Episode 66: Shiv Narayanan of How To SaaS 
on the Future of General vs Vertical AI

Episode Transcript  

Shiv: Welcome, welcome, welcome to this episode of the Private Equity Value Creation Podcast. And we're gonna be doing things a little bit differently today. One of the things I realized is that we've been doing this podcast for so long and most of the episodes that we do are with guests that have expertise or are themselves private equity investors or advisors that are working in this space. But one of the things that we don't do is talk about topics in a deep dive fashion, where we can help you guys understand certain value creation drivers or strategies and approaches beyond just a conversation with a guest. And there's a ton of work that we've done here at How to SaaS that can shed light into a lot of those areas, whether it's go to market strategy or growth strategy or pricing or hiring and a bunch of other things. And so we wanted to start a series which will be interspersed with all the other interview episodes that we do so that we can also bring very quick or quick or specific episodes where we're doing a deep dive into a specific topic and help you guys understand concepts that we know can add a ton of value to your existing portfolio companies or also help you figure out where to invest and where the opportunities might be in the market based on on current market trends. And one of the things that's been really top of mind for us at the moment is AI platforms and AI is a strategy area in particular because it is something that is disrupting all industries, all markets, all product teams are thinking about it. Go-to-market teams are thinking about it. Investors are thinking about it. And so we wanted to focus one episode on that today. And we've also done a ton of work here at How to SaaS that we think could help you on your context. And the specific topic that I want to touch on today or go really deeply into is this concept of general AI platforms versus verticalized AI platforms.

To set the stage on this, I posted something on LinkedIn recently on this topic. Imagine if you are a neurosurgeon and you have a very important surgery coming up and you're trying to figure out if there are any resources that can help you in this space. Can you actually use a general AI tool like Gemini or ChatGPT to actually do the surgery? And the quick answer is quite obvious. It's that obviously you can't. And the question is, Why is that? Because these AI platforms have made so many strides, they're in the news and they're covered all the time. So why is it that we can't use these platforms? And the answer is that none of these tools have the requisite context or expertise to be able to actually help you do a neurosurgery because that use case is so very specific. And the general platforms do not have the training data from different neurosurgeries that have happened in the past or the results of those surgeries or what the different permutations were, what worked, what didn't work. They don't have the context of the surgery type. They don't have patient data. They don't have specifics on the specific procedures and things like that. And then on top of that, those platforms don't have the expertise. They actually are not neurologists that know how to do those procedures. And so whatever advice they would pull would be from general websites or general resources like WebMD. And so a neurosurgeon could actually not use any of the outputs that that platform would give to him or her. And now imagine a different AI company that actually has access to every single neuro procedure that has happened till date across every single hospital, categorized by type, by results, by the outcomes of those different approaches, along with patient data and specifics. And on top of that, the tools that that AI company is developing has the requisite processes and frameworks and context and expertise of all those procedures. And then the company takes all of that and then trains an AI or LLM to actually learn and improve upon whatever those inputs are. Could a tool like this help in your surgeon? And the answer to that is obviously yes. And the reason for that is this is actually the future of AI tools, because we're caught in new cycles and publications cover the generically bigger companies. We're always kept in the loop of what OpenAI is doing or what Google is doing with Gemini or Claude or all these other platforms that are emerging, DeepSeek, et cetera. But the future of AI is verticalized. And that's kind of what I want to help you guys understand today, especially because most of private equity is investing in companies that are vertical platforms, maybe they're in FinTech, maybe they're in construction, maybe they're in healthcare. There are all these different specialized verticalized software businesses that have a very specific customer base and they are for those customers and not for other customers. And AI is very similar because the efficacy of an AI or LLM platform is entirely dependent on the training data and the algorithm that is present inside that platform. And so if you think about things that have made a ton of strides in the last couple of years, like the general platforms, they've done amazing work, right? Gemini, ChatGPT, et cetera. The types of things that you can do with these tools is incredible. And the coverage is also justified because now you can short circuit a bunch of generalized roles that previously you needed expertise in-house to be able to do. So for example, something as general as producing a blog post, maybe you can get a general AI tool to do, but the more specific your use case gets, the less likely those tools are going to be able to fulfill that.

Meanwhile, you have this need on the verticalized specific use case side that is largely underserved. And when I say underserved, it's more just that the market has just not caught up yet because you have verticalized software companies servicing those markets, but you don't have verticalized AI companies servicing those markets yet. Whereas the generalized AI platforms have actually kind of been able to do that. And there's a couple of ways to kind of think about this. One crazy stat that's out there is that, and we actually had a previous podcast guest that talked about this as well, is that the bulk of features that are developed for a particular software, end up going unused by end users. So it's billions of dollars annually is wasted on product development, engineering, feature development for features that the end user actually doesn't end up using on a regular basis. Like even if you think about big platforms that are intrinsic to most companies, your HubSpots or Salesforce and things like that, most of the usage that you're getting out of those platforms comes out of a very limited feature set. Maybe it's tracking your pipeline, maybe it's sending out emails. But for the most part, you're not in trying every single feature that that platform has developed. In an AI-driven world, all of that changes because we don't need to develop those features because you can just interface with an agent that gets you an answer to your question without the company needing to develop that feature. And that's one of the reasons why Satya Nadella and Marc Benioff and all these people have been talking about agents replacing SaaS in the future because you're going to have these front end applications that a user can interface with or ask questions to. And then the AI agent does all the work in the back end without the need for the features because it can query a database or use whatever intelligent tools are inside to get you an answer without needing to produce the actual front end feature for the user. And in a world like this, right, in a world like this, the data is the product, not the features. So if we think about SaaS platforms today, the features, the feature set or like what we make buying decisions, we're seeing, like Mailchimp can send emails, can HubSpot send emails, Mailchimp can develop landing pages, can HubSpot develop landing pages. That's kind of how we make comparisons between different functionality of different software applications, right? In the future, the better your data set and the better your algorithm, that will be the distinguishing factor because the product to the user is the end output that the agent spits out based on the query that the user is making. And the reason this is important and the reason I'm talking about this and I'm setting it up is just giving you guys the context is that because the data is the product in a verticalized AI world, the more proprietary your data set that is difficult to duplicate, difficult for competitors to get access to, difficult for the rest of the market to even get access to, the bigger your moat in a particular industry. And this can be any type of data. This could be data that sits inside your platform that can be leveraged to create AI tools to help the users do a lot more tasks or get more value. It could be data that your platform outputs based on inputs from the user. This can be leveraged to train a proprietary algorithm or AI that allows for better outputs. Or can be data that's collected from your platform as part of working with your customers. Maybe they regularly send emails or build websites or whatever it is that they're doing on your platform. That's going to give you feedback loops on what actually works and what doesn't. And you're kind of like this data aggregator to figure out, this set of ad campaigns works well, or these kinds of landing pages do well, or these kinds of sales email sequences do better than others. And now you can kind of educate the customer back on how they can the way they interact with your product or use your product.

And beyond this is one more thing, which I think is the really big opportunity, is that it's when you have a proprietary process or insight into a market that allows you to leverage all the different data that you have to create more value for your end customer in the form of outputs or whatever usage they're getting out of your product. And this is especially applicable in cases where users are using a software to actually achieve an end objective. So for example, if you are a tech enabled service that helps companies run better financial reporting, as an example. Well, and we've seen companies, there's a ton of companies like this in the marketplace. If that tool had a proprietary process to analyze the inputs into your financial platform and the reporting that you're generating and came back with recommendations on how to find one, two, three, five percentage points of EBITDA by making the business more efficient without sacrificing growth, that would be an incredible proprietary feature in quotation marks to add to your platform, which would increase usage through the roof because odds are that up until this point, a platform like that is being used more as a input equals output tool where I just put in something and it kind of gives me a report and that's the end of my usage for it. But if I can put in data into that platform and now it's giving me intelligent advice back on how to improve things, now I have turned it from just like a cost center or a productivity tool to actually a cost efficiency driver or a revenue driver for the business. And now it is part of my operating system as a company. Another great example, just because we do marketing here at How to SaaS is let's say you give a platform full access to your Google Ads. One is if it operates kind of like one of the big reporting tools out there, it just kind of pulls in all the data from Google Ads and Facebook ads or LinkedIn ads. And it just gives you a report of how much you spend by channel, by campaign, by region, et cetera. And then it tells you how many leads you generated, how many MQLs you had, how many opportunities and how many deals you closed and how much revenue you had. That sounds great. What would be even better is if that tool could then identify which campaigns you need to shut off, which campaigns you need to scale, and then actually go in and actually make those changes. And without you having a human on your end going in and making those adjustments, the tool in itself does that and now this tool has replaced a full head count, maybe a couple of head counts worth of expertise on your team and is generating you significantly more efficient revenue.

And so the point on this is that every one of you guys, especially if you have portfolio companies that have certain components like this, have a huge vertical AI opportunity in front of you because each of those companies have a proprietary data set that is unavailable to anybody else in the market. Like, yeah, maybe competitors have a similar data set, but this is a bit of a race, right? Whoever gets there first in a way wins because you have that proprietary, the proprietary algorithm to actually leverage that data to create value for the customer. And then that will create a virtual cycle and build upon itself. And by the time a competitor comes in, you're off to the races and you're kind of way ahead. And this is one of those things where being first is actually really important. And anywhere where there is some sort of a service component inside your businesses, where your tool is either analyzing data or ingesting data, transforming data, or you have a service where you're taking a client through a step-by-step process to get to an end outcome, you are ingesting all kinds of data that is not being leveraged for vertical AI use cases. And so you are kind of exposed to this continuity risk. Because somebody somewhere in your industry is working on an AI solution that is ingesting similar data sets or building a similar algorithm that will allow them to output recommendations or like an agentic application or automate a certain process that leverages a similar algorithm that makes your end user significantly more efficient for a fraction of the cost. And then that would make your business irrelevant. Maybe not today, maybe not tomorrow, but you might start to see a decline. And in five years time, when that AI is truly a full-on replacement for what you do, your business basically has a major continuity risk issue.

And so there's kind of like these two sides, because on the one hand, if you develop this, you can potentially cannibalize your own business. you can also potentially distract your current business by trying to develop an AI tool that takes away from investments that you can make in your core products and services. And on the flip side, you have this almost existential risk, which is if you don't do this, there will be someone that produces a vertical AI tool in your market that tries to steal your lunch money or steal your customers because they will make it significantly cheaper. And the job of the business leader, the CEO, et cetera, is to always make sure that you are ahead of where the market is moving and work on the business and ensure business continuity so that the business continues to grow through all kinds of marketing trends and transformations. And so this is my argument to say that this is the main reason why you need to be thinking about this for your company before someone else does it to you. And it's even possible that one of the general AI tools does this. Like maybe ChatGPT and Gemini cannot do what I'm describing today, but maybe in five years time, once they have ingested enough data, they will be able to do it, right? So one way or another, this existential threat is coming in your market. And so thinking about what are the product-based solutions that you can produce here, it's about product, it's about pricing, it's about packaging, and it's also about thinking strategically about how you can leverage this platform or this idea of building a verticalized AI tool to help you actually grow the core business in a way where it kind of intertwines with what you're already selling. So that like kind of it's like a rising tide lifts all boats. Like there is a way to do this where it doesn't cannibalize a core business. It actually helps you grow in all areas of the business.

So we've actually been doing this at How To SaaS. And that's, I want to bring, use that as an example here to kind of bring it to life because we're a services-based business. We've been doing this for about six years. And we work with large private equity investors, and we have three main offerings. It's marketing due diligence, which is between LOI to close. We can analyze the potential of a target investment. Two is general strategy consulting. This happens either post-close on a new acquisition, or it's an existing portfolio company that we're scaling up and figuring out what the right size strategy is, budget, team, transformations, integrations, all that kind of stuff. And then we do fractional CMO services where we are operationally leading the team, partnering with the CEO or investors. So we built this business and it's grown year over year. It's highly profitable, 40 to 50% EBITDA margins. And it's a great business to own and run for me personally. And I'm the only owner and it's bootstrapped and there's no outside investors. It's a good business. The issue is that I can see how verticalized AI will be leveraged in different parts of the deal cycle for private equity investors in particular. And I want us to be leading the charge on that so that we are more and more part of their operating system. And the way I kind of like in our business is almost like a service crew. Like if you think about like the International Space Station, are still people like, or shuttles kind of going there and dropping off food from time to time and things like that. That's kind of how I see How To SaaS as a business is you have the private equity universe where there are general partners or operating partners that are buying these companies and growing these companies. And we provide a very essential service to help them scale those investments on the value creation side as they're about to buy these companies because they don't want to necessarily hire a full on marketing expertise group inside their PE firm because it's too expensive. So we hedge that by having our core firm act as like an external operating partner on the marketing side that can be a tool in the toolbox of all of these other private equity firms. And part of that service or part of being that type of a service crew is developing tools that help the same end user in cases where they don't necessarily need us.

And if they're using other tools to kind of try to figure this out, but those are not meeting their actual needs, I see it as our responsibility to kind of try to fulfill that need. And one of the things that's kind of jump to my mind, especially in the last year as we've started to invest in our own AI platform is that if you think about the main deal stages that private equity investors kind of go through, you have LOI, you have closed, you have the first 100 days, and then there's like ongoing board meetings and things like that with an existing portfolio company. We are almost always brought in after LOI. So LOI signed or it's about to be signed. I often get a call from a PE investor saying, hey, I have this company that I'm about to put money into, we think marketing can be a lever. Can you help us figure out the potential during the diligence cycle? And so we will vet that during the diligence process. And then the deal closes and then we transition to a full engagement. And then we figure out the budget and the strategy and the people and everything and the roadmaps. And then we transition into a fractional CMO engagement. So that's kind of how it proceeds. But the issue is that pre-LOI, the same investors are meeting hundreds of companies. Most deals do not reach an LOI stage. And even the ones that reach LOI stage, not all of them close. So, but the issue is that pre-LOI, the willingness to spend on an outside diligence provider, like just kind of plummets because we don't know if we're going to win the deal, why would we invest in an external vendor to kind of help us, which is totally understandable. So we only find out when a deal is kind of imminent or it's kind of like confirmatory diligence. And now it's just about building the value creation plan and running as fast as possible, but the need still exists. And more and more firms are kind of hiring these general operating partners to help them try to sort out the value creation planning in the earlier deal cycles. And we've even had some podcast episodes about this where the deal teams and the operating partners are often talking to each other to figure out what the potential would be so that they can get a running start. But the issue is pre-LOI, the tools available in the market to be able to figure things out are minimal. Not only that, you don't have enough data. It's quite scarce. You have maybe access to a CIM, a basic data room. Maybe you get one or two calls with the management team. And oftentimes you may not even have all the things that I just described. But at the same time, you kind of need to build your internal IC memo. You might need to have an investment committee meeting. You're still building your investment thesis. You're trying to figure out, is this worth putting money into? What should the LOI look like? What should the valuation look like? What is fair here? What will help us generate a good enough ROIC to make it worth it for our LPs and the return for the fund and all those kinds of things. And the tools that are out there are kind of like these outside in analysis tools like SEMrush and Ahrefs and stuff like that on the marketing side, where they are kind of scraping data on the internet and kind of outputting something that investors don't have the sophistication to use. The data is not always accurate. The data needs to be transformed. There's a bunch of issues there. And then the option too is to kind of hire somebody like us for the diligence side, but it's too early for that and spending $25 to $50,000 on a diligence engagement, it just doesn't make sense at that early of a stage in the deal cycle. And then you don't have operating partners that have the specific expertise necessarily to do this work. I was talking to a major private equity firm a month ago and they were saying that they actually reduced the size of their ops team. It's a global PE firm because they first thought that they were going to hire specific experts in operating teams. But they realize that it's better to have generalists who are super smart and have a big Rolodex of external partners that they can call on because that ends up making their PE firm way more efficient. And I'm seeing more and more PE firms kind of do this.

And so you kind of have this gap where the analysis is required, but there's nothing to really address the need. Coming back to what we've been just talking about, which is verticalized AI solutions and where the opportunities are and things like that where I figured out that we have a huge advantage and a proprietary, process access to our own data set. That's kind of locked. It's a, it's a huge moat that we have in our businesses. We have been doing this for six years and we have seen thousands of companies work with hundreds of PE firms, seen all kinds of data points and we have our own proprietary process and how to help companies from all different sizes, different verticals, different sales cycles, different total addressable markets, different average contract values, et cetera. So we've kind of seen it all. And so based on that, we have a huge proprietary data set that we have built ourselves and we have mapped over 975 software categories, in the B2B technology space. And we have built out high level marketing analysis, for all of those categories. So what happens is you kind of go in and you put in basic fields. You put in things like your revenue, the software category you're operating in, year over year growth targets, your EBITDA percentage, your net revenue retention, what your marketing and sales budget is, what your lifetime value is, like five to 10 data points. And with that, our software, and we're calling it Inquisio, it outputs a very detailed marketing due diligence report that helps you see the marketing potential of a target investment completely outside in, but in a format that a private equity investor can analyze, can use for their internalized IC memo, can use it to forecast a value creation plan and start to operationalize things way earlier in the deal cycle. And it's worth mentioning that this data can also be used to completely benchmark existing portfolio companies to see are they in a healthy range, are they underspending, are they overspending, which area should they probably be spending more into, which area should they be spending less into. And it kind of goes along the lines of the vertical AI conversation that we've been having is that with that proprietary data set, the proprietary algorithm that we have built, we are uniquely positioned to create our own vertical AI platform. So that's what Inquisio is. It is our own data-driven AI slash LLM tool that allows private equity investors to perform marketing due diligence on investments in seconds. And it produces an output at a very high rate of fidelity. We've benchmarked this against a ton of companies. And if you were to try to do this work outside in, right, if you were to get an expert internally to do this work, it might take you two, three days, sometimes a week. If you try to do it outside and using tools, your fidelity rate might be like 10%. Within Inquisio, the fidelity of like what the output is versus what the actual items that that company needs to be working on, it's north of 80%, sometimes north of 90%. And we're getting better at this every single day. And that's only possible because we have been doing this work for six years and we have the data that we have and the clients that we've worked with and developed the process that we have and the people that we have and things like that.

And so we're about two weeks away from launch from this. And as I've been kind of building the marketing materials and the communication for this, more and more I've kind of been thinking about how this is actually the future because it's not just about what I'm telling you about Inquisio, but any firm like us, any software company, any B2B service, any tech enabled service that has access to data like this should be thinking about how to build a platform like Inquisio because every private equity investor that I have demoed this product to has said they are interested and we're going to be giving them a demo in a couple of weeks so that they have a free account to play around and all those things. And so if you have a unique data set and you have a proprietary process, odds are there is a vertical AI tool sitting inside your business that can scale your business tenfold.

And I'll kind of explain the business strategy behind why we did this and how you can kind of apply to any portfolio company or your respective company here as well. So, just taking a step back, right? Kind of how I've grown this business over the last six years is at some point I realized that the main KPI or the main metric that we need to move to grow this business is to meet more private equity firms. When I initially started the company, I thought it was to meet more software founders. And very quickly I realized because when we sold the last company to private equity and the PE firm started pulling me into the other investments, I realized that the big, big differentiator or way to get into multiple companies is to partner with a PE firm because they're already making the investments into the ICP of the types of software companies we want to help. So we build inroads with them and we really support them and be a true partner to them. They will introduce us and that will become our reoccurring revenue over time. And so, and by the way, this is just a sidebar. Like I think most companies need to think about this because in every company there's really one or two entirely in most companies, 10 million, 100 million. There's usually one or two key metrics or North Star metrics that if you move and you have all your activities kind of zero in on, it changes the whole business. And for us, it's meeting more private equity investors in whatever business you're thinking about. It might be something else, but it's usually only one or two things. And this is where kind of distraction is the enemy because when businesses start to get more complicated, we ourselves make things way more complicated than they are. But in reality, what really moves the needle is one or two things.

And so as I kind of realized that private equity is that main entry point for us, I focused our entire go-to-market on that. And kind of how we've done that is like I've written two books. The first book is titled Post-Acquisition Marketing. The second one is titled Exit-Ready Marketing. And I could have written all kinds of marketing books, but I specifically focused on these because that is our ICP. I want founders who are ready to exit to read it. I want PE investors that are buying companies to read it and it's written for their use case so they can create more enterprise value for their companies. Number two, we run this podcast. We are a marketing services firm for private equity, but we built the podcast called the Private Equity Value Creation Podcast because it's not just about what we sell, it's about the problems of the ICP that we are serving. So we serve the private equity market. They have all kinds of value creation problems when it relates to pricing, M&A, organizational design, hiring, leadership. Everything else beyond just go to market sales process, right? And marketing is just one of those levers. If I just keep beating the drum on marketing, marketing, marketing, I'm going to lose interest from the ICP very quickly. Instead, it's more like I am entrusted with a responsibility to help this market, kind of like the service crew analogy that I gave earlier. And so I will try to serve that market in all the different ways possible or available to me to help this market. And so that's why we have this podcast where we bring on service providers that sell other things to private equity, bring all kinds of private equity firms so they can share best practices. We've created a stage here and we get thousands of downloads a month because we have built that stage and all kinds of private equity firms are coming on and sharing their story. And even this episode, right, it's a little bit about Inquisio, but I'm talking about a way to help you grow any company from a general and vertical AI standpoint. So it's not just a self-serving piece of content, genuinely trying to help folks as they kind of try to think about AI as an avenue for growth. And then I also speak at private equity conferences and publish content daily on LinkedIn and things like that.

One of the biggest challenges is that meeting more private equity firms takes time. You have to build relationships. You have to actually build trust. It's very important to get a pilot engagement under your belt before they kind of make you part of their ongoing standard operating procedure. And so that's the work that we do. We do the business development. We do the podcast. We do the relationship building, the account management, and all of those things. The reason I'm sharing all this is connecting it back to this idea of how can vertical AI tools help companies grow when there's this risk of cannibalizing the core business. So for us, let's say there are a hundred deals in the private equity market at any given time, we find out about maybe five of them, let's say 5%, because it's based on the relationships that we have and it's based on who is paying us for a particular deal that they're working on today. But every month there's enough business for us to grow and kind of serve but it's not to the point where I'm aware of all the deals that every private equity firm is kind of working on. But Inquisio changes this for us because it creates a low cost alternative for the private equity investor to self-serve their way through a high level diligence process and be able to get an output that helps them figure out what is the potential or value creation plan for a target investment. And then in the product, as they kind of get these answers, there's going to be a schedule a consult button where they can then set up a demo and decide to work with us in an actual full service type of engagement model or full diligence model or a fractional CMO model. So with this, the expectation is even firms that don't work with us today, like we know beyond like a few hundred firms, right? But I would say in terms of client firms, we have like 70 to a hundred firms that we've actually done business with, but there are hundreds of private equity firms globally. So I want Inquisio to be part of the OS of all of those firms. And then as it's part of the OS and analysts use it or operating partners use it or partners use it, whoever's using this tool, we will get exposed to all the different deals that are active in a market at any given time. And then that kind of becomes our sales pipeline to kind of grow the consulting business over time, like creating more value or upselling services on top of the data that they're getting back from the software. But it's all done in a way where we are starting with value first. We're giving insane value that what would be inside of an Inquisio diligence report. We would normally historically like five years ago, I would have charged $25 to 50K for that report. And now we like raised the bar significantly for what the type of output you can get from just a tool on a self-serve basis. And the cost through Inquisio is a hundred dollars per report. So you can see it's like a 100X decrease in the value that you get. And it kind of wipes out a ton of diligence providers that would actually charge for that service. But on the back end of that, there's a bunch of things that I am confident that we can do that no tool can do because they're very complicated, go to market or organizational or financial problems that it requires a human to solve that we can then come in and step in and actually charge a regular engagement fee and partner with a private equity firm to actually be able to do that.

And so the reason I'm sharing this is that, while at face value, can look like it's cannibalizing the front end of the business, it presents a massive growth opportunity because it expands our TAM from firms that are kind of like going to ping us about the deals that they're working on to basically everybody that's going to make it part of their standard operating procedure as they diligence firms and think about commercial diligence and things like that. So as you're thinking about the companies that you have where vertical AI solutions can play a role, that's kind of how you have to think about it. It's like, it's a way to get in the door with the ICP. And one of the ways, like one of the reasons why I invested into Inquisio, and we've at this point put in about a half a million dollars into this tool in terms of people and time and other investments in terms of development. So, is that, one of the reasons I did this is that it occurred to me that, you know, how we've been doing demand gen, and this is something that doesn't get talked about enough, but it's a, think it's a very important idea is that there are levels of demand gen. So if we were to, let's say, create a Google ad for a company, that's like level one. Because anybody, the barrier to entry to launch a Google ad is actually not that high. Doesn't mean you'll get a ton of results from it if you don't know what you're doing, but you can launch a Google ad, you can direct it to a landing page, you can generate some leads, and it might make some sort of a dent. The next level might be some piece of content. It might be an SEO article, it might be a white paper you write, et cetera, et cetera. That's also great, and it gets you people that are more interested in what you're offering. A step above that is thought leadership. This could be like being on a podcast, it could be speaking at an event, it could be writing a book, and that kind of starts to give you this halo effect that affects all other channels because if you have a podcast that really hits the mark on what your ICP cares about, they're gonna ping you for other services and try to work with you in different ways as well. And I used to think that thought leadership is the highest form of lead gen because, or demand gen, because you're servicing the customer, you're acting in their best interest, you're providing them with a ton of value and that builds trust and sales is all about trust. So like, you know, for me, that's why I wrote the books is, it's, I'm building a ton of trust and giving a ton of value to this audience. But there's one higher form and that is product. And even though, you know, you've been in the SaaS world and been building companies in this space for so long, it's just one of those things that It's like kind of like right in front of me, but it's not been there. It's like not realizing that it's there is that if you have, for example, if you were to look at most software companies that offer a free trial, the number one source of revenue for any form that they have on their website will be the free trial form, which seems obvious because obviously it's more qualified than somebody who downloads a white paper. But still the point is that the main offer, being product, generates more revenue. And so, and the reason for that is that product has recurring value. You can only read a book once, you can only listen to a podcast once, you can only watch a YouTube video once. And you have to keep producing assets on all of those things to get continued viewership or continued listenership or keep the audience or retain the audience over time. But with product, if you get a user into the product, you're going to get recurring value out of that over time. So Inquisio as an example, if we convince a private equity investor that doing diligence through Inquisio, is a great thing for them, they will most likely use it on a recurring basis as part of their standard operations. And I don't have to keep producing more stuff to get them to use it. I just have to make sure that the algorithm and the outputs inside the tool improve over time, but they're already inside the tool. So the logic or the business strategy to create verticalized AI tools is, yes, you're cannibalizing some things, but you're opening up the door to your ICP to engage and interact with you on a recurring basis with one core offer that takes a ton of effort to stand up initially, but the recurring value and the enterprise value of that is basically infinite. And so as you kind of think about it from that standpoint, it starts to become more of a no-brainer investment.

So that's kind of what I wanted to share with you guys today. It's a long episode and as a solo episode, but there's a ton of great concepts that I hope you take a lot away from. And I'm also happy to chat about it more. You can always ping us at howtosass or ping me by email to reach out to me to discuss it more. And if you are interested in Inquisio as a platform and you want to learn more about it or you'd love to get access to it, be sure to keep listening to the podcast. We're going to release more in the next couple of weeks to give you guys a heads up on what's coming. And also just ping us and just say, hey, we're interested because the forms aren't live yet and the website's not live yet. We're about a couple of weeks away from launching. So we'll definitely be hitting you guys up with that and giving access to all the private equity listeners that listen to the podcast.

So with that said, I hope you enjoyed it and please be sure to leave a review or send me some thoughts and comments based on the content you heard today. Thanks a lot and I'll see you guys next time.