How Paligo Grew to $11.3M in 2024 Revenue and 500 Enterprise Customers Before Its AI-Native Pivot (Interview with CEO Rahul Yadav)

Introduction and Paligo Overview

Nathan Latka

0:00Hey folks, my guest today is Rahul Yadav. He became the CEO of Paligo, that's P-A-L-I-G-O dot net in December of 2025 after serving as CTO at Milestone Systems, that's with Canon Group and Chief Technology and Digital Officer at TV2 Denmark. He holds an Executive MBA from INSEAD and has 20 plus years leading technology organizations across enterprise, SaaS, media, and even consumer electronics. Rahul, you ready to take us to the top?

Rahul Yadav

0:27Absolutely.

Nathan Latka

0:28All right. So tell us first, if folks haven't heard of Paligo, what does the company historically do? You're bringing change, but historically, what does it do?

What Paligo Does: CCMS and Structured Authoring

Rahul Yadav

0:36Yeah, so I think first of all, great to be here, Nathan, and thanks for having the time to talk to me here. So what Paligo does is Paligo is born out of a need of where, when enterprise need structured documentation, especially when you have lot of documentation where complexity is very high. Paligo has devised this tool called, in this category called CCMS, Component Content Management System, and basically is a tool for technical writers to really make life easy in terms of documentation. That means it's easy to create, it's easy to reuse content, easy to publish and then we offer 30 different integration points to two different kind of you know CDP, know knowledge bases but also you know some of the tools like Zendesk, Intercom and the likes. So it's a structure authoring tool and I think we have a lot of success in last 10 years and we have roughly ⁓ nearly 500 enterprise customers across 38 countries.

Nathan Latka

1:41What's enterprise mean? What's the average customer paying you per year?

Average Contract Value and Customer Pricing

Rahul Yadav

1:45Yeah, so the customers, mean, you on average, I mean, we have a deal size anything from 25K per year to anything, you know, all the way to 150K USD per year. So that's enterprise.

Nathan Latka

1:58What? You have folks paying you today. Your biggest customers are at $150,000 a year.

Rahul Yadav

2:04⁓ Yes, something like that. And some are also bigger than that, but on average, that's the ballpark. Simply because we are seat-based, right?

Nathan Latka

2:08What would you, how big is a space though? Like what would you say? I mean, do you have any million dollar customers yet or no?

Rahul Yadav

2:14No video at all.

Nathan Latka

2:15Okay, okay, okay. So this is helpful. We sort of know your price point. We sort of know what you deliver here just to make this really land for me. So sometimes I'll go to a website or my tech team will go to a company's website and we need like really clear API documentation so we can build a connector or a webhook, et cetera. Are you sort of the backend of how that company that we're trying to integrate with might manage all their API docs to help me with support and onboarding to their webhooks and APIs?

Rahul Yadav

2:39That could be one of those as well. So yes, so we have a lot of those customers who do API documentation, but also especially like, you know, if you're a manufacturing customer, for example, where you have multiple products in your portfolio, you write documentation in different languages, and the complexity goes so high that you need a tool to really structure. I mean, you know, we come into play when companies started to find the tools like Word doc, Google doc, SharePoint, any kind of Wiki. When they hit the roof, you actually come to a tool where you write in this structured authoring format called DocBook XML and then you really simplify and then enable reuse.

Founder Transition and How Rahul Became CEO

Nathan Latka

3:15Okay, let's get more of your backstory. Usually I have founders on the show, but as we all know, every business is not perfect. And many times even co-founders leave early, even the founders sometimes leave early. So you are not the original founder. Help my audience understand how you ended up leading Paligo as CEO.

Rahul Yadav

3:29Yeah, so actually this is a perfect example of founder transition. So before me, we have the founder, Anders and Frank, who are still in the company, by the way. Anders has taken a role ⁓ of a board member, and then Frank is an architect in one of my engineering teams. So essentially what happened is they have created this product called Paligo that you see today, and they really saw the need together with the investors, saw the need of bringing an external CEO coming in. especially because they could really see a potential of this company, what this company could be, especially around AI and what has happened in the world of AI in the last one and half year. So they wanted someone seasoned who really understand product, someone who understand technology, someone who understands scaling product and engineering, but also business. And that's why we are owned by these founders, but also owned by a very leading B2B SaaS private equity, Alfvén & Didrikson in Stockholm. and then we have GRO Capital from Copenhagen. So together with this, we formed these three sets of owner for the company and they really wanted someone to come in from a product and tech background to really take the company to the next level.

Capital Stack and Ownership Structure

Nathan Latka

4:42the capital stack here is an important story. it fair to say that GRO Capital, I believe they did a 20, they led the 29 million dollar round in April of 2023. Are they the majority holder today?

Rahul Yadav

4:54Actually, the stakes are more or less all the three groups. So the founder group, Alfvén & Didrikson, another P, and then GRO. I think they nearly own equal parts, so 33 % each approximately.

Nathan Latka

5:08okay, great. Got it. Okay, so was this a story where, you know, they put in a bunch of money, right? I don't think you've raised since you've become CEO, but they put in a bunch of money. The founders sort of couldn't figure out how to transition and they needed something sort of just come in and, you know, create a new vision going forward. I mean, it's not to say that they forced the founders out, but did everyone sort of agree you needed a new path to drive faster revenue growth? And that's what led to your your arrival?

Rahul Yadav

5:30Absolutely. So I don't think they forced the founder out actually and I would even say that founder is still in the company and actually I have some great conversations, ongoing conversation with the founder. So this is not the story of the investors not liking the founder and they want someone from outside but actually together with the founder they realized they need someone with a fresh mindset outside the industry coming in who has done this kind of product and tech transformation and then really leverage AI for the benefit of the company.

Revenue Growth History and 2026 Targets

Nathan Latka

5:58I want to talk more about your go forward plans with Paligo Labs and how you're thinking about AI. Before we do that though, you joined in December of 2025, which is obviously very recent. Can you help me understand the growth story though, right prior to your arrival and your first sort of couple months? What was the sort of revenue growth rate from 2024 to 2025 and what's your target here in 2026?

Rahul Yadav

6:19Yeah, absolutely. So I think some of this, Nathan, is also confidential, so I will not be able to share a lot of things openly, but at least, I mean, the numbers that you can find yourself online, I will be happy to share. So for example, the growth from 2024 to 2025, we were shy of 11 % ARR growth. ⁓ And what we're looking at is at least ⁓ around 18 to 20 % growth this year. But this is again, assuming our current product with the strategy, before what I developed for the strategy, right? So actually my ambitions for the coming years is very, high. I really, didn't join this company to deliver a 15 or 20 % growth. I joined this to really do an exponential stuff what is possible with what Paligo can offer. And that's what excites me.

Nathan Latka

7:05Yep. I won't make you say this because I don't want to get you in trouble with anything, but my research team is very good. So we use public data sources because you're based in Sweden. The company has to file with the government. So a lot of this is public. I'll share that story and then we'll focus on the go forward strategy. So 2020, according to publicly filed documentation, the company generated total revenue of $2.2 million. That's converted and burned about 700 K. They grew 78 % up to 2021, hitting 3.9 million top line burning about 2 million that year grew another 70 % in 2022, breaking $6.8 million, burning about 3.1 million. Then 2023, they grew another 46%. Remember, this is the year they raised all that external capital, but they grew to 9.7 million top line but burned a ton 5.1 million. Then in 2024, they kept growing nice growth 10 % up to 11.3 million of revenue and then picking from 2024. Rahul just shared they grew another 11 % so you guys can do the math there targeting 20 % growth this year. Rahul any comments on that generally? we correct here?

Rahul Yadav

8:06Yeah, you are absolutely correct. And I think, as I said, the target is shy of 20 % this year. But as I said, when I look into year 2027 and onward, my ambitions are much higher.

AI-Native Strategy vs. AI-First Bolt-On Approach

Nathan Latka

8:17So let's talk about that. How is a legacy software company where the growth has slowed and the investors are going, Rahul, we put money in, you gotta turn this boat around. What are you doing to take advantage of AI?

Rahul Yadav

8:29Yeah, absolutely. I think the natural thing any CEO in my position will think about, hey, let me bolt on different features on top of the product and then let's try to sell out of it. And this is what the market keeps on pushing you for, right? And actually we have taken a very different approach there. So my approach has been, you know, we went back to the drawing board as soon as I started, even actually prior to I started, I would say, to look into, really look into, can we reimagine this company in a very different way? Yes, we have the existing customer, who are actually by the way, our user love us, right? And you can actually see our NPS score and our feedback on trust radius and all is quite high, right? Simply because the tech writers who are our primary user, they love us. Simply because the product is very easy to use, a collaborative product for structural authoring. Now, the... So this is sort of one of the things, one of the many modes that I can think of that we had. But other than that, when we start to think about, let's rematch in the company what this could be. Look, what has happened in last two years, right? AI and agentics came in, right? And then everyone talks about AI as if that you're gonna just fit in some features on top of the existing product. And surprisingly, you're gonna hit a jackpot. That's not how it works actually, right? So what we looked at is that in an AI native world, not in AI first world, So actually, just wanted to, for your listeners, wanted to, at least the way I create this, how I differentiate this is you have a traditional SaaS company, then there are companies who do AI first, meaning that they bolt on AI features on top of your existing product. I actually call it a lipstick on a pig model. Your product is still old, clunky, with tech debt, with old UI, and then you bolt on different features. You have AI assistant, you can have this and that, and then you have that, and then you try to figure out how do you actually monetize that. And most of the companies today are actually struggling with that simply because the features that you put on bolting it on top of it what's happening is that you are you know basically it's difficult to monetize because it the table stakes for the for the user and then what I call AI native is that whatever mode whatever DNA you have the company can you really translate this into not only your existing users but maybe create a new users and new buyers and that's exactly what we did so what we looked at is that if we have 10 years of experience DNA legacy of structure authoring to structure content for human consumption. One of the biggest assumption we had as a part of the strategy is that, do we really think that it's going to be human who's going to read the documentation? Absolutely no. It's going to be agents reading the document, right? So we said, if that is true, the structure content powering any kind of agentic is going to become very more important than ever. So what we said that, you know what, why don't we actually move in addition to what we did today? to structure content and create content and provide an end-to-end structured truth platform which can power agentics. Because you know, one of the biggest things you see now today is 85 % of the companies, enterprise companies, are struggling to deploy AI agents in production. Why? Because your AI hallucinates. And why it's hallucinating is not because of the model. think models are very, very sophisticated and they're getting better by the day. What's happening is, especially the enterprise content is still unstructured. What if Paligo could take that role of really creating that, you know, moving that unstructured content into structure and then fueling agentics for enterprises?

The Data Moat: 3 Billion Words in DocBook XML

Nathan Latka

11:53Well, why are you uniquely positioned to structure historical unstructured enterprise data? Can you quantify it somehow? The number of documents you've processed over 10 years, the number of words that these folks have written in your platform over 10 years, some ETL process you're running on data lakes, right? Help me understand that.

Rahul Yadav

12:12Absolutely. actually, so just a small nuance when I say data, it's content, right? So because this is content which is basically text-based. We have processed approximately in our content corpus more than three billion words, which is enterprise-grade. Not only processed, each of these word is governed, has semantic meaning, has relationships, and is version control. And you can track back. to the source, meaning that you can also develop a relationship between that. And just to give you perspective, so that's there, right? Then you have approximately out of those words, you have approximately less than 1.5 million topics or components that has been written inside Paligo.

Nathan Latka

12:57What does that mean? That's a little more vague to me. Give me an example.

Rahul Yadav

12:59Yeah, what happens is that remember to create meaning you have sets of words you write to create a component and that component inside Paligo could be reusable in different contexts. So for example, if you have a product A and a product B, it could be that the piece of document you write could be reusable in your two products. But the way we create content in structure authoring in Paligo is using these components. That means we have out of those 3.2 billion words, you actually have roughly less than 1.5 million topics or component that are reusable and they're governed and you can trace back. Auditability is there and you can also provide provenance, which is a big deal actually in the agentic world.

Nathan Latka

13:43We're investing out of my fund at Founder Path and a borrower says, Nathan, we are hedged against AI because we have a data moat. I asked the same questions I just asked you. And if I don't get an answer like what you just gave, we generally will not invest. When we get a great answer like you just gave, we try and dig deeper to understand, is your data set the largest in your space? Because usually if it's structured data, it's a direct correlation that you will win this space because you have the best brain. Right, and you're gonna enable your users to use the brain they've built on you, which is the most powerful brain. So the question is, are three billion words over 10 years and 1.5 million components the largest data set that you know of in this space with this context?

Rahul Yadav

14:24Yeah, so remember, this is where we need to probably have, again, a little bit more nuance, right? So this is B2B space, ⁓ especially in one of our largest customer set we have, is regulated industries where accuracy requirements are very high for the AI, right? So when you have manufacturing, when you have life science, when you have med tech customers, If you look at that industry using documentation, then the data side is good. Now, other thing which we are doing to actually...

Nathan Latka

14:50Is it good or is it the best? Is it the largest data set for regulated industries like medtech and manufacturing that you know of in the B2B space?

Rahul Yadav

14:57I think if it's not the largest, it could be very close to the largest, would say. So that's one.

Nathan Latka

15:02Okay, and if it's not the number one provider, you still feel like it is, you know, N equals, it is a representative sample size where you can program against it. There is embedded intelligence you can pull from it.

Rahul Yadav

15:12Correct. And then actually there is another nuance there is no one. So I can say that yes, it is the largest in the format that we use, the standard we use called, it's called DocBook XML. DocBook XML, you can actually look this up. DocBook XML is one of the best format in terms of semantic depth. So actually if I look into, if it's one of the largest data set, in a regulated industry that is based on DocBook XML, the answer is absolutely yes. And there are other formats like for example most of the companies out there, they do markdown, they do JSON, they do DITA. None of them is as good, especially in terms of semantic data, and that's what is needed.

Nathan Latka

15:59Why? Because DocBook XML has the best structure. Those other ones you just articulated are not structured.

Rahul Yadav

16:05Yeah, they are structured but especially when you want to create meaning out of content, when you want to create semantic understanding, version control, all of that, DocBook format is actually superior.

Nathan Latka

16:18And so one of the things I would do, right, if I was underwriting right now looking at to invest, know, DocBook, XML, I'd say, can we acquire that business, the massive data set? answer is no, it's an open standard maintained by Oasis, right, a nonprofit standards body. If I went to Oasis and saw like the top users of DocBook, you know, in terms of the companies, the API connections, the commits, whatever, would Paligo be at the top of the list?

Rahul Yadav

16:39Yeah, I think so. mean, you know, again, remember that that format is also used in different contexts as well. So, you know, so it depends on how do you use it actually, but especially in the structure authoring product and tech documentation space, yes, Paligo should be on the top of the list.

New AI Products: Translation and In-Context Assistant

Nathan Latka

16:55Interesting. Okay, so if the audience listening right now believes everything you're saying, which it does seem like a representative data set, you do have real revenue history, you do have customers that love you in the enterprise space that are regulated industries like medical tech and manufacturing. What are the new sorts of AI tools you are building for a manufacturer that has used you for the past six years? How are you helping them get jobs done?

Rahul Yadav

17:14Yeah, so especially for our current product, what we have already done actually just the last quarter, end of March, we released something called AI Translation. So translation is one of the biggest cost drivers in some of these companies. That means translation.

Nathan Latka

17:27Ro, let me pull this up while you talk about it. I'm gonna pull up your website. Do you have this on your website anywhere yet?

Rahul Yadav

17:32Yes, we should have it. So if you actually go to, I mean, you can probably just Google Paligo AI Translation and you'll find that one. Paligo.net AI Translation.

Nathan Latka

17:34What should I click? Okay. This one.

Rahul Yadav

17:47Yeah. Actually, the myth.

Nathan Latka

17:49Okay, so this is a new product that you've spearheaded since you joined in December.

Rahul Yadav

17:53Yeah, I think, no, if you go back, actually one more back, the second link, if you click that one. Yeah, I think this is not, this is actually, if you go back, this is not the one as well. We should have actually, focus on, please go translate one theme. There should be, we actually have released, and I think it should be there on the website. Otherwise, I can send you the link here.

Nathan Latka

18:13Okay send it afterwards, we'll edit it in post-production, but describe to us how it works.

Rahul Yadav

18:17So the way it works is that, see, historically what you need to do, especially in your workflows, you need to write content, you need to export in a traditional world, you need to export your content written in English, you send it to a translation management system, you wait for weeks, and then translation comes in. By the time the translated version comes in, your English version might have evolved. Then you actually have a challenge of, again, reconciling it. What we could do now in...

Nathan Latka

18:43change management basically.

Rahul Yadav

18:45Correct, change management, also the, remember, everyone is trying to develop product and release product in real time. That means the cycle times are very, very short. If you have that, and if you need to wait for a couple of weeks to translate. I mean, know, are really, the whole your CI CD, your continuous delivery actually is dependent on translation. What we have done now is that we have created AI translation within the product. So essentially, more, the moment you write content, you can actually use within through AI translation. You can translate in the product and then you can move on. And there are two advantage. One is time, that you can essentially get instant translation. And then the second thing is the of translation is fraction of what you pay to the TMS systems today. So this is one example. Then what we also are big on, especially on the top of the product, we are also embedding AI. So we have AI translation now. Sorry, AI translation is one. Then we also have AI assistant for content creation. But the way we do content creation also is in context, meaning that we don't write a prompt. inside the product and then you create a long content slot. What you do is that you basically do on a component level again. So it's really, really in context AI that we are rolling it out. So that's on the current product. But I think, let me just expand a little bit. So what we are trying to do for those customers in the future, Paligo will offer not only structure authoring, which is what we have been doing for last 10 years. I actually call it, we will deliver as a part of the strategy now, we will deliver everything from ingestion to consumption and consumption could happen by AI but also by human meaning that ingestion, authoring, publishing but also not only publishing but also hosting, and then answering your agent that content and then also grounding it as well to make sure that the answer the agent provide it is grounded in structured truth which sits inside Paligo. No one actually and that's a category in itself and we are creating that category no one else has done this in the industry so far. Everyone provide this point solution across this valuation.

Nathan Latka

20:59ability to win the market space that you just articulated is directly correlated to your ability to enable your customers to give you as much of their data as possible so that when you write and try and guess what they need to write you have as much of their brain as possible. The good way to measure that is how robust are your integrations. So when people tell me, ⁓ we ingest, we're the brain for our customer, I say you don't even have an integrations page, you offer no integrations. For you, know, I'm spot checking this as we go along, you've got a robust integrations page. How much of your engineering resources are going towards making sure you're structuring ingestion data in a way that can talk to other data you've already normalized in your system so you can use AI against it and so agents in the future can talk to the ingested data.

Rahul Yadav

21:40Yeah, absolutely. So what we have done is we actually have a dedicated team which actually helps with what we call content migration, right? So whenever we acquire a new customer, we actually help them to migrate. But that's not all. We are not stopping there, Nathan. And so what we are doing now is we are, as we speak, we are creating an AI-powered ingestion. Any kind of data that you're going to throw at Paligo, we will structure it into the DocBook XML. We will still have, for the last mile, we will still have human in the loop. That means our tech writers. they will evolve their role from tech writers to content curator, content orchestrator, so that when we ingest data, any format that you'll throw at Paligo through this AI-powered ingestion, we will make sure that it is structured, it is enriched with metadata, it has all the semantic understanding and the semantic depth, and then once we store it, then actually we can put that data to use.

Nathan Latka

22:33It's a great lesson for all of you watching. Again, if you are not investing and enabling your software to talk to other softwares with your own integrations, you are behind. You gotta get moving. Like if you can't talk to other stuff, you're screwed because in the future, to our lowest point, it's gonna be agents talking to each other, right? And if they call you and there's no telephone on the other side, you can't even take the call. So Rahul, this makes a ton of sense. Let me ask you about pricing. As you pivot to all these new product structures, are you changing your pricing dramatically or no?

Pricing Evolution: Seat-Based to Usage and Outcome-Based

Rahul Yadav

23:00Yeah, so we have ⁓ quite a big project internally actually looking into. So yes, we are changing, we're going to change the price as the new products comes in, as I said, and we will talk about it in the Paligo Labs in a bit. ⁓ as we evolve the product, we will move, know, so today we are seat-based pricing. We will evolve the pricing to, ⁓ especially for the current product. Of course, you know, we need to make sure that we maintain our current customer so that they actually get more advantage of the money they pay for today. But on top of it, we will move to usage-based pricing and then further down we will also move to an outcome-based pricing and we are evolving that as we speak simply for the

Nathan Latka

23:36I know if your future is directly correlated to a new customer giving you their full brain, but you make them pay more to integrate Salesforce, that actually hurts your growth prospects by gating that with a pricing plan. Why do you gate that? Don't you want them to use as many integrations as possible for free?

Rahul Yadav

23:53Yes, so it depends also on where you are. So when you, the picture that you're seeing now or the website you're seeing now, this is actually an integration on the output side, not on the input side here, right? So this is the integration we do where we publish out of Paligo to Zendesk, to Salesforce and all of that. So that's our current product. For what I would actually like is on the input side, that's what you're saying. My goal is input side, it will be, I will offer it everything for free. Right?

Nathan Latka

24:03Okay.

Rahul Yadav

24:22would like to remove that barrier to entry.

Team Size and Replacing SDRs with AI Agent Sophia

Nathan Latka

24:25What is your team size today? How many full-time folks?

Rahul Yadav

24:28So we have 87 people on the payroll today. I just want to say, I mean, engineering focused a team of roughly 32 people.

Nathan Latka

24:31How many are engineering focused? And what's the rest? Do you have like quota carrying reps or what do they look like?

Rahul Yadav

24:41We have quota carrying reps. have we have have GNA of course the you know the finance a few people in finance HR, but then we also have We have customer success team. We have professional services We have tech support and then of course we have a contact is a good event, know and then what we have done is This is again a story that I'm gonna share it at the SaaSiest But but actually we have converted all our BDRs and SDR We have none now and we have completely deployed you see this picture right there, right? We have converted and we actually hired an AI agent called Sophia. She's our BDR, she's our AISDR and product specialist, which is powered by Paligo.

Sophia's Results: 300 Conversations and 15 Meetings

Nathan Latka

25:17Is it working and what technology are you using to do this? Did you build it custom or is it a third party?

Rahul Yadav

25:23So it's a third party, of course it's trained in Paligo data. So it is working. She has 300 conversations so far with the real customers. ⁓ And she has booked 15 meetings for us so far. Out of this, 13 is new logos and two with existing customers.

Nathan Latka

25:42Anyone else that are, yeah, I was just gonna say anyone watching this that wants to do something like Sophia on your website, like one of maybe your customers or a whole, Sophia's gonna be really dumb if you don't have structured data to feed her, to train her. I mean, that to me, Rahul, is another advantage of Paligo, right? Any of your customers can launch new AI tools very quickly because you help them keep their brain organized.

Structured Data Advantage for AI Agents and Token Costs

Rahul Yadav

26:07Absolutely. And this is exactly when I say that Paligo will serve not only humans in the future, but agent. This is exactly what I'm saying. Any kind of agentic solution in any space that you're looking at, you need structured data, structured content. And I can tell you if someone is trying to sell you that, Hey, you know what? You can clean data retrospectively on the downstream side using they call retrieval augmented generation. are multiple forms, know, types of that. You have agentic rag, have hybrid rag, you have vector. rag, all of that. I think you know what, you will never be able to achieve the accuracy if your data is purified at the source, that means in the process of structured authoring, right? And that's one thing. And second thing is all of these agents that you're using, they are token hungry and you know, ⁓ it's always a start to hit your PNL, right? So if you have structured content, you will, we have, I mean, we have benchmark internally, we can do the same query we can resolve the same query for fraction of the cost with rag without unstructured data what says Paligo structured data you can actually solve the same query with much higher accuracy but for a fraction of the token price and this is again

Nathan Latka

27:24Do you do, when one of your customers wants to get something done with your platform, do you enable sort of tool calling to any foundation model, including some of the open source ones on Hugging Face with cheaper per credit prices?

Rahul Yadav

27:38So what we are talking about Nathan is this is something that we are building as we go. So we don't have that offering today that I'm talking about. My goal is that eventually we would like it to be model agnostic. So from a technology choice right now, that's why we have chosen is that especially for Paligo Labs what we're trying to build, we're using AWS Bedrock so that customer can choose the model. So you can bring your own model there.

Nathan Latka

28:01I see, perfect. And who are you using for Sophia? What's that company? Okay, great. So I had Amanda on the show recently. mean, her pricing is not cheap. I mean, she told me publicly she's charging groups like HubSpot 90 to 100 grand per year to launch each of these agents. Are you in that same range? Okay, and is it an ROI positive investment for you so far or no?

ROI of Sophia and Go-to-Market Engineer Model

Rahul Yadav

28:05So it's 1mind. So see, activated Sophia less than two months ago, right? But I if I look into just purely from moving five team of five BDRs. So, and then making them, four of them making them ⁓ account executives, so they close deals, right? And then one, a go-to-market engineer who actually orchestrate ⁓ Sophia and the other agents we have. Actually, that is already a positive business case for me, even at this stage.

Nathan Latka

28:53Very, very interesting. All right, hey, on that note, Rahul, if people wanna follow your story as you scale, where can they find you online?

Where to Follow Rahul Yadav

Rahul Yadav

29:02I think the best way to find me online is connect to me on LinkedIn. I am quite active. I really believe in building in public. So everything that I've said in this podcast, but also everything that we are doing in Paligo, I'm really sharing openly simply because I don't think as the world moves toward AI native, I don't think everyone has a predetermined recipe to actually do that transformation. So what I'm doing is basically I'm doing a build, measure, learn here. ⁓ If you really, whatever we have spoken today, if this resonates with you, I'm happy to get connected, happy to have a chat because I'm also to learning as we go with this.

Nathan Latka

29:41Folks, Paligo.net launched by two great founders in 2020, that's Anders and Frank, at 2 million bucks of revenue in year one, scaling by 2022 to 7 million of revenue. And by 2024, over 11 million of revenue according to government filings. They'd raised significant capital and ultimately brought Rahul in in 2025 as CEO to focus on how they should grow in the age of AI. In my opinion, their biggest asset, over three billion documents or three billion words, pardon me, that have been created on their platform using the open source doc book XML model, specifically in medical tech, manufacturing and regulated industries. This is structured data. are now training their models on so that they can offer an AI product back to their customers. That is really smart. That can get work done for these brands. The thesis is if they have the best brain in the space with the most structured data, they'll be able to produce the best results for their customers. And that is ultimately what Paligo labs is doing on a go forward basis roles targeting 20 % revenue growth here in 2026. We'll see what happens as there as he builds with his team of 87 folks and 30 engineers. Rahul, thank you for taking us to the top.

Rahul Yadav

30:46Perfect. Thanks a lot, Nathan. Great speaking to you.