00:32:17 · Mikk
[00:32:17] S0: We have a lot of folks listening and obviously the the previous guests as well. They just... They buy a lot of businesses. And the very obvious problem in in most every SMB and what private equity and overall business buyers have spent decades to to reduce is the key man risk. And now listening from your end that you can just put all this data into those... Into AI and educate this AI agent, and what you do pretty much is get all this knowledge and experience from the from the business owner's head and put it into AI. It... I can see it makes a big difference. Obviously, you you you can't replace the in person relationships, which someone has built over over decades. But when it comes to business multiples and the overall key man risk, maybe you can talk what you have seen there.
00:33:13 · David Flickinger
[00:33:13] S1: I think it's it's a great point. One of the things, of course, you're right. You're not gonna totally eliminate a key man risk in a business. Of course, these human to human relationships are so critical as I... As I've said. But I think when you can distill what's in that owner's mind over time into this memory layer such that you better understand how they make decisions, how they evaluate risks in the business, how they see opportunities, you can start to value and think about that business differently. And I think a lot of the discount that a buyer gives to a business is just a lack of, I don't understand what drives risk or decision making. I don't understand what drives success. That guy does. So I need him. Not only because he has the relationships, but he or she understands what is gonna be a successful project or job or whatever in this business. And I'm not sure I understand that for this particular business. Maybe understand it in a category, but for this little business and this little market, you're not sure you understand it, so you apply a discount. And I very much believe that a well deployed AI architecture can distill and extract that knowledge into a layer that a buyer can look at, can interact with, can use their own AI agent as they do diligence to connect into that layer and really start to understand the nuances and the specifics of that individual business. Like people, all these businesses are individuals, and they're different. And they have their own nuances, and they have their own risks. And so there's often not a one size fits all approach, but to the extent you can more deeply understand what makes this business successful, what makes it risky, what makes it a tremendous opportunity. If you could only do x, y, or z, you can more fairly value that business. And that's a win for the buyer and the seller. And so I think the opportunity to deploy these architectures, particularly in the lower middle market where you see these massive discounts on purchase prices, I think benefits both both parties because you're getting a better deal. The buyer more deeply and more confidently understands what they're buying, and the seller has set up their business in such a way that it's more likely to succeed in the hands of another buyer, of another owner. And I think that part maybe is lost a little bit too.
00:35:48 · David Flickinger, Mikk
It's not just, well, how do I get an extra turn on the sale of my business? I wanna do this. Typically, you have some maybe rolled equity as an example, and you wanna make sure that you maximize that. And so you care deeply as well as a seller about making your business more valuable in the hands of another owner. And so why wouldn't you want an architecture that gives you more predictability with respect to how to run your business? And I I think that's where a lot of our customers and requests are really focused, especially these individually owned businesses is I wanna make sure I get the most value for my business when I sell it, but I also wanna make sure that it's really successful in the hands of another owner. [00:36:36] S0: Let's say someone is listening, thinking of selling maybe one of their portfolio companies within the next twelve to twenty four months, and what they hear is like, okay. It makes a total sense. But where do I start? What knowledge or data should I be putting into AI to make sure in twelve to twenty four months, my business would be a bit more valuable, for example?
00:37:00 · David Flickinger
[00:37:00] S1: Well, I can't believe that people are just using an off the shelf tool and calling it AI in their business. So if you're doing that, great. It's better than doing nothing. But that certainly doesn't provide any real value enhancement to your business. What I think folks like that ought to be thinking about is how do I have a real AI architecture in my business? In other words, how do I have my own memory layer, not somebody else's memory layer, somebody else's tool that I'm just using as if it were a SaaS subscription? How do I have my own memory layer? How do I have my own architecture? How do I have an AI agent that connects in to all the systems that run my business such that when I ask it a question or in eighteen months when a potential buyer asks it a question, it has all the relevant context, answers, and with a high degree of confidence can produce an output or work product that's very closely aligned with what my business does, that's very specific to my business and is not some generic answer. That, I think, makes a business more valuable subscribing to an AI product or maybe even building a little homegrown SaaS product. Sure. May... Maybe there's something to that. It's better than nothing, but I think having a real agentic architecture and you start to hear this buzzword more and more about agentic AI, that's really what agentic AI is, is having your systems connected to sub agents that all connect up to a main orchestration point that you or your employees interact with that can give you answers, create work product, and provide insights about your business that are real, relevant, and context rich based on real things that your business has.
00:39:01 · Mikk
[00:39:01] S0: It's all great. Business is risky. Entrepreneurship is risky. Using AI seems like a no brainer. What are some of the downsides or risks when when when doing and implementing all this?
00:39:16 · David Flickinger
[00:39:16] S1: The the first one that comes to mind is cost. So so cost is a huge one. And for a small subset of business, it's data security. I think data security is is, one that's a risk for everyone. But, of course, on the spectrum of lower middle market companies, maybe someone who's doing $5,000,000 a year is less concerned with their customer data being sent over an API to OpenAI than a business that does $500,000,000 a year. That business, no matter what industry it is, might actually be concerned with customer queries that are unsanitized going across to Gemini, Claude, Anthropic, all in pursuit of... Well, hopefully, those companies now soon to be public have good security policies themselves, but maybe you're making those businesses smarter or you're at very least sending customer information that your customer thought was proprietary out into the world. So there is a data security layer, and we deal a lot with health care businesses. My background is in health care. So we've thought a lot about that. We essentially have this anonymizer or sanitizer that we can plug into this architecture that will remove either patient information or sensitive customer information when you send an API request. So data security is a big one for the right sized businesses. I'm not saying everybody needs a sanitizer on top of their agentic architecture, but everybody should be concerned with cost. And I think in the news, there's been several articles now. Microsoft canceling a bunch of Claude code subscriptions was probably the most notable one recently where I think in the absence of this context layer I described, these agents are using the most powerful models to just bludgeon through, and it's very inefficient, and it's very, very expensive. And so I think one needs to think about how you deploy AI with the right model for the right task. And I'll give you an example. So let's take this roofing estimator as an example. You have this AI orchestrator. Maybe you wanna use Mythos or Opus 4.8 or GPT 5.5, whatever the latest model is that you wanna use. You're really smart model as your brain. But if you're reading PDF specs that just need summarization, well, maybe I don't need the latest and greatest for that. Maybe an open source model could do that. Most of these open source models are as good as the frontier models were six months ago, so they're really good.