Ep76 | Building Exit-Worthy Growth Engines with AI

Welcome to the Exit Engine Podcast, episode 76. Welcome to a special episode of the Exit Engine Podcast where we help founders, owners, and entrepreneurs turn their businesses into assets buyers fight for. If you're looking to grow smarter, reduce risk, and ultimately exit stronger, then this is the show for you. I'm your host, Joe Stapleton, and with me as always is my partner in value and salability, Mr. John Kniff. Good morning, John. Actually, I should say good afternoon, John. It's so special we're doing this in the afternoon.

Maybe you're in the morning. I'm in the afternoon, Joe.

Okay.

Good afternoon to you.

Exactly, John. This is a very special episode today because it's a little bit outside of our normal scope. And yep. you know, obviously we're, we're still very focused on, on giving founders information they need to be able to exit, you know, build a company and grow it to exit on their own terms. Um, but, but there's obviously been— there, there's one topic that's kind of ubiquitous, right, about— that's, that kind of cuts through every single topic and, and, uh, every single business, uh, probably discipline. It also obviously has a huge effect potentially on where some of it potentially where companies will go in the future and what they will be able to do in the future, how they will exit in the future, and the whole thing. And of course, I'm talking about AI.

Artificial intelligence.

That's the— that is the one. So if you're a founder, an operator, you already know that most of your biggest decisions about customers, hiring, product, capital, or even exit timing are made with incomplete information, right? We are all out here kind of guessing, hoping, Thinking we know what will happen in the future. And John, we know we've talked about this so many times in terms of how we discuss mindset in exit, that we know that our brains are terrible at doing one particular thing, and that is predicting the future.

Yeah, predicting the future. And AI is wonderful at— I use it each and every day, and it's sort of an incredible tool. And many people have been using it for a long time, but But it's amazing what is being done and then will be done with AI in the very near future. So it's an incredible tool.

That's it. And of course, when you are in a situation where you're trying to predict the future, right, what you're doing is you're betting— essentially, you're betting on behavior, okay? And you're betting on the outcomes, the predicted outcomes. So today's episode— and how does AI come into that? Well, today's episode is about how we might be changing that, or at least how one company might be changing that using AI. not just to analyze what already happened, but also now to predict what's likely to happen next. And so we're gonna dive into the world of behavioral AI and what it means for go-to-market strategy, enterprise value, founder confidence, right? All of it, right? And how we're gonna potentially even how it might affect you in terms of the lead-up to exit and how you're with exit planning, okay? So, but first, John.

Yes.

Let's talk about the new ExitEngine ecosystem. We've just launched what we think is gonna be a very valuable and important platform for those of you who care enough about your business and your own futures that you wanna learn grow towards successful exit.

It's everything and everyone you'll need to plan and execute a successful exit on your own terms and timeline. ExitEngine helps founders grow smarter and exit stronger by providing diagnostics, roadmaps, coaching community, and advisory support that reduce risk, increase value, and make their businesses buyer-ready. It is the first exit planning ecosystem in the world, and you can start for free. Right now, just go to www.myexitengine.com.

So, John, we're clearly and suddenly perhaps living in a world where timing, traction, and team dynamics make or break acquisitions. Having the ability to see behavioral patterns at scale could be a massive differentiator. And, and of course, when you're prepping for a capital raise, if you're rethinking about go-to-market strategy because you have to grow top-line revenue, if you're preparing your company for sale, I think this episode is now going to be— this is your front row seat to how next-gen intelligence tools are being built and how they'll impact your journey. So our guest today is the brain behind NextLM, a behavioral AI platform designed to help companies predict customer actions, team performance, and business outcomes before they happen. Chris Anzalone is the founder and CEO of NextLM, a visionary entrepreneur building AI systems that combine behavioral modeling with real-world business logic. With deep expertise in AI, machine learning, and predictive analytics, Chris is at the forefront of what's next in applied intelligence for growth-stage companies. He's helping companies scale smarter, sell better, and lead with confidence in a market full of unknowns. Chris, welcome to the Exit Engine Podcast.

Awesome. I appreciate it. Great to be here.

Excellent.

Chris, you have been around the block. Okay? This is not your first rodeo. This is not even your first foray really into AI, right? You are, and by the way, you are also obviously a founder, but an exited founder. Tell us a little bit about your background, where you kind of came from and how you got into and what kind of led you to NextLM.

Yeah, so I guess I had a bit atypical of a founder background, or maybe it's more typical. I spent about a decade in finance, started as a trader after business school, wasn't really for me. I, you know, it was fun, but I couldn't see myself doing it my entire career. And I ended up in capital formation. So most of my career entailed raising money from ultra-high net worths, fund managers, and I became more focused on the human component of it than even the finance component.

Mm-hmm.

Because ultimately it's a sales job.

Yeah.

Did it for about a decade and realized that it wasn't where I wanted to be for the next 10 years of my life. It was— it required a tremendous amount of socializing and, you know, sort of constantly out. And once you break 30, it's not as much fun as it is when you're in your 20s.

It gets physically harder, frankly.

Yeah, those recoveries get tough. So I took about a year, didn't know what I wanted to do, traveled a bit. And I ended up running into a buddy of mine from business school, as a matter of fact, and we hadn't seen each other in years. And I said, so what are you doing now? What are you working on? And he told me he was running an email marketing agency. I was a little surprised. I mean, the guy, last time I talked to him, I think he was interning at McKinsey or BCG. And I said, you're running an email marketing agency? What happened? What's the story? Then he told me they were doing about $19 million a year and there was 5 of them. And all of a sudden it made sense. So it was the first time I'd ever been introduced to even the concept of go-to-market in the agency space. So I did a little bit of due diligence and I said, you know, I'll, we'll get paid thousands of dollars and we just have to book the meeting. We're not responsible for closing. It was like unfathomable coming out of an industry where very much you eat what you kill. So we jumped in with both feet. We went 0 to 1 in 90 days. And the biggest reason was we had really, really tight sales processes and we were used to selling something much harder. I brought a buddy of mine from finance with me and we started building our organization. What I realized almost immediately is unlike financial sales that are very emotional, there are certain factors that drive people to make investments. When you're selling software, enterprise software, or selling to mid-market and enterprise companies, it's all about timing. The emotion's gone. It is, you know, are they in a procurement cycle? Do they have a budget allocation? Is this something they're moving forward on? Because especially large companies, they're not agile enough to jump all over a new technology or a new product or a new service. So I started looking at Intent providers. That was the first thing I sort of came across is these intent providers. And it was the first time I ever heard the term account-based marketing.

Right.

And immediately, and I think it's because I didn't come from the industry, my sort of antenna went up and I'm like, wait a minute, companies that have 200,000 employees are literally looking for everything at all times.

Yes.

They should trigger every single signal across the board. So it doesn't really matter that I know that IBM is looking for cybersecurity, just to make up an example. They're literally looking for everything.

Right.

So I wanted to focus on the individual, and that's ultimately how I ended up here. But I knew that the answer had to be in data. Earlier in my career, I had taken a bit of a break, and I was on a National Science Foundation grant. that studied algorithmic bias in public policy. So I'm an MBA, but I have a heavy data background and AI and machine learning background. So instinctually, I sort of knew what was in the data. I just didn't know where to look. And I started realizing that there was a tremendous amount of data out there that you could just purchase. And I got my hands on 22 billion rows of data. It was website visits across 8,000 websites. And we were able to de-anonymize that and bring that back to the individuals. And I ended up hiring a machine learning scientist. He's now at Anthropic. They offered him a comp package at 26 years old that made me reconsider every decision I've ever made in my life. He was with me about a year and a half, made a— he built the first model. But I said, you know, what can we do with it? And he said, we need a source of truth. We need to know like what conversion looks like. And while, you know, our organization was growing, we weren't growing fast enough to manage that size of data. So this is late 2021, so sort of pre-ChatGPT.

Yep.

I had reached out to a friend of a friend. I have— I'm a big believer you sort of live and die by your network. You know, if I should be able to get anything I need within 2 steps, 3 steps max in my phone, Or I'm not doing my job well as a founder or an executive. But I ended up getting in touch with the owner and founder of one of the big progressive dialers, you know, where it makes 5 calls at a time. Think Orem, Nookst, those types of tools. And it was one of the big ones and he loved the concept of what I wanted to do and said, sure, I'll give you access to all my data. I don't care. He gave us access to 180 million sales calls.

Oh my God.

With dispositions, with categories, with industries. And again, this was 2021. It was, you know, no one that was thinking about training data or what any of these things mean. It was just a, you know, a friendly gesture to another founder.

Wild.

Yeah.

Yeah.

So we started testing that and what we were able to do was determine this is what website behavior looks like. When someone says, yeah, I'm not interested. This is what that person's behavior later looks like when that same person says, yeah, you know what, call me in a couple of weeks. And finally, this is what it looks like when they say, sure, I'd love to take a meeting. Now, we had 30 guys on the phone, so what we would do is we would basically test a set of data based on the behavior. We would see how it would do, and then we would backpropagate it and try it again. And we were continually refining the Machine learning output based upon the results. We ultimately built a technology and a system that allowed it to happen. We also brought in time series data for the first time. That's a notoriously hard problem to manage in AI.

Yeah, yeah.

Amazon actually just solved it about 6 months ago with Kronos, but prior to that, it was very challenging. And we built a trimodal architecture and started bringing time into it. So it was not how these signals looked in isolation, but what did they look like over a time series? Fast forward 3 years, that agency set over 10,000 meetings the last year, exited the agency, and had built a system that instead of processing 8,000 websites to date, now tracks behavior across 450,000 top-line domains. tens of millions of websites underneath them, it's about a trillion behavioral signals per month. So we're ingesting every day 35 billion rows of a website, an identifier, and an IP address that we then tie back to our system, ingest into our model, and predict who is about to make whatever decision or whatever move that they're going to make. Initially, we thought it was just for B2B SaaS. That's what we built it for.

Right.

So we started what became my second company, which was focused on behavioral intelligence. So we know what website someone's going on. Let's see if we could figure out more about their personality, who they are, how do we connect better with them? And we launched our— my next venture. We got to 1,000 customers very, very quickly. And we churned one customer over 100 days. Super sticky product, 29-minute average session time. People loved it. Problem is, even though we had built a better mousetrap, so to speak, B2B sales intelligence is an incredibly noisy space.

Right.

So I learned a valuable lesson. VCs don't like to compete with big giant billion-dollar incumbents.

Yeah.

So I got beat up a little bit, and at the time I had 2 engineers. One was out of Lam Research and the other was out of Meta. And I kept saying, there has to be a different way to present this. We have the coolest tool. How do we present it different? How do we tell a different story, a different brand story? I think in a lot of ways, most technology and software Even data has become commoditized. So it's, can you execute? Can you tell the right story to get users to engage? So I said, let's build a natural language front end just like ChatGPT. It can't be that hard to do. We have all of the data already.

Yeah.

And we have a model trained on behavior. So my engineers hated the idea. It's challenging. It takes a lot of horsepower to build. But we ultimately built it. And I'll never forget, I got a call from my head of AI. It was on a Sunday. And he goes, you have to get on a Zoom call with me. And I said, okay. You know, what's interesting is I had a hard enough time getting him on camera on like a Wednesday at noon. And all of a sudden, he wants your attention right now.

Yeah, right. Yeah.

Now it's 6 o'clock on a Sunday. He wants to get on a Zoom call. So I get on the call and he pulls the platform up and it's— I see it's, you know, being run locally. And he goes, this data is about 2 months old. Check this out. And he types in a search and the search says, find me senior engineers looking to leave their job for a new startup, something to that effect. And he ran the search. And at the time, that demo only populated the top 2 people, but it populates these 2 people. And he clicks into it and it says he was on, I don't know, TechCrunch reading about, you know, engineer founding engineer comp packages. He's reading about the best startups on Crunchbase or AngelList. I said, that's very cool. I said, how accurate is it though? This isn't what we built. And he goes, well, check this out. He clicks on his LinkedIn and sure enough, the guy had just left his job at Google to go to some AI startup.

Right.

And I said, how does that work? And there is, from an AI standpoint, there is a very technical explanation about how models generalize over time. And he explained it, and I understand enough to be dangerous.

Right.

But what I realized on a more human level, and the way I've sort of translated it, is people make decisions for themselves in a similar fashion.

Right.

So I think about whether or not I want a new VC on my cap table the way I thought about proposing to my fiancée, the way I thought about buying my first home, right? Or bringing on a new partner.

But to be clear, that method is what? Slowly in these kind of dribs and drabs over time, not in one big decision thing where I'm sitting there all at once deciding this one thing right now. It's small, little—

It doesn't happen Saturday night at 10:00 PM when you put in every single query from 10:00 PM to 11:13 and you have your answer.

Yeah, that's the thing. In isolation, even the, the individual things in isolation don't mean anything, right? Just because you go to Zillow doesn't mean you want to buy a house. But if you go to Zillow and the next day you're looking at school districts in that same area, and then a couple of days later you're looking at moving companies, you can sort of deduce that something's happening more than just casual browsing, right?

Right.

The thing is, is like, I have a very stable, methodical approach to decision-making. It's probably from my upbringing. Who knows? People who make rash decisions make rash decisions— excuse me, make rash decisions about everything, right? Like, they're not going to take a, you know, this long thought-out process, but then do something sort of completely out of the norm.

Right, right, right. Nobody decides, well, this decision, this methodology I'm going to use to make this decision is going to be different than every other decision I make.

They buy a salami sandwich the same way they buy a Ferrari.

Exactly.

Exactly.

So we had the personality data from the company that I ultimately ended up sunsetting. And now we had this ability to predict all kinds of behavior because we've had so many disparate outcomes that we were feeding back into the model over the 100 million hours that we had trained our AI models over 3 years.

Well, let me ask you a question about that. So you said a lot of stuff. So, so Talk to me about, or talk to us about, like, the connection back, right? Because, like, the— and I'm using the wrong terminology because I'm not an AI person, but, like, you have all these hypotheses and you have all these thoughts about what people have searched for. And then you go back and the example that you gave earlier was the guy left Google and went to an AI startup. So how do you turn the queries into actual results that you are validating the, I guess, the strength of the model, if you will.

Yeah, so that's a great question. So that's the— when people say AI is just probabilities and just numbers, most people are like, okay, whatever that means. And what that actually in effect means is when you're training a model, you have your input, then you have your potential weighted factors. in this case, different websites that people are visiting. And then you have the outcome that you're expecting. And what the model will do is the model will run a combination of different variables. It will calculate how far off the result is from the expected result. Then it will backpropagate and do it again, trying to limit the loss amount. So trying to get the distance between the actual result And the predicted results closer and closer and closer.

In other words, create a shorter path to the result based on the kind of iteration of these different kinds of basically possible outcomes. I mean, it's almost like you're taking, you know, individual timelines, right, that are all like these possible things that could happen, right, in terms of this, all this stuff. And then basically once you kind of figure out what's the best way for it to get to this end thing, you can work backwards and figure out, right, how to predict it moving forward, right? Does that make sense?

Yeah, but with machine learning, you could do millions of iterations at the same time.

Right.

And you could have millions upon millions of stages and steps in the variables as well as individual variables. So what we had that started all of this was by having 180 million sales calls where we knew on one end what the outcome was. And on the beginning, we had that same person and we knew what all of their different behaviors were. We were able to predict these behaviors lead to this outcome, these behaviors lead to this outcome.

Right.

And then we started optimizing to say, okay, how do we predict and repeat the, I wanna take a meeting outcome? And ultimately when we exited the agency, our lead to opportunity conversion, so if we booked a meeting for IBM or Oracle or one of our customers, the likelihood that that would become an open op in their CRM was 72%.

Holy shit.

And it's because every person we were contacting, we were getting at that sweet spot where they're done with their research, but haven't reached out to the big incumbent yet, right? Oracle will always win because they're Oracle. And I know if I need certain things, that's who I'm gonna go to, right? People will associate certain solutions with certain companies and they win by default. You need to disrupt that pattern and engage with them prior to them reaching out to the company that they think in their head when they think of that problem. And by doing that, we were able to set really high quality meetings and a boatload of them. I mean, 10,000 meetings a year with 30 reps.

But that's 72%, Chris. I mean, just, just, you know, look, for all of our audience and the whole thing, you know, obviously I have a huge marketing background, okay? And so You know, we were all, right, our whole lives were like, you know, how can we figure out and predict, you know, what's gonna happen in the future? I mean, it's been the thing that's been missing, you know, frankly, from marketing, sales and marketing and the whole thing for forever, right? And the guys, the human beings out there who are good at closing and, you know, the rockstar sales guys, whether they know it or not, they probably have this kind of similar thing going on in their heads from their experience, right? They know what the signals are, they see what the stuff is, But their starting place is the same. The starting place is, let me get as many people into the top of the funnel as I possibly can, because I know that I'm only going to change— I'm only going to close or get 7% of them to end up taking a meeting.

At 7%, you're wildly successful, Joe, to your point.

Exactly. But I have to go after 7 million people in order to get 70. into my funnel, right? And so, that's been the always been— that's why television media took off, right? It was the blasting, the only way to be able— it wasn't about prediction, it was about, you'll tell me if you're right because I hit you with my message and you'll fall into my filter if it's right for you, right?

And you happen to hit that serendipity, right? Like, I think of real estate marketing where, right, you know, if I could identify the sellers in you know, Oyster Bay, Long Island, of who's going to sell their property in the next 6 months. And we— I did a project with this, you know, 10 years ago with a bank, with a bank, you know, I'm not going to mention the name of it, but it was amazing how much information we could glean from their credit card. But that was sort of like we were putting this together sort of on the fly. But it sounds like what you're saying is, Chris, if you have all that data about successful home purchases and that data purchasing information, you could put that into your model. And, and get a much higher response rate or much higher hit rate to see if you predict this thing to happen, you're much likely, you know, you want to go after this person because it's a, it's a level of efficiency that we have never, never even come close to.

And I think that's where, that's really where it is, right?

Not surprisingly, the head of the private client group at Compass Realty actually is on our advisory board. To date, 4 out of 5 of their top earners, these guys who do $300 million in sales a year are on the platform because it's a very different approach. I think one of the reasons that all of these companies struggle to hit it out of the park with AI is that they try to use AI to improve their existing process. Whereas we said the process is stupid, there has to be a better way. Let's just create a new process. Why am I sending out 10,000 emails to get down to the 100 that I wanna talk to? Why don't I just spend, like, rather than send those 10,000 emails more often, why don't we just use the AI to filter down to the 100 that I want anyway? And I know I could pick up the phone and close 7 out of 10. And it's a very different mentality. I think people often see the platform, they're like, all right, cool. How do I start emailing these people? I'm like, oh, you're not dumping these into some marketing cadence. These are your buyers. They were literally looking at your competitor yesterday. Yeah.

You're calling these people, getting them on the phone or knocking on their door, for goodness sakes.

Yeah. Yeah. It's absolutely right. And I think that's why our investors are so excited. And, you know, we got such big early adoption. We haven't churned a customer yet. Again, in fairness, we're only in early access, so it's still limited. But people saying, how do I get this exclusivity? Real estate, everybody wants exclusivity.

Which, yeah, let's do this right now for everybody. Chris, where do people— we're gonna do this at the end too, we wanna get this in the middle— where do people go to access or to see more about it? I know you're an invite only, but what's your web— what's the company's website?

Yeah, so actually by the time this airs, we'll be fully wide open. It's nextlm.ai. You can sign up for free, you could run a couple of searches for free. And actually we designed it so our— the free searches you will get are the full power of the app. It gives you everything. We have something called Deep Insights, which will actually show you the behavioral pattern. Are they increasing in frequency of engagement? Are they decreasing? And what we've really focused on is how do we translate that into somebody who's not data-centric, right? Somebody who doesn't study data all day long, A bunch of numbers and signals doesn't really mean anything. So we've worked really hard to translate it into not only here is your person, but here is why it's your person in a digestible way.

Right.

We've actually recently added to that to make it actionable, DM scripts that are— the specific language is optimized for each prospect based upon their behavior.

So, yeah, so, and forgive me if I, you know, I know we talked about behavioral AI, but so when someone runs a search, like if we could, if you could just walk a person through someone who's doing a search and they're like, what is an optimal search if they're, you know, they're looking for a certain set of customers? What is done from step 1 through step 10? Step 10 being they have a meeting with them. If you could just walk through sort of What you're— what you want your users to do with NextLM and then the sort of practical way they can go from step, you know, 1 to 10, if you will.

I would say that the first step, and this is something that we are continually educating our users about, is get out of the mindset of audience building. You're not building an audience, right? If I'm selling a $10 million house, for instance, I don't care if the janitor wants to buy it. I just care if he could write the check.

Right.

And, you know, so we've been, we've been programmed to think about things through demographics, but ultimately we just want people that will buy.

Hey, I'm Joe Stapleton.

And I'm Jon Kniff.

You know, Jon, most founders put everything they have into building their businesses— years of sweat, sacrifice, and problem solving. But when it comes time to exit, many are shocked to learn that what buyers value isn't what they thought.

Here's the truth. Buyers don't pay for potential. They pay for proof. Proof that your business can grow without you. Proof that your financials are clean. Proof that your processes are clear and repeatable.

If you wait until a buyer shows up to think about these things, you're almost guaranteed to leave money and control on the table. ExitEngine was built to change that.

ExitEngine is the first readiness platform that helps founders design their ideal exits. step by step, combining education, coaching, and support, and access to an elite network of business specialists and capital. ExitEngine empowers you to build a valuable, transferable business and exit on your own terms.

It's not about selling tomorrow, John. It's about making sure that when you're ready, your business is worth what it deserves to be.

Don't let your life's work get discounted. Control your business destiny today. Start preparing now while you still have time to engineer the exit you want. Visit myexitengine.com to learn more.

So we're focused on the behavior. It's a natural language prompt as simple as, find me someone looking to purchase a new enterprise CRM. And it will identify that behavior. You could put in title and location. It's generally not necessary. Real estate location matters, but most things it doesn't. If I'm a wealth advisor and I have a million-dollar minimum, find me people who can, who are looking for a wealth advisor who can handle a million-dollar minimum. And our model is smart enough to identify those people. And it'll give you a list of 5 people. And with those 5 people, it'll give you all the contact information. It will give you a breakdown of why we identified this person. Like, what behavior have we seen from them that makes us believe they're a good person for you to call? And we'll even list, we'll list the domains, you know, the top 3 domains that they were on, how long they were on it, what the depth of their engagement is. You know, if we could, we pick up scroll speed in a lot of instances. So we can tell if someone's really engaged with something.

So cool.

Yeah, it's wild.

Well, Chris, I just— I want to step back just for a second, okay? Because again, it's— we know this, we've talked about it before, we've seen the thing. So I was— but I want to slow down just one more, just a second, just like what John was doing. Because again, it's a matter of— for people listening to this, you're just kind of like, wait, what's going on here? So understand that what we're talking about, okay, is exactly what you think it is, which is The ability to know that a specific person has actually been out there looking for the service that you provide, not necessarily about them going to your website and said that they want to be interested, which is what everybody is waiting for. Oh, I'll be out there and I'll, as you said, build audience. And when those people come and I get them to come down my funnel, then some of them will fall in and some of them will fall out, right? And I will, I will take the ones that I think have said, have raised their hand and said, yes, I'm, I'm interested. What Chris is doing, right, with this, this thing is basically has been able to, to predict and show you that a specific person was out there looking for your services, okay, and that they are obviously right there within your wheelhouse. And essentially all you should have to do is, is contact them and say, I'm someone who sells that service that you're looking for, Isn't this a fortuitous time that I happen to reach out to you right now when you are looking for this service right now? And they go, oh my God, you're right, that's crazy. How did you— isn't it amazing that the universe brought us together this way? And yes, I am looking for this stuff. And now you're off and running to the races, and you've done that by doing a search and picking up the phone.

Okay.

Like, like, like as if you had a store and someone walked into the store and said, hi, I like— I'm looking for a CRM. Right?

Yeah, that's absolutely right. It's a very different approach to sales and marketing because you're finding people who are looking for you, but they haven't raised their hand yet. Right? They haven't reached out to your competitor. They haven't reached out to you. You may, you more than likely don't know they exist, but they're out there in their home or in their office looking for exactly what you sell.

Yeah. And that can be, that's a, you know, the top of the funnel, throw in 100 people. I get in touch with 30, 7 blow me off, and 2 people set the calls. That's a certain type of person that's been successful from the beginning of time until now with sales, right? The get everyone in. But what you're talking about is, is a more subtle sale, like, right? It's a subtler— sorry, a more subtle discussion with that prospect, right? Because They're a different type of prospect. They're way— they haven't maybe raised their hand for intent yet saying, I want to see a demo or attended a webinar. They're that pre-stage of that. So it takes— I would take it, I would imagine that there's a lot of training opportunity for people to be trained on sort of what this new type of prospect is and how you should approach them.

So that's actually why we built the 3rd stage, the coaching corner. It gives you the language to use when you're reaching out. That's optimized for that person, that situation where you can convey the opportunity, your specific opportunity. 'Cause we wanna, you know, that language is tailored to what you're selling or what you're looking for without being creepy about it. Right. Like nobody wants to get called and say, hey, I knew you were on this website. I offer something, you know, comparative. There's, it requires a level of subtlety. You know, a lot of a lot of the thought behind this was earlier in my career, everyone always had the magic lead. Oh, I have this list; it's the best list. You'll close nine out of ten. They're all looking for you.

The Glengarry leads. Yeah, I got in first, guys, just so you know.

You did.

Listen, one of my my partner refers to you know these are the Glengarrys all the time. Brilliant, brilliant guy, and um. Yeah, no, 1,000%. And that was, they were always sort of elusive and it's because the approach was always reactive rather than proactive.

Right.

So much of sales now is reactive. Oh, they visited this website, so that must mean. It's the difference between actual deterministic intent versus implied intent.

Right.

And so many of these companies are shooting in the dark. You know, now they've taken intent and now it's being called signals. It's a rebrand of the same old data.

Yeah.

And by the way, the signals are like, oh, the company, you know, the signals are the company just, you know, has an upward trend of hiring employees. You're just like, well, that's, that's, that could be it. Like you said before, it could be anything. I mean, they're, they're in the, like you said, and some companies are in the market for everything all the time.

Well, that's a signal for everything.

Right.

You know, I put out a job rec on LinkedIn, I'm getting people trying to sell me everything from cybersecurity to a refrigerator for the office that we don't work out of.

Right.

Because hiring signals growth. So we're the only platform in existence that our quote unquote signals are not publicly known. Right there. It's not something you could just, you know, anything out there. I can tell, I can figure out if a company's hiring. I could look at press releases. I can look at if somebody's job changed.

Right.

We go a level deeper because we look at the individual and we look at the individual's behavior. It's very much a B2C approach to B2B sales. This stuff has existed in the B2B, in the B2C space. Every time you think your phone's listening to you, and you get some ad for something you were just talking about, you know, a couple of hours later, is because these companies have been collecting this data for quite some time now. There's the famous story with Target where they sent baby formula coupons to a woman. She didn't know she was pregnant yet, right? But they had picked up on these tiny little differences in her buying behavior.

Yeah, I remember there was an article, that article was in the New York Times, and it's weird, I remember this in October 2012, because I remember it so well because the father This— the article said that the father got these coupons, went to his wife and was like, what's going on? And it was his daughter who was actually pregnant at the time, and nobody knew. So nobody knew. It's— yeah, if you— it's a wild article.

I'm gonna go read it now. I've always only, you know, sort of heard of the story more than anything.

Urban legend. Yeah, right, exactly.

Yeah, yeah, it's very much an urban legend. I'd have to go read that article though.

That's so funny though. But, but look, I think this is the— this is so— this is what's so fascinating about this too, is that it's, you know, to me, is that it— first of all, you're right I mean, we've talked about this before. You, you, you are— the reason that you're training around this too is because you are saying, look, we can't— and you don't want your tool to be responsible for this either, this Big Brother thing, right? Which is always going to be a little bit of a sticky wicket, privacy. And for those reasons, the reason that that story exists is because it's a cautionary tale around all that.

But I think we put a lot of safeguards in place in that regard. You can't look up a person, so you can't say, you know, what's Jo Stapleton looking at?

Right.

The biggest thing, I have a really funny story with that. We were sitting down with a very well-known VC, tier 1, one of the big ones, and we were sitting with a managing partner, and I had 2 of my partners on a Zoom call, a couple of us in a conference room, and he said, what if we just put in a list of people and then we could say all the behaviors that does. And my lead engineer says, you know, yeah, that's sort of opening the door for like every creepy stalker in the world. And I'll never forget for as long as I live, the MD turns and goes, yeah, but we'll make so much money. Yeah, thanks, but no thanks. It was right out of Silicon Valley. It felt like I was living in a TV show. I couldn't believe it just happened. But yeah, we're entirely behavior first. So there's no real way to trick the system. You can't say, oh, find me the CEO of this company, if they're— there's no way to trick it. My fiancée was actually the head of content moderation at TikTok for 5 years. So we have really, really tight content policies just about the way people utilize the tool. But yeah, we took a lot of pains to make sure that that Big Brother feel didn't Didn't happen. So why we don't put the specific websites, we'll say they were on cars.com, but we won't say what, you know, we won't, we won't get like the specific house that they were looking at, for instance.

Right.

If they were on Zillow.

Right. That stuff actually probably doesn't matter. Right. But, but here's the thing I wanna, I think it's important to point out the difference here too between general like website searching. Right.

Right.

Which, which again, will, will We can use search to kind of predict it, right? Like, you know, oh look, this might be somebody who might be searching for us, it's good enough, whatever. But it's the reverse, right?

Right.

This is instead of, again, instead of allowing people kind of coming to find you through these mechanisms on their own, it's about kind of turning this really on its end and going back the other way.

Yeah, we know they're looking to make purchases sometimes before they even know they're looking to make purchases. And we look at each person and each set of behavior at an individual level. Websites are one piece of data that we're ingesting. That's the thing that I think is sometimes challenging to convey is that the websites aren't the results of our prediction. The websites are actually the embeddings. Those are the beginning of the process that leads us to predict that this person is the person you should speak to. We've seen a ton of success in fundraising because we could put in, you know, find me VCs in New York looking to invest in, you know, Series A AI companies. We've used it for recruiting. We've used it for, you know, find me a senior engineer who wants to leave their big corporate job for a hot new startup.

Right.

And, you know, we brought on one of our most recent AI engineers So the tool is really limited only by your imagination. One of the earliest sort of funny use cases that we had was one of our investors is getting married soon, and it said, find me an investor in New York planning on getting married. And it surfaced a couple of people. She didn't come up on the list. Believe it or not, there are several people in New York looking to get married. But one of the people— we were on a Zoom call, and one of the people came up, and my chief product officer— I said, what are you doing? He goes, I'm pulling her up on Instagram. He tracks her down on Instagram, and sure enough, there are the engagement photos, and there are all of these different data points. I can't tell you how many times I'm on calls with either realtors in really specific areas. I was on a call with one of the guys who leads Aspen. He's the number one realtor in Aspen. So you can imagine he has a very particular clientele. But he ran a search and he goes, oh my God, I know that guy. I literally know that house. I've been trying to get him to— pushing him to make something happen. I got to give him a call back. I haven't spoken to him in 6 months. So we've had so many sort of red balloon moments that verify the validity of everything that we built. It's been a wild ride.

Dude, Chris, amazing. There's so many implications for this, right? And so many use cases and so many ways to kind of put this into practice in any— and it's not just about sales. I mean, I know that this is right. I mean, I understand that this is right now kind of where the focus is because it makes sense. It's the first line of defense. It's the thing that people want. It's obviously such a change in the paradigm that it's literally the thing that everybody's been waiting for. I mean, you know, I mean, I can tell you specifically from my own cases. I mean, we sat there how many times, you know, I was— I ran an analytics, a marketing analytics company for a little while. And I can tell you that, you know, the predictive modeling we were doing was just a joke compared to this, right? I mean, a joke. And that was only— that was 10 years ago. You know, I mean, the fact that we can do this stuff now and that you can actually predict within to the person, right? I mean, that is just— the whole thing is amazing. And the fact that it cuts out people like my wife, Anzila, who are, who are Not buying a house, by the way, but are looking at Zillow all the time and showing the difference, right? You're not wasting people like that because there are other signals that go in and other things that are going to be coordinated with it, not just about the one search activity or the one website activity, it's all of it together, right?

Yeah, it's not in isolation, right? It's how do these things interact over time and space, and for I mentioned this earlier. One of the biggest mistakes I made with my first company that I ultimately exited was I made myself so central to the business.

Right.

I didn't put the right processes in place that would continue to run without me there. So there was this big question mark on variance without me and, you know, the rest of my leadership team. What would the business continue to do? And I'll tell you, we paid the price financially. You know, that multiple was not what it, what it could have been. This, I think in a lot of ways, this tool allows you to build that repeatable, scalable process that makes revenue more, you know, more predictable, you know, makes the entire process more systematic. Right.

Well, let me ask you a question, Chris. So what's, so what's next for NextLM, right? So Walk us through, you said, you know, the site's gonna be widely available soon. What do you predict, you know, for user prediction? What do you predict is next for NextLM to see, you know, where are you gonna go in the future? You know, to the extent that you, I don't wanna give you, get you to give things away, but where do you think this will go in 2026 and beyond?

I think ultimately it becomes much more of an infrastructure piece. I think there's a lot of specialized tooling around a lot of different industries that this, like you mentioned, it has so many more use cases just beyond sales, right? There's a whole, if I wanna know if my customers are going to churn, like there's a tool that could pretty easily be built around that. There's tons of things you could do if you laid this in with other HR tools. So I think we're going to see NextLM become more of an infrastructure piece that has all of these different vertical applications, right? Where you have sort of individual scale with each one.

Right. So it could be like, and it's even the wrong terms, but like almost like an API for artificial intelligence, if you will.

Yeah, that's actually where we plan on getting to. I mean, that's it. It eventually becomes an API. That is the backend that powers everything from enterprise to small business. It powers ecosystems.

To append their data so they can— so again, you can append your HR data or use your HR data in their instance of NextLM to predict company churn, to predict— sorry, to predict personnel churn, customer churn, whatever sort of individual problem that the company wants to solve to use artificial intelligence. This is a very practical way to do that.

Yeah, we would just need to think about how to make sure it's never too Big Brother-y. I always want to be on the side of the good guys, you know, before I start giving it to HR companies to find out who's shopping other jobs. But yeah, no, there are literally infinite applications to it because it's truly behavior agnostic.

Yeah.

You know, we ran— I ran some crazy searches when we were building the model just to see what the results were. Find me an executive in the Bay who's looking to get divorced, and you start finding people who are looking at asset management and asset protection. Now, we've obviously blacklisted all kinds of things like that.

Sure.

Yeah. But just to sort of test the limit of the model, we don't know where the edges are. We don't know how far we can push it. So it makes it a lot of fun to continue to develop. For my team— sorry about the puppy— for my team, it's a lot of fun because we're always thinking about new use cases. We're always thinking about new ways to play with the technology and tweak the technology. We're learning so much from our users because I think about prompting in a certain way. I've worked in the space for a while. You get to learn that people communicate with AI in very different ways.

Yeah.

And we get to adapt our technology not just based on outcomes, but also based on those different ways people engage.

Who's using it, not just who you're trying to find, right? I mean, right. Yeah.

So we built our own sort of core understanding of how sales and marketing works.

Yeah.

And it'll be so cool, like when you get, when you start looking at those queries while people are using it, how they're using it, the different use cases will, like the data that these users will put in in the very near future, will present so many opportunities for new business cases in 2026. It'll be eye-opening, I bet. It'll be cool.

Yeah, I can't wait. It's been an awesome year, an awesome couple of years, long hours. I was pulling 120-hour weeks at one point, but it's awesome to see the reception we're getting in the market.

And it's not a surprise. I mean, I think that I can speak for— I think I speak for every sales and marketing person out there when I say thank you. It's been a long time coming. We've been trying to figure this stuff out and the whole thing, and to be able to get to this place now, I think it's obviously, you know, I think one of the things here is that, you know, is that we're gonna have to have a follow-up on this, obviously, because I know there's gonna be new stuff kind of happening and new ways of being able to use this, you know, as we go, first of all. But second of all, you know, look, there is a discussion to have around how this affects things moving forward because Obviously, this does— this changes the paradigm, right? Obviously, it's a major shift. Um, it, it not only gives us what we've all been wanting to have, but like you said, you got to train your brain around it, um, to make sure that you don't start really pissing people off. Let's face it. Um, and, and I think the other thing that, that comes up just as a, you know, as a question, uh, to be discussed is, you know, what— when this thing is so out there and so like Prevalent, and now everybody knows it's being used that way. You know, there, there's, there, that no matter how many kind of like things you put around, how many, how many guardrails you put around it from a thing, there's still— it's like the, it's like the fact that the phones are listening to us in general, right? There's a, there's a whole thing there that's like, oh, I don't want, I really don't want them to be doing that. You know what I mean? I'm not sure why I don't, but I guess I really don't. And anyway, you know, if this— all I'm saying is that. It brings— it still brings up a discussion about how this can be used and whether or not do I get a point where I get to opt out of stuff like this somehow because something is, you know, something is following me around no matter what, right?

We've built opt-out. There's a very easy opt-out to get out of our database. Everybody in it has gone through an informed consent process. We have like an 11-step compliance process. Yeah, but you know, it's interesting. We've had a lot of these discussions internally. And one of the things that we keep coming back to is, have you ever used the internet in Europe, like in the EU? You're looking for a restaurant and you're getting ads for a toaster oven and it's so impersonal. I think instinctually people want relevance.

Agreed.

I would rather get called by one person selling me exactly what I want than 10 people selling me things that I don't. And it ultimately improves the customer experience because you get rid of a lot of the noise when everyone's using it. You know, it's no longer hiring equals every potential sales in the world. It's now, you know, I need cybersecurity, so 3 cybersecurity guys are going to fight over this.

Right.

You know, it makes it much more about who can execute rather than The sort of luck of finding the person that happens to be your buyer because they meet your demographic criteria.

Look, I mean, since the internet, and I was, you know, my kids think I invented the internet, but the idea is that if I go back to, you know, I've been working in the internet space, you know, since 1996, right? I mean, I started one of the first divisions of an advertising agency to take on this whole digital marketing thing. We used to call it new media back then, but the idea was that, you know, the But the big, the 2 big promises of the internet in general, okay, way back then were, to me, were one, the long tail, right? The ability to be able to have little tiny little communities built around small, right, right, the niche kind of idea, right? I can build a community around these, these little interests because I can bring all kinds of people together through this technology when I used to have to just be local, right? So, a long tail was always one thing, but the other thing that I always saw was the ability to be able to get people in line with what they really want. So, instead of just getting ads after ad after ad, that's the annoying part, but if the ad is something useful and I'm looking at it and it's something I actually want, well, then it's not a nuisance.

Yeah, no, that's absolutely right. I mean, I think that's sort of the relevance component.

Right.

You know, it's allowing people to just be approached by people selling the things that they really want and it makes the market more competitive. And as a seller or as a business owner, it levels the playing field, right? You know, like you go up against these big companies and you lose 99 times out of 100 because everybody knows their name and they have a, you know, a multimillion dollar go-to-market strategy. They're in front of these people 24 hours a day. Whereas now you could level the playing field because you could pick up the phone and call them.

I can send them a message in it. Yeah, exactly.

Yeah, you know, it really becomes, you know, how it's how David beats Goliath.

Yeah, it's awesome. Well, Chris, uh, Chris Anzalone, thank you so much, man. This has been, uh, incredibly informative. Um, I can't wait to get this thing, and we, you know, we're obviously talking about ways in which we can get this in front of our community and all that stuff as well. So You know, so stay tuned, guys, for, you know, for that stuff. Chris, um, if people want to find— let's, let's do the website one more time, and, and people can reach out to you or however you, you know, where, where can people, uh, find you if they want to?

Yeah, absolutely. Uh, the website is nextlm.com. nextlm.com. You can email me directly nextlm.ai.com. They all work, they all come back to us. Please send me an email directly. I love talking about what we built. You know, myself or someone on my team can definitely jump on a call, walk you through it. And look, we made it where our starting tier is $200 a month. This isn't a $30,000 a year contract.

And it could, by the way, it could be.

Just FYI.

Yeah, I mean, I think in a lot of ways it could be. And I have people on my team who are, you know, actively, you know, pushing for that. But we wanted it accessible. We used technology to make it very, very cost effective. And we're seeing some really outsized results. So for the time being, you know, $200 a month, it changes a lot of aspects of a person's business.

That's amazing. Thank you so much for being here today. Really appreciate it. Can't wait for what's next and to see what happens as we move forward. But I really appreciate it.

Thank you for having me. This was a blast. A lot of fun.

Very cool. Awesome. Hey, John, this is a game changer. All right, and it's not— I'm not overhyping this, okay? And this is a big deal. And I can tell you that, you know, we've talked about this before. My, you know, my, my entire career has been, been waiting for this, you know.

Well, I ran a huge project at my—

one of my—

the real estate company with a company— a guy, this guy named Charlie from Seattle who sent me this data set to say, hey, I've predicted that these people are going to sell their houses in, uh, in Northern California And look who did. And he sent me a new list and he's like, I'll call you in 6 months to see. And it was just, it was an amazing phone call. We almost bought the company. And this was, I mean, this was 2014. So yeah, I've, and we never got it operationalized, you know, we worked and I worked with a bank to do it. So I think this is so cool and it will truly revolutionize sales. And I think it'll make decision-making a lot easier and better for people. And as Chris said at the end, I think it'll really level the playing field.

It is. It's predictable, you know, that's the thing that people need. And the application of this stuff, not just in sales, but across the enterprise for decision-making of any kind, is where it's really going to show its value too, right? I think this is amazing stuff. I can't wait to be able to get it in front of our founders and all that stuff. Guys, thank you so much for listening to this podcast today, this special edition of the Exit Engine Podcast with Chris Anzalone from NextLM. We appreciate it. Would you please like and subscribe this podcast for us? Please share it with a business owner you love. If you have questions, you wanna talk about your exit strategy or anything having to do with the growth of your company, you can email me directly at jo@myexitengine.com or at john@myexitengine.com. You can find us all over LinkedIn. You'll find us hanging out with Chris. You'll find all this stuff everywhere, all over the place. Look at it right now. Guys, this is— you're in control of your own destiny, right? Take a look at this stuff and make sure that you are growing towards exit. Thanks so much. Have a great day. We will see you on the backside.