know, or just curious, was a was a software business, and you have all this information out there. It's public, so this is like number of employees, what year you were founded, what industry you're in, what's other good examples? You know, basically business descriptions, how much capital you raised. There's a handful of more. It comes in and it, but not all the data is easily identifiable with the same company. So it fuzzy matches it in our data lake to get a fuzzy three-dimensional view. And then we take our LLMs and web scrape and bring in more information. A lot of this is about, you know, awards they've won and interviews they've given. And it's one of the hardest things to just figure out with these companies what they actually do. And now we have a pretty clear, three-dimensional, crystal clear view of this business that is proprietary to us, built off things that aren't proprietary, but we've put it together. And that takes the 50 million companies that kind of are in our ecosystem and allows us then to sort them in what I call kind of the most simplified Harry Potter sorting hat. So it sorts it to fit or not fit. And we've been building this for eight years, and so every company we talk to, we tag things that this is what makes it likely to be a fit for what integrity does, and this is what it makes it unlikely to be a fit. And it learns from itself and continues updating it, and it sorts into the fit category for the ones that are fits. And from there, he took our algorithm, and now the algorithm learns from itself. We let the system learn and make changes on the fits, things that we tag. We don't let the AI change the scoring algorithm. I'm sure you've seen some things about AI running ramp on a the source code and the backup source code for a company the other day was deleted in under a minute. So Will, our head of AI, is the only one that can actually agree to, hey, let's change the algorithm in this way, shape, or form. But the system will suggest things, and then we meet as a team to discuss them. If the score is high enough, then we reach out to them with what used to be mass customized emails that we could only really dictate on where you are, what industry you're in, uh maybe like where you went to school, a few other things. Now our AI-enabled emails, and we just got one back from a CO literally yesterday, it was perfect time for this podcast, saying he's like, hey, I delete almost all these emails, but yours struck a chord with me. So we took our eight years of A-B testing emails and then integrated further AI into them. And so we now tell a pretty compelling story about why we're the right person to be talking to them at this moment with what we bring to the table. And so our response rates are through the roof compared to anything else. And the whole reason for this, why we built this way, is these companies, since they haven't raised capital, they're like these founders' babies. They're very comp protective of them, and they need to invite us into their home to co-parent their baby. And the only way I know how to do that is to get there first or early. So on average, we get there a year to two years before we invest in a company, and that gives us time to build the relationship. So I'll pause there. I'll go, I can go into the other ones, but that's the that's CAPE sourcing. That's how we do the sourcing part.

SPEAKER_00 30:50

Yeah. Uh I appreciate all the depth and fidelity there. When you think about the investments that you've made and the return for you as an investment firm, are you able-I mean, you mentioned the response rates are much higher. So is the conversion of the kind of outbound the primary kind of benefit there? Are you able to do more outbound? How do you how do you think about sort of like, you know, where this translates into a kind of benefit for the organization?

SPEAKER_01 31:26

Yeah. The whole thesis of building it was to do more with fewer and source with a senior team. So we have now three junior professionals. It would have taken us 15 to 20 people in the old way of doing it. So we've got that. And then we find these companies first or early. Like one of our companies, you know, literally when we got on our first Zoom with him, he accused us of dumpster diving, going through his trash. He's like, How did you find us? Our website's in Mandarin. You know, you're a bald white guy. It doesn't look like you speak Mandarin. And I had to convince him we did not go through his trash, but we saw his business, Shi Kei Tong, at a restaurant tech convention. We put it in our algorithm and it scored a 94 and a half, which means it's really likely to be a fit. So the way it benefits us is we find more businesses earlier. Their first conversation or one of their first conversations is with a senior person. And so we stand out from the pack because they're not talking to a 22, 23-year-old. But the real thing, and I can I can give you the number. So it's basically on average, it takes us about 10 emails over 10 months for them to respond. Some have taken years, some have taken weeks. But we on average have 15 months then to build that trusted relationship, to show them how we we haven't got to CAPE operations, but our value-added playbook of how we add how we're able to take your maybe not ready for primetime, founder, owner, operated boot business to the next stage. And so we can show them that. They can talk to over months and quarters, companies we've worked with. We can help them make hires during that time. So on average, it's 15 months, but we've had some, the uh AI forward ad tech business, we talk to them for four years. And so it gives you that time to win the deal. And we're never going to be the only group talking to them. But I'll tell you, if you go to make a decision and there's groups you've been, there's somebody you've been talking to for two years and somebody you've been talking to for two months, you have a lot more trust. And since most of our deals are non-control, we're not buying control of the business, they care a lot more about the quality of the partner than the price. Um, and that is where we differentiate. So that's the biggest, you know, it's fine, getting there early, having the senior people to differentiate because they're not talking to junior, and then having the time to build the relationship.

SPEAKER_00 33:42

Let's talk about operations and how your value creation plan is uh influenced by AI and also ultimately how it drives a price down the road, which they do care about. Let's walk through um like CAPE operations and and what that does and how you built that too.

SPEAKER_01 34:02

Yeah, so we're building full display, we're building that out right now. It's not fully operational. We hired a woman, Jackie Atwood DePont, comes from an operating role. She's our currently her title is uh chief of staff, but she'll probably go into chief of operations. So she's working with Will on our engineer and you know AI person, along with the founders of the firm, to effectively codify our operating playbook. So, you know, we're only a 12-person shop. Right now we only have two partners, but what they are in the process of doing is taking everything that we've done to date with our portfolio companies and everything we're doing, and then eventually the things we we do in the future that we haven't done yet, and having, you know, white papers written about it. And it's basically not to maybe tip a hat too much to another group out there, but uh, you know, Vista Equity Partners, this is kind of how they, not necessarily AI enabled, but built their operating model, which is have all these white papers. So when do if you're the CEO of one of our companies and you have a question about how can I go down this route, not only can you are you talking to us, you can go to this repository of information and we can have it basically be automatically generated based on the data, the date, all the data in CAPE is coming into CAPE. And so we can literally say, Company XYZ, for this company, when we went through a pricing change three years ago, write that up into a white paper, and we have that now as the first building block for our companies. The second one is they're gonna want to talk to them, you know, the other CEOs. And so we're in the early stages of building that out. The other aspect of CAPE operations focused more on an AI is we have many of our companies, it's a it's kind of a lonely position to be the CEO. Everybody's foc everybody's experiencing AI risk and opportunity. And so we'll get a lot of the same questions from people. And so we're putting together basically a shared network of when people have these questions, they can come to our our for lack of a better term, you know, our our marketplace of ideas and talk to each other. And then we're bringing in experts that we've used to actually hold, you know, town halls on all right, what's how if you're focused on what your issue is and like your last guest was telling me, rebuilding, rebuilding the business model in AI, where do you start? How do you do that? We'll bring that to bear. And so it's this is a a s we're building that plane while we're flying it, so to speak, but it is uh it's already pretty powerful.

SPEAKER_00 36:38

Yeah. Let's talk about the portfolio and and AI. You have said that every CEO U back has to kind of fully understand AI as both an opportunity and a threat, regardless of their sector. What do what does that conversation sound like? And what are you trying to get the CAO to admit or commit to?

SPEAKER_01 37:01

Yeah, so maybe the best way I can explain it. We used to ask, and this is not for everybody, but the the first question we used to ask before AI, that was kind of our red flag, yellow flag, was, you know, what are you look, what are you looking for apart in a partner? And if they said, I've got this all figured out, I just want, I just want m your money, and I want you to, you know, get out of my hair, that's not how we partner, and so that was a kind of a runaway for us. Now we're asking them, hey, what's how are you, how do you currently use AI in your in your business, both in terms of how you create and how you're delivering the solution to your customers. And then what's your view going forward on really as simple as the opportunities and the threats? So what are you planning? What's on your your roadmap? And if we hear from them, there's can kind of be two answers that are, or two barbell answers that are awful. One is we've got this all figured. Like we are so far ahead. It's to my AI native, you know, we're an AI native business. We built this, nobody's gonna touch us. That is a red flag because that will continue to change. You need to continue to invest in it. You can't rest on your laurels. The other one is on the other end of the spectrum, AI is never gonna touch us at all, and this is why. And even those motes that I talked about before, our belief is it's not AI is never gonna touch it. It's just, hey, how do you situate your business? Like all of our companies, just to cut to the chase, are using AI. Some of them have developed their own AI on top of them having the moats that we discussed earlier. So we run away from the folks that think they've got it all figured out because they're so far advanced, and we run away from the people that are the ones saying it's never gonna come here because, you know, not in my backyard for X, Y, and Z reasons.

SPEAKER_00 38:49

Yeah. I'd love to walk through an example of how one of your companies has invested. And uh you shared a story with me about a prop tech portfolio company uh where AI changed the plan and then some other things happened. And you leveraged some technology that you built in a new way. Let's talk about that example. So where did it start?

SPEAKER_01 39:12

So it started your question about diligence, right? So and how we how we suss out, you know, where are they on the on the AI spectrum? So our third-party tech due diligence walked away from this business and we saw it, but they put a fine-toothed comb on it saying there is a portion of this business that has now it has about 11 million of revenue associated with it, but that if nothing changes, there is both an AI opportunity and an AI risk in that we had a group, we had groups that we partnered with outside of our organization, and because we partnered with them in fairly manual capacities, they split the revenue with us. So our take on that revenue was 11 million. Their take on it was 11 million. And they in this diligence, probably three years ago, it was the roadmap was placed out to us that, hey, if you can build your own AI, you could disintermediate those external third parties. And it's not quite this simple, but because you're disintermedium, instead of 50-50 split, you'll basically have 100. We're never going to disarmate them completely because you need some people. You can't just do it all with an AI bot. But let's say 95% we can. So we started about building our own. When we hired a new CTO, which is one of our usually value ads, we bring in kind of a stud or studette in the tech depart world. They took the third-party tech due diligence report, which is effectively a roadmap, and started building this out. And the CTO came to us about nine months in and said, Look, we're making a lot of progress. And he showed it to us in the board meeting. He said, But I think I can short circuit or truncate this if we go out and buy a specific AI native uh platform that has focused, been focused on prop tech, specifically multifamily, where this company was, and I can get there faster. And so what we did is we took our AI sourcing and said, okay, AI, this gets to my first comment about the first bucket. AI native businesses tend to trade for a higher multiple, so we knew it was expensive. So we wanted to basically find the smallest business we could find that had a great product but did not a lot of revenue because we were gonna be paying off a revenue multiple. And so we focused on sub-1 million dollar or about one million dollar ARR businesses, and we found a sub-1 million dollar ARR business. We bought it, and over the last nine months or so, we've been integrating it, almost fully integrated, and just had a board meeting last week, and we're gonna be able to disintermediate, already have about half of the third-party partners we've been working with, and we'll be able to get another probably 40 or 45 percent. And that acquisition alone is going to take that 11 million and almost double it. And that 11 million is about a third of our, you know, of our revenue. So you talk about it, it it could be single-handedly the difference between a low 30s of revenue and a low 40s of revenue, which is a big game changer in the world that we operate in.

SPEAKER_00 42:06

Yeah. I want to go back to how you're investing in AI at the fund level in in two things. One, uh, some other initiatives that you are focused on, like reporting, how AI is gonna influence that, and then and then kind of high-level thought on what the future of investing looks like in a like AI native world. The technology is not going to slow down. You're gonna be able to do more in the future than you can do uh today. Um, one is like where are the other areas that you're focused on today? And then let's talk a little bit of what the future looks like.

SPEAKER_01 42:44

Yeah. In the from uh so CAPE reporting is something we've built out where every so we spent quite a few weeks and months. We did it, we had the guinea pig, one of our companies, and we said, what do we want, what do we need and want from our companies in terms of data to produce all the reporting that one we need on a monthly basis, and then take it to not to get too boring, but when we raise funds, people ask us to do these DDQs, due diligence questionnaires about our companies, and they're very helpful for the LP, and they're important for us to do, but they're a big pain. Like they take forever. And so the goal is to get all this information into the CAPE system and automate the production of the financials, which we've already Will has already done, and then eventually automate most of the production of these DDQs, which is in process. Um, we're not fundraising right now, so you know, we haven't really given that a full shot yet. But so on the CAPE reporting from our companies, it is set up now that with APIs, they don't have to lift a finger, these come our portfolio companies. Their information comes directly into our data lake house uh in CAPE. Will system does all the fuzzy matching and putting it together and uh what's the word I'm looking for? Structuring it correctly, uh forming it. Correctly and it spits out exactly what we need. And so it is a huge win if you have if you talk to a lot of private equity backed businesses. One of their biggest frustrations is everybody wants things reported differently and it can change and whatnot. And we basically have it set up so it's you know incredibly user-friendly. They don't have to do anything. So that's been a big win. In terms of how things you know changed going forward, you know, I think we're in an interesting time. It's hard. I'm sure you saw, you know, Microsoft came out, I think it was last week, saying that their data shows that it's more expensive to use AI than hire people. And so that's one thing. Uh I know Uber CEO got a lot of press for come out and saying, you know, they blew through, I think your last guest had it, blew through their budget, their AI budget in four months. So I think it's hard to say. I think everybody's kind of gone all in and saying, oh my gosh, this is gonna change everything. I do think it'll change everything in part, but I can't sit here today and say, you know, it's going to go as drastic as everybody thinks. I think we will, and the market will go for going forward, be investing in, and this is not a hot take by any stretch, but more AI native and AI forward businesses because there's gonna be more out there. I think you will see, and I think that will cause valuations for those to go to continue to go up. So I think they'll get pretty spendy. I think you will see the motes that we focus on, and there'll be moats added to those that will you'll have more competition there. And I think because of that, you'll see multiples go up, uh, which will be a challenging thing again when we start like how how sure can you be that the those motes are defensible? And if you pay up for one of those and it ends up there is a chink in the armor, that can be a a problem. And then where I also see kind of the investing space going is we are going to have a sorting or kind of a come to reckoning of firms that how much they did see this coming, how far advanced they were on it, and how quick they are to react. So the folks that are not using, and there's a lot of company firms in the middle market, lower middle market that you know aren't using AI at all, aren't really deploying it at their companies, I think you're going to see, I don't think private equity in general is dead. I don't think growth equity is dead. I don't think investing in software is is dead. But there are going to be a lot of comp firms out there that have busin have investments they made in 2020, 2021, maybe even later, where multiples were very high, businesses that weren't as defensible, that those multiples will go down. So they will have either poor or maybe not at realizations at all. Maybe it'll be donuts, zeros, you know, on their investments. And that will separate the wheat from the chaff in terms of these firms, and it'll be a lot easier for firms that perform to raise, and it'll be, I think, next to impossible for firms that don't to raise. And then I think that will, not to get too far ahead, will lead to new entrants, uh, people a lot younger than than us that are, you know, probably even more AI forward, AAI native, and thinking about this. And so I think I would venture to guess that if you took a snapshot and you see this in the public markets of the leaders as of 2026 in various parts of private equity, I think 20 years from now, you will see a very different group of firms leading. Yeah.

SPEAKER_00 47:22

If if a peer listened to this and calls you next week and said, I want to be the wheat, not the chef. Uh, I want to do cape at my firm. Where do I start? What would you tell them? I mean, if it's a competitor. Well, no. Yeah. If it's a comp Don't start.

SPEAKER_01 47:40

Yeah. If it's a competitor, you know, we we won't. We've had ironically, before this whole AI, you know, explosion, we had people ask us five, six years ago if we should, we hadn't raised our fund yet, if we should just not raise a fund and and sell what we're doing to firms. Where we will help with folks is if it's if it's not competitive, you know, we'll give them kind of uh, I'll get on the phone with them or a Zoom, uh, I'll bring Will in, and you know, we will walk them through some of the stuff that we've done to help them. And and then the nice thing that we've got, and I mentioned this a little bit earlier, is that we've been doing this for eight years. And so our inform we've already learned from all this. Even if we did hand over almost everything to our competitors, which we wouldn't, it would still take them years to get to where we are. So the short answer to your question is if it's a direct competitor, we'd probably, you know, be nice about it. Uh we like to play nice. We named our firm intentionally with integrity. We're not gonna be jerks about it, but we probably wouldn't really help. Um if it's not a competitor, maybe like my old firm in the buyout world, I would absolutely we've had LPs ask us to help. We've done that. We've had, you know, we help with our portfolio companies because if you think about what we're doing, it's really generation. And lead generation on the source on CAPE sourcing applies to almost all businesses. And so it it's a case by case, it depends who you are, how competitive you are, uh how much will we will help.

SPEAKER_00 49:11

Yeah. Anything you would tell them not to do? Let's say this is a friend. A friend is asking you, uh, what would you tell them not to do? Like learnings that you've had where reps and time and money was spent and it turned out not to make a lot of sense.

SPEAKER_01 49:28

I think so we've we've benefited, I think, because like I said, we had the first generation pre-I and we brought Will in. So we didn't spend a lot of time, money, reps that that didn't work. But what I'm hearing, what I'm seeing, this is where I would give them advice is I get almost every day, actually probably every day, somebody reaching out saying, we've got the next whiz bang sourcing AI-based system for you, you know, to sign up. This is our business. I've got the next AI-based way to help your companies with operations. I think the danger there, and this is, I think in general, people think about like, okay, well, I'm just gonna take this AI wrapper and slap it on top of what I'm doing. And I think you'll see a lot of money spent that doesn't get what you want. The old analogy I used to give is before AI, but if you took a an RD, like a tech business, the board said, This is what I want you to build. And they would give it to their star engineers, and the engineers would build a Ferrari that, you know, only maybe an F1 racer could drive. But that's not what the people that are operating need. They needed maybe not a Yugo, but they needed like a Ford, you know, Ford focus. And so I think the challenge will be that these kind of one this the easy what allures is alluring about these easy fixes is, yep, I can say to my LPs, we're doing this, I've got this piece of software, we're dealing with sourcing from an AI perspective, we're dealing with ops from an AI perspective, but it actually isn't uh what's the word I'm looking for focused enough on your business. It's a little bit like Salesforce used. Salesforce is can be a great tool, but you need to spend tens of thousands of dollars to curate Salesforce to work for your business. And I think the same will be true about these kind of you know, AI wrappers and whatever it is, if it's sourcing operations, what have you, that yeah, you can't just start there. You need to do a lot more, and it's gonna take time to build it. And so I would just be cautious of I don't think there's the quick fix out there. If you're going to go, you know, all in on this, I think you need to spend, you know, for us, we've spent, you know, millions of dollars on it over time. You need to be willing to say, okay, I'm gonna spend a lot of money. It's gonna take a decent amount of time, a little bit like building a house, and it's gonna, and it may take longer to to show up and get working. We have had some issues, like, you know, one of the things that's come up is, and this is a little bit of a zig, but as AI has enabled more emails to be sent out, a lot of emails aren't getting delivered. I think it was uh, gosh, who came out of one of the leading tech companies the other day, so their emails weren't getting delivered. We've got Will leading that for us, and we have multiple pieces of third-party software we've integrated into our system to now monitor exactly what's going to eat to people's spam inbox. We've got indicators to show what we need to do to continue to maximize the health of our of our domain name. And so there's just there's these unintended consequences, I guess I would say, and I'd be I'd be worried about the the quick fix for anybody.

SPEAKER_00 52:46

Yeah, I think the quick fix is like attractive because it's quick and it sounds like a fix. But the problem is that it's often not a solution to your problem. Yes. It's just a solution. And so you really need to approach these things, whether it's AI or not, with a sort of problem solution hat on and uh and understand maybe you need to buy something, maybe you need to build something. If you're gonna build something like who are you building it for? Yes. And you know, how is it gonna improve what they do, whether it's got AI in it or not? And it's very easy, you know, when it's noisy like it is now to forget that. Yeah.

SPEAKER_01 53:22

And I think you see with Plurus, you know, the the work you do with a lot of different companies. I mean, you can't just slap what you do for company client 102 with client 404. Like it's not the same.

SPEAKER_00 53:33

No, no, absolutely not. Uh every situation is unique. I I'm a big believer that the sort of problem set is heterogeneous and so is the solution set. And so you need to be thoughtful about that. Uh as we come to a close, Doyle, I really appreciate the time. Any last perspectives or thoughts or recommendations that you want to leave our audience with?

SPEAKER_01 53:52

Well, at first I just thank you for for having me. Uh I've enjoyed, you know, the discussion and and being able to share a little bit about what we've done here. Yeah, I guess, you know, on the I would just wrap up with what I started with, maybe that they that I I do think it's way too simplistic and the peanut butter approach of spreading evenly that, you know, SAS is dead because of AI. And then from the selfish standpoint, you know, for listeners out there that are either, you know, potential investors or know people that are, you know, we get this feedback from some of our investors that some of whom are the leaders in the you know the biggest names out there, like a stepstone, for example, that what we're doing, they're not seeing other people do. So if you're if you're potentially looking to invest, we're not raising now, so I can say this, but next year we will be raising and we'd love to uh love to love to talk to folks. And uh we also think we're the last one I'd say is we're you know, we're not perfect for everyone, but we are a really great partner in in helping these businesses grow from you know 10 to 40, 50 million. We've done it dozens of times. And so if you know people, if your listeners know companies that are kind of looking to take that next step and take their first institutional capital, I'm biased, but we're a pretty darn good, good partner.

SPEAKER_00 55:01

Amazing. Doyle, thank you for coming on, just curious. Take care.