AI: The Secret Weapon for Private Equity Valuations

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Hey, welcome to another episode of Making Billions, I'm your host, Ryan Miller and today I have my dear friend Chris Taylor. 

Chris is the co-founder and CEO at Fractional AI, an AI service provider grounded in engineering excellence to support private equity fund managers to increase the value of companies they've invested in. As the CEO of fractional AI, Chris has scoped hundreds of applied AI projects, from complex workflow automations to new product launches. 

So what does this mean? Well, it means that Chris and his team at Fractional AI are leading the charge for fortune 250 companies to increase valuations, reduce operational complexities and improve returns for shareholders when they succeed.

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[THE GUEST]: Chris Taylor is the co-founder and CEO at Fractional AI.

[THE HOST]:  Ryan Miller is an Angel investor, former VP of Finance, CFO of an insurance company, and the founder of Fund Raise Capital,  https://www.fundraisecapital.co where his strategies helped emerging fund

Transcript

Ryan Miller  

My name is Ryan Miller, and for the past 15 years, I've helped hundreds of people to raise millions of dollars for their funds and for their startups. If you're serious about raising money, launching your business or taking your life to the next level, this show will give you the answers so that you too can enjoy your pursuit of Making Billions. Let's get into it. 

Ryan Miller  

What if I told you there's a technology that could instantly transform how private equity firms make billions, a secret weapon that most investors don't even know exists yet. Imagine cutting due diligence time by 80% generating leads faster than your competitors, or automating entire workflows that used to take teams months to complete, now done in seconds. Today, I'm sitting down with the one person who's actually doing this, and he's not just talking about it. He's a guy that fortune 250 companies call when they want to unlock hidden value, and he's about to reveal exactly how. So if you're an investor, entrepreneur, private equity manager, or just curious how AI is really changing business, you can't afford to miss what's coming next. Making Billions starts right now. Here we go. 

Ryan Miller  

Hey, welcome to another episode of Making Billions, I'm your host, Ryan Miller and today I have my dear friend Chris Taylor. Chris is the co-founder and CEO at Fractional AI, an AI service provider grounded in engineering excellence to support private equity fund managers to increase the value of companies they've invested in. As the CEO of Fractional AI, Chris has scoped hundreds of applied AI projects, from complex workflow automations to new product launches. So what does this mean? Well, it means that Chris and his team at Fractional AI are leading the charge for fortune 250 companies to increase valuations, reduce operational complexities and improve returns for shareholders when they succeed. So, Chris, welcome to the show man.

Chris Taylor  

Thanks for having me. Big fan of the pod, excited to be here in chat about AI today. 

Ryan Miller  

Yeah, thank you man, we've been very fortunate to be in the top 2 or 3% in the world, and it's all because of amazing guests like you brother. So let's jump right into it, man. So what is it about Fractional AI that makes private equity fund managers want to work with you guys?

Chris Taylor  

Yeah. I mean, I think a big part of it is that these fund managers realize that there's a tremendous amount of value to be created by this new technology. I mean, I don't think that's any secret, I don't think that's controversial at this point, but most of them are unclear exactly how to make that happen. And so there's, there's all this promise of the technology, but they're looking across their portfolio and they're thinking, I think we're we're not quite getting the value out of this that I was hoping for maybe a year ago, and and looking for ways to really accelerate the adoption of this technology across their portfolio, and especially for those companies that have tons of automation potential. How do I get them to build that automation quickly and dry value creation in the portfolio. And on the investing side, I think the big question for a lot of these new investments they're making is, okay, there's this disruptive technology out there right now, and that's going to change the landscape of industries over the course of the next decade. What's going to happen to this company that otherwise looks like a good company, and pre chatGPT, I would have bought this. No question, do I still buy it? What does this technology mean for this this industry that I'm looking at, this new company that I'm looking at? And so having a trusted partner who really understands this technology and has seen a lot of the use cases up close put things into production driven value is a really helpful, helpful partner to work with on both the investing side and the value creation side in the portfolio. 

Ryan Miller  

Yeah, and I imagine most funds lack engineering talent and all that to just do it because they're, I mean, their expertise is in doing deals and many other things. And so is that a pretty good entry point for you guys, as well as to say, hey, we can be that for you, we can help not just with the fund but your portfolio companies, as you mentioned before, is that kind of where you come in. And I imagine, as you automate and do a lot of these AI implementations in a lot of companies, then eventually valuation goes up, which is what we're all here to do. Would you say that's a fair assessment?

Chris Taylor  

100%, I mean, I think the building with this technology is really hard to do. You know, you're trying to wrangle this magic, hallucinating ingredient into a reliable production grade system. And if you're putting that workflow automation out there into the world and putting or putting a new product feature in front of customers, you need it to perform reliably and and to get reliable production grade systems. Today, it takes a top caliber engineer that really knows what they're doing and typically, in the world of private equity, engineering talent isn't usually synonymous with PE owns companies, it's more of a VC thing. And so the for the PE firms that we're working with today, you know, I think there's a, there's a nice, a nice pairing there of the engineering talent that we bring to the table and all of the the things that they're great at on the PE side.

Ryan Miller  

Brilliant. So what are some of those, say, the common issues that a lot of fund managers and CEOs are struggling with with AI right now? What are you seeing?

Chris Taylor  

So I think the most common thing we're seeing when we're talking to the portcos is, typically, if it's a CEO or CTO, they have a number of ideas for what they want to do with this technology. But they've, they've never really seen a project go all the way through to production. And so they have an inkling of, okay, I think this workflow that's happening here is a good candidate for automation, but I'm not totally sure if it is, and I also don't know how long it's going to take to build that. I might not have the resources to build that and so they have, they have a few ideas, but they're, they haven't really taken the leap of prioritizing them, because they don't know exactly how much, how many resources is going to require. They're also sitting on an entire roadmap of things that have nothing to do with Gen AI. You know, they had a they had an existing business with a million priorities before this new technology showed up, and now they have 1,000,001 and and so oftentimes the this this one, because it's the most it has the most unknowns that kind of sits there in idea land for too long. And so most of the time when we're when we're having conversations with those folks, the first conversation looks like here are the three ideas that I've been bouncing around with my leadership team, and I think they're good ideas, but I'm looking for validation on that. I'm looking for somebody who can pattern match to a bunch of projects that they've done, and tell me, this is feasible, this isn't feasible. This is going to take X months and Y, Y resources to get it done and and so typically, the early conversation, those are the types of conversations that you have, and for the right company, you can pretty quickly narrow in on what is that early win that you can get and get something into production within about three months, usually, that is driving real value. And get that getting that quick win into production is, is, is great for a lot of reasons, one of which is, just now, everyone at the company has seen one of these things all the way through. And having that learning experience makes the next, you know, makes the rest of the AI opportunity so much easier to understand. 

Ryan Miller  

Yeah, okay and I imagine a big one that we think about on my side of the thing, of the aisle, and a lot of people that I talk to, labor costs are big ones we look at you get a lot of agents and bots and all these different things that are coming and continuing to get better. How are you seeing AI affect, say, labor budgets right now?

Chris Taylor  

Yeah, I think there's two patterns that I'm seeing a lot of, one is automation of BPOs. So to the extent you have, you know, offshore teams doing highly repetitive, manual workflows for you and and to the extent those things are large cost centers, there's typically pretty good automation opportunities there. So we recently did a project where we automated a process that a BPO was doing end to end and for private equity, that means growth and EBITDA, and that's a good thing for everybody. And, and then on the US labor side of things, what we're seeing today, typically, it's the small teams that are stretched very thin, and they're doing these repetitive tasks that are eating up their bandwidth, and they're not able to get to higher order things on their on their to do list. And so you have, you know, small back office team in charge of in charge of something, and they have some other stuff that they really want to do, and you automate the thing that is eating up their bandwidth and free them up to do higher order tasks and so on. For US labor that's, that's the dominant thing we've seen.

Ryan Miller  

Okay, and, you know, a big one that we see on, in the finance side is due diligence. I don't know if you've ever had the say, the privilege of going through a data room, but can be quite taxing. Are you seeing anything pop up through due diligence and data room automation, anything like that? 

Chris Taylor  

Yes, absolutely. I mean, I think the the way I conceptually think about  LLMs, I think the one of the most helpful framings that that you can have for this technology is LLMs give computers the ability to read, think and write. And when you think about what happens in a clean room, it's people reading through tons and tons of documentation, 1000s of documents, and they're trying to answer very basic questions on a checklist. They're trying to pull out the right piece of information from these things, and all of that is reading, thinking, writing and tasks that LLMS are well suited for. So at the moment, we're working with DataCite, which is a leading BDR, and working with them to infuse AI into their product suite. And I think it's, it's an area I think the diligence process is going to change a lot with with the advent of AI.

Ryan Miller  

Yeah and you know, there's one that I've been really dying to ask you, and it's, it's a big deal for both high finance, so we'll say venture capital, private equity, for sure. So those business owning ones less so for say, a hedge fund, as well as portcos, these portfolio companies. So both need something that we call leads right leads for investors, leads for good deals, and then on the portco side, leads for customers who want to buy. So AI has this unique opportunity to really help with lead generation. Are you seeing anything like that or what's going on there? 

Chris Taylor  

Yeah, on the lead side, there's a, there's a few ways that I've seen it. I mean, I've been on the receiving end of all of the AI SDR email outbound that's picking up. So every day, I wake up to 10 plus, you know, 10 plus cold emails trying to pitch me. it's typically trying to pitch me cold emails. It's these companies that are dogfooding their own AI SDR software, but, but another way that this is surfaced is in processing of leads. And so for anything RFP related, this technology is amazing. And so seen, have seen that come up a number of times, but you know, if you have a high volume of leads going to your sales team, and a lot of what you're doing is qualifying or taking information out of those and processing that, and building a building a proposal based on information and RFP and all of that. That type of, those types of tasks that can eat up sales people's time are very well suited to LLMs.

Ryan Miller  

Brilliant, wow. So lead generation, due diligence, those are exciting. Those, those those really get me excited, I'm like, if you could shave off some time on both of those, I'm all in. So what are some examples? So you've done a lot like fortune 250 companies, that's really good, you got a great clientele. You built a huge reputation for yourself in such a brief amount of time, why, because you get results. So speaking of results, what would be what are some, I don't know if you have case studies or anything. But what are some, some clients that maybe we've heard of, that you've worked on, and how did that work? Just to give us a sense of, really, the impact of AI in either portcos or in private equity funds.

Chris Taylor  

Yeah, so, I mean, I can speak quickly to some of the work we're doing with a fortune 250, it's a legacy software company been around a very long time, and you can, you can imagine this company has done a lot of acquisitions over the years, so their systems underneath the hood are very disconnected. You know, they have a lot of database fragmentation that they're dealing with and when, when their customers are using their software and they're trying to pull information out. Oftentimes, that information lives in a lot of different systems, and it's it makes that a challenging experience for their customers and so you can imagine kind of two approaches you could take to this problem. One is a very, very lengthy process of integrating all of this into one database, and another is actually using Gen AI to look across all of those fragmented databases and get the information back to the customer and a new Gen AI powered experience. And so that's, that's one thing that we're building right now. And so it's a, I think, a dramatically improved customer experience when, where it's going to be when we're done with that project. 

Chris Taylor  

Some of the other other examples we have on our case studies page, we did some work with Zapier. So Zapier, great company, underneath the hood, you know, they do a lot of work for building integrations with tools. You know, a lot of what they're doing is it's automation, plumbing, and integrating with new tools makes it more powerful. And so their team was working on actually using AI to build integrations to make that work go faster. And for them, one of the challenges that they were, they were having in the early days, was dealing with hallucinations. So if you've you've heard of AI, you've probably heard of hallucinations, if you if you haven't, you know, it's basically when the LLM makes something up and says something that's that's inaccurate and and so in this case, what was that, you know, when you're building a new integration, the process is, you're reading through third party API documentation, and then, based on the information in that documentation, you're writing some code, in this case, and it has to be very exact. You have to take the information out and exactly copy it over and do it the right way. And if you're making things up in that code, it's not going to work properly. And so for them, when we started working on that project, the task that they gave us was, hey, the hallucination rate here is 26%. So 26% of the time that we're generating code for a new integration, there's, there's a hallucination in here somewhere, how do we bring that down? And actually, when they, when they brought us task, it was, it was less, less defined than that, it was this thing has hallucinations in it sometimes, like fix it. And so you the test number one is setting up the evals to tell you that it's okay having a 26% of the time now I can measure it and understand where it's happening, and typically where it's happening. It's, you know, LLMs don't make things up when the, they don't mess up the information usually. Like, if, if the information is there, they're pretty good at copying it, what they're really bad at is, if there isn't any information there, if you tell it, hey, we'll go look up X and X is just missing from the documentation. It wants to please you, so it wants to give you X and so it just makes up something and just put something in there. And so, you know, task number one for us was, okay, we got to put the metrics in place to measure this and now we understand it's happening 26% of the time. You know, use an LLM as judge and set up emails, and then kind of go through and figure out how to get that lower and we were able to get that hallucination rate below 1%. And that's what a lot a lot of these projects look like, under the hood is putting metrics in place to understand the accuracy these systems, and then using those metrics to guide your progress and your development, to bring those bring those metrics to where they need to be, so that you can get comfortable with the reliability of the system and ultimately put it in production.

Ryan Miller  

Man, they must love you to take it from 26 to less than 1% I mean, that's that's unheard of. I don't know much about AI, but I know that is very, very good number, so good for you on that. What other things have you done as some other projects with companies that we may have heard of or may not, just to illustrate the power of the value that can be created for fund managers? 

Chris Taylor  

Yeah. Yeah, I another project that we are we're still working on, but it is live in production right now and generating value and it's a very cool one, it's a voice agent that we built. So this voice agent, we built it for a consulting company. And what this consulting company does is they interview employees at their clients to find opportunities for those employees to be more productive and, and so you can imagine, for this, this consulting firm conducting those interviews is their biggest cost. It's their biggest bottleneck for growth, because it's super time intensive. And this firm is very small, you know, it's a startup, they have 10 or fewer employees and, and so they can't really if, when they, you know, have a fortune 100 company that they want to work with, and they want to go conduct hundreds of interviews, they don't have the staff to do that, and they don't really want to hire it. And so what we built for them was a voice agent that conducts those interviews the way a human would, and, and that voice agent is live. You know, they deployed it, they deployed it recently at a fortune 100 it conducted 150 interviews in eight days, and it did it at a cost of about $500 and that's just something that was, one, would have cost them way more, and two, like wasn't even feasible before this voice agent. And I think that, I think this technology of using voice to conduct interviews is a really interesting new thing that exists that I think is going to change the way that kind of consumer insights and things are going to going to happen, because you can get way more, way richer information from an interview than you can from a static survey, and now you can suddenly conduct interviews at the scale that previously you could only do static surveys. And so I think it's a really, fun new technology that exists today

Ryan Miller  

Awesome, man. Well, before we wrap things up, is there anything else, final remarks, anything at all that you can share with our fans around the world? 

Chris Taylor  

Yeah, I mean, I think that if you're in private equity, and I think most of the community is here, I think this technology has a ton of promise for you. I really do believe that one of the biggest winners of this entire moment of Gen AI is going to be private equity firms. Both because there is, there's so much opportunity in their existing portfolios, and second, because I think they can get really good at buying businesses that have huge Gen AI potential. That type of strategy of buying a business that has distribution, that has process know how and domain knowledge, and the one ingredient that they're missing is maybe the technical ability to build some of these, some of these workflow automations that will unlock big, big gains for them. But I think private equity is uniquely positioned to find those companies, buy them and benefit from this technology trend over the next decade. So very excited for the opportunity out there for PE firms. And, and if anyone in your community wants to reach out and chat about about the opportunities in their portfolio or in their investing strategies, I'm more than happy to do so go to fractional.ai and just hit the request a console button or the button at the top right, and I will, I'll be in touch to have a conversation.

Ryan Miller  

I love it. Well, I'll be one of those people giving you a call, and we're going to talk about a few of the deals I'm working on as well. So just to summarize everything that Chris and I spoke about, look to fix your due diligence process and data rooms, leveraging AI look for some of those solutions are starting to pop up. The other area that Chris is starting to see is lead gen. So look for improving your lead gen with AI, and then also just focus on optimizing your labor budgets with AI as well. You do these things, and you too will be well on your way in your pursuit of Making Billions.

Ryan Miller  

Wow, what a show, I hope you enjoyed this episode as much as I did. Now, if you haven't done so already, be sure to leave a comment and review on new ideas and guests you want me to bring on for future episodes. Plus, why don't you head over to YouTube and see extra takes while you get to know our guests even better. And make sure to come back for our next episode, where we dive even deeper into the people, the process and the perspectives of both investors and founders. Until then, my friends, stay hungry, focus on your goals and keep grinding towards your dream of Making Billions.