SaaStr 837: 10 Things To Do Right Now to Become AI Native with Filevine's CEO & Founder

0:01Welcome to the official Sastr podcast where you can hear some of the best Sastr speakers. This is where the cloud meets. Up today on the Saster Podcast. I had an engineer say to me just a few months ago, with a ton of pride, mind you, we have built an incredible SaaS application that makes tons of money, grows fast, customers never leave it. FileMind has almost 96 % gross revenue retention, 124 % net revenue retention. He has every reason to be prideful of the application he's built. And he said, and the great news is now we get to sprinkle AI on top. That is fundamentally incorrect. We all know in this room that if you are going to build an AI application today, you can't simply say, hey, we've got all these APIs.

0:46OpenAI has APIs. Done. We'll connect them. We've got an AI application. That is not going to cut it in 2026. To be AI native, you actually have to change the architecture of your system. It has to flip.

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2:17See you there. Saster Annual and AI Summit 2026. It will rock. Thanks so much for being here. My name is Ryan Anderson. I am the co-founder and CEO of a company called FileMine. We are an AI legal company, and I'm going to talk to you about how we became AI. We have 6 ,000 customers, 700 employees, growing extremely fast. We will be kind of well above$200 million in ARR this year, growing above 50%, close to 60%. but we have successfully now made the transition to an AI native company. And how can I tell you that? Well, it's in the numbers. It is very plain to see that the numbers back up, that we are now doing far more revenue on a new quarter by quarter basis in AI products than in our SaaS product.

3:13Now, that's not to say that the SaaS product is in any way less successful. And in fact, it's still growing. The SaaS product itself is growing at 35, 40 % year over year. We are just growing so much faster on the AI side of the house. So the question for all of us in this room that run SaaS companies, that are leaders at SaaS companies, is sort of, how did we do this? And how should we all continue to go AI native? It is a very tough transition. And I'll tell you, the trick, number one, and the hardest part, is that nothing is sacred. Let me tell you what I mean. You all have teams that have built incredible things.

3:54They are justifiably so proud of what they have built. How could they not be? You have products that are working today that bring in a lot of money, and you are going to have to have conversations with your leaders about tearing down what they have built. Literally large components of your code base, not all of it, of course, but meaningful components of your code base that work today, that make you money, will have to be torn down. Now, the trick, of course, is that not everything needs to be torn down, only the things that make sense. So deciding what should be torn down and what should not be torn down is a big deal.

4:36It requires a good amount of judgment. And we have come up with what we think is a pretty simple four-by-four matrix to decide what should and shouldn't be torn down. The number one thing you have, The number one advantage you have is that you have all this data and a working system of record if you are a SaaS company. So we say the things on the y-axis that are critical to your competitive advantage and things that don't slow you down, that keep you fast and moving fast in the development of AI. Those things, those are easy calls. Upper right quadrant, don't tear that down. If anything, preserve it, make it better, fortify it.

5:13This is an area where you can succeed. this will be the cornerstone of your AI native movement. The other easy call, the bottom left. Now I say easy call because logically it's easy. Emotionally, it will be just as hard. But the bottom left are things that don't help you, that are not important for your competitive moat, and slow you down. Why in the world would you keep those things? But I promise you, promise you, when you talk to your technical leaders who are building these products, some of them will tell you, we absolutely have to keep them. but they're slowing you down and you shouldn't.

5:46And these are emotional things. Of course, it's emotional. Think about somebody who's worked five years on building a microservices architecture that does not serve your AI needs. And yet you will have to take it away. It will have to go. So you're gonna have to tear those down. The upper left, bottom right, those are more challenging calls. Those are more nuanced. You're gonna have to decide, hey, does this thing that doesn't slow me down but isn't really helpful for my competitive mode, do I really want to keep that or tear it down or not? Those are going to be hard calls to make, but they're much more nuanced and based in kind of judgment that's situational.

6:21That's fine. What's important though, is that you look at those logically and coldly and not based on somebody else's feelings. I often say that one of the challenges in working in any company, but especially a tech company, is our disposition to be agreeable. Unfortunately, in this instance, Agreeableness is not going to help you. You're going to have to be disagreeable as a CEO or as a technical leader making these calls to go AI native. All right, but your SaaS application does have a ton of value. We want you to move from content to context. Right now, your SaaS app has a ton of content. You have built an application that is very good at bringing in lots of data from lots of sources, cataloging it, storing it in the right places, making it easy to pull data out and be viewable by your customers.

7:13That's awesome. But in this world, the value shifts. We no longer care so much about the content itself. We care much more about the context. And what do I mean? Well, again, we have tuned all of our systems for kind of ingestion of data via a keyboard. So there is a big shift at play. We all know this is coming. that the world that we see today is not the world we're going to see in five years. Most data will come into applications, not via a keyboard over the next five years. So we're gonna have to take systems that are tuned to get data in one way and get data out another way to be totally different.

7:53Now in a context system, and you have an advantage in building a context system, your application should ingest data agentically and take action with context. So you have all this content. it really can act as context for an AI agent. You are so well positioned for the future. And yet I hear it's very kind of in vogue to say, you know, SAS is dead. We don't need the database anymore. We're not going to need all this stuff. We just need AI agents doing everything. But anybody who actually thinks about it for a little bit knows that that's not quite right. And I'm going to use a very simple analogy.

8:30It comes back to the 1990s hit film, Clueless. and imagine for a moment i came to you and i said hey good news we have an ai agent that can pick out your outfits in the morning if you're like me i don't like picking out my outfits it takes too much time i would be like awesome that's awesome done i'll sign up for the ai agent that picks out my outfits and in fact share already had an ai agent that picked out her outfits um but if you then said oh by the way now that you have this ai agent you don't get to have your closet anymore We're not going to show you your closet. You can't see it. In fact, it's just a bunch of unstructured data and clothes in a mess, and you never get to have it anymore.

9:09You'd be like, hold on, wait, wait. I would actually like to have my closet and the AI agent. Can I have both? Of course. Of course. That is the better world to live in. And that is the world where we, as SaaS leaders, actually have a significant advantage. Your SaaS application is like Shere's closet. The agent helps take action based on the content inside the closet. It's a simple analogy, but that is actually the world that we're headed to. And it's where we are far more advantaged than our AI only competitors. But to do this, our architecture will have to change fundamentally. This is not a small change.

9:46This is a big deal. I had an engineer say to me just a few months ago with a ton of pride, mind you. We have built an incredible SaaS application that makes tons of money, grows fast. Customers never leave it. Favine has almost 96 % gross revenue retention, 124 % net revenue retention. He has every reason to be prideful of the application he's built. And he said, and the great news is now we get to sprinkle AI on top. That is fundamentally incorrect. We all know in this room that if you are going to build an AI application today, you can't simply say, hey, we've got all these APIs. OpenAI has APIs.

10:24Done. We'll connect them. We've got an AI application. That is not going to cut it in 2026. To be AI native, actually have to change the architecture of your system. It has to flip the old way. The way that my first kind of one of my top engineers said is, OK, you have an AI layer that sits on top of, you know, your basic core services, your AWS, your data, your code base, the other kind of traditional services, maybe, you know, a calendar application, all the typical things that a CRUD app will have. You have to move from that to an architecture where the AI data layer sits right next to the AI application layer.

11:04And why is that? Because the ML engineers you hire are going to demand and they are going to require that they can tune and change the way data goes into those AI applications all the time on nearly a daily basis. and they can't be going to your old traditional tech team saying, hey, can you please change how the API provides me this data? That's not going to work. You are going to lose. You are going to go too slow. So we introduce an AI data layer, and this layer owns how information is prepared for AI, how you ingest, process documents, emails, messages, and events. For example, we sell legal software.

11:41A legal case is just a graph of people, events, claims, outcomes. It's the exact same kind of material that you would use to build an AI data layer, but the ML team has to own that data layer. And when they do, I can tell you from personal experience, the AI applications you can build when you format it like this, dramatically better, dramatically more reliable, higher context, more complete, more accurate. Your customers will be much happier. You will actually be able to compete with the AI native applications. So together, this makes up the core AI applications layer for things like copilot, search, summarization, recommendations, and reporting.

12:23Think how important this is for a moment. Reporting. Search. In a world where you have AI, you want reporting and search to be AI native. You don't want to even think about whether or not a user should semantically search or should use traditional search. Both, I think, are pretty darn important. You're still going to want traditional search. There are reasons to do traditional search. But you don't want your end user to have to think about that choice. You want to design with AI in mind from the very beginning. All right, to build all this, you're going to need to hire AI natives. The problem is they don't want to work for you.

12:58They want to work for AI companies. So why in the world would AI natives want to work for your old SaaS company? I've got one piece of very good news. besides wanting to work for cool AI companies, the good ones actually want to work for companies where they have more access to data. Let me tell you a little bit, a little story. In our world, there are a ton. Legal tech is such a hot area for AI today. There are so many competitors that have said, hey, give us your documents. We will run AI on top of those documents and we will sell you AI outputs. There are hundreds of these companies. The problem is that they think that that provides an answer to the customer.

13:40But we know at FileBind that to actually get an answer to a legal question, you need way more than the documents. Let me give you a simple suggestion or a simple explanation. Let's say that you wanted to ask our system, what's going on in this case and what should I do next? Your application might be, what should I do? Tell me about this opportunity for this customer and what should we do next? is a very common question that you would use a chat application, a co-pilot to ask and get an answer. But to get a good answer to that question requires way more information. It requires you to know who's done what.

14:17Those are audit trails. It requires you to know who is who, who is doing the things in your application. It requires deadlines. It requires a calendar. For us, it requires conflict checks and contact information. You have to know so much to give a complete answer. And in the world of legal tech, and I believe almost for all of you in this, an incomplete answer is actually worse. It's worse than an inaccurate answer because the customer doesn't know what they didn't see. And that is deadly. But the good news is because you have such rich data, you can sell this to the AI natives you're recruiting.

14:53And then you can sell them on your distribution. And here's what happened. This is distribution from a real product that we launched just recently. It went from, what, five or 10 people using it a day to hundreds of people using it every single day in just a few months. This can happen to all of you and your AI team will love using products that absolutely rip because you have distribution and data to make these products completely different than your AI only competitors. The problem is you are going to have to acquire talent fast. I have a confession to make. We had an ML team. It was fledgling.

15:32Now we simply bought a company. You might have to go buy a company. And I realize that may not be an option for all of you, but we bought Parrot. It's an AI native company. And now we've merged the two teams, our traditional data science team that we had before with the Parrot team that is AI native today. AI natives want to work next to each other, sometimes physically, but they want to work with other AI native talent and build something amazing together. We now have a critical mass of AI talent at FileVine that can move very fast. Now, you have a branding problem. You have a branding problem in the employee talent markets, and you have a branding problem because you have a SaaS application today.

16:11A lot of our customers would say, we love you for what you do, but you don't really sell AI, right? We had to convince them over and over again, we actually do have great AI products. And that took a rebranding. So the perception of who you are needs to change. And it can sound so simple to say, okay, we just updated the colors in the logo. But we did more than that. We planted a freaking flag. We are different now than we used to be. The old logo very much called out like a military legal vibe. We wanted to say, no, no, we are moving fast. We are up and to the right. We are older than we used to be.

16:49Let me tell you, this change, it meant a lot to our customers and to the people looking at us, but it meant even more internally. This change in our mark has told the people who work at FileMine every single day, the old mark is from a traditional SaaS era and the new one is from the AI era. And it actually matters. It is highly symbolic and you should have no problem telling your team, we are moving and you need to give them a symbolic thing to look at for that change. But what gives, why is any of this important? None of it actually matters, folks, if you don't build great AI product. Here is the good news for all of you.

17:28If you are up against a competitor who is an AI only product and you have the system of record, I promise you, your customers do not want to leave. They are far more interested in staying right where they are and having their data and their documents and all the workflows that they've built on your SaaS application do all the work they were doing before. They want to stay with you, but your product can't be 90 % as good. It can't be 95 % as good. It has to be at least as good. Obviously, we want to build it better, but it has to be at least as good. The good news is though, we do win in a tie.

18:04Now, you have to obsess over usage. To know if your product is any good, our customers using it. At Filevine, we are religious about this. We do not let our teams roll out applications beyond beta without audit trail logging to know exactly who's doing what with these applications. These are real numbers from our AI Fields product. It's absolutely massive. I think we've had 150 million actions taken on our AI Fields product in just a few months. This is our Docker view product also growing extremely quickly. We always are thinking in the language of daily active users, weekly active users, monthly active users.

18:44We think about this all the time. And our Blockbuster AI product, this is by far our best one, is an incredible product. Chat with your case. It's a simple co-pilot, but it does so much and is relied on by our customers for everything. It is fun to see them. You'll see a customer use it like five times. Then the next day they'll use it eight times. Then the next day they use it 20 times. And we now have a customer using it, I think like 2000 times a day. This product, just this one product is growing in usage 10 % week over week. You can build these kinds of products, but you're going to have to have a new team and a new culture to build them.

19:20All right. Assuming you've done all that and you all have a bunch of data sitting in these SaaS applications. Again, we got really good at ingesting data as SaaS leaders. Your data is your company advantage, but you are going to have a bunch of only companies come to you and say, I can't believe you have all this data and you won't give it to me for free whenever I want it. It's your customer's data. How could you possibly be acting this way? Well, I've got a couple points that I'd like to bring out. Number one, first of all, it is entirely fair that if we spent years building a product and ingesting data and cataloging it and categorizing it in a way that It can be easily ingestible by AI applications.

20:05We want a relationship and we want to have a discussion before we simply allow access to the API. So we have control over our APIs today. We have moved from open API access to personal access tokens. We know exactly who's accessing our API. We know exactly what they're doing. We know how they're using the API. We know how often they're using the API. They can't get at it without our express permission. We actually evaluate every single request. You might imagine these AI competitors, they really like this. Actually, they hate it. They're really upset about it. But what we have found is that our customers actually totally get it.

20:45They totally understand our position on this. And look, we have never said one time to any competitor, you can't access our data. But we have always said, let's have a conversation about that. Let's see what makes sense for us and for the customers and for you. Now, one little tip, if you're ever negotiating with these folks, they will demand access to your data like it's their freaking moral right. But the minute you say, OK, sure, let's have a conversation about that. But of course, it goes both ways. Correct. We can take the AI outputs that you guys get from our data and we'll get them right back into our system.

21:18Correct. Isn't that how it'll work? All of a sudden, the shoe doesn't feel so good when it's on the other foot. Just a little tip when you're negotiating with these folks. So it's fine to integrate, but review the requests. But most importantly, the AI traffic is going to reveal something to you. You will review, reveal promising areas for new development. You will all of a sudden see very early on which products are gaining traction. And you should 100 % copy those products. You should copy those products and build them right into your system. You have the right to do this. Do not let these folks win this battle.

21:52You have worked too hard and built an application that is worth too much to your customer base. You deserve to win this, but it's going to be really hard. All right, price to dominate. The good news is you have built a SaaS application that likely has very high gross margins. Congratulations. We were all taught that gross margins were a really important part of building a SaaS application. your ai-only competitors they struggle with margins badly they have a really hard time with margins because they have all their lom costs are super high so you get to do something kind of savage you get to sell the ai products at a lower price point than your competitors can sell them at why can you do this because you can keep a blended gross margin at a much higher rate than they can keep it at.

22:46So yes, while your investors might say, oh my gosh, like why are we selling the products cheaper than our AI only competitors are selling them? Your answer is because we're gaining market share. And by the way, our blended gross is still higher than our competitors' blended gross. And here's the good news. You are way better situated to win this battle than your competitors are. Oh, here's the chart. This is how this works. You can see the blue line is kind of what happens to the blended gross. As you drive down the blended gross on the AI gross margin versus your SaaS gross margin for Filevine, it goes from 80 % and sure, I mean, maybe it goes to 60, right?

23:24That would be an extreme case. But that's way better than the AI competitor whose gross margin is driven all the way down to 10%. And you get to be that kind of player. And you should because I promise you, these AI competitors, they will cede you no ground. They will be just as vicious competing with you as you should be with them. Lastly, you want to build hard things. I have a couple different components of this. First of all, you're ready for a major change. You should rebrand and think about your system differently. We have created a new category. We call it the legal operating intelligence system.

23:59I think you should think in category creation. It is something entirely new. You have a SaaS product, you have AI companies. Really, if you're going to blend the two, it's an operating intelligence system. For us, it's a legal operating intelligence system. For you all, you're probably going to call it something different, but you are creating a different category, a blended category of software. Now, what's cool about Lois is we can rethink the interface from the ground up from an AI first mentality. AI, just like the cloud is now assumed today, AI is just going to be assumed. It will fade into the background.

24:36We will talk less about AI. We will simply talk about technology. At the end of the day, that's all we have ever been is a technology company solving problems for customers. And that means that the way we price has to change. FileBind is moving from a subscription user-based pricing to usage-based pricing. For us, it's kind of a hybrid model. We charge on what we call a matter or a project. But this allows us to kind of charge each customer for how much they're actually using the product. We have found this is working really well. We already do it in a major way at FileMine, and that has worked incredibly well.

25:13We get far more revenue from our usage-based pricing customers than we do from our traditional subscription-based customers. But again, the product isn't just an AI plus a SaaS product, at least not for us. We have made a very important decision. We no longer sell to customers who won't buy the AI products. We no longer sell to customers who won't buy them. Why is that? Because we have to assume that AI is implicit in everything we build. We don't want to be making a distinction. Oh, geez. Like, how do we sell this to the non-AI customers? That's crazy. There should be no non-AI customers. What are your customers doing if they're not buying AI?