Rebuilding IT From the Ground Up for the AI Age: Serval's Jake Stauch

0:00Jake Stauch:You know, I think that there's always a gap between the idealized vision of what you think your job is going to be and then what your job actually is. I think it's true for every profession. You idealize and kids do this the most, right? When they're like, want to be a firefighter or an astronaut, what that job looks like. And then when you get in that job and you realize, oh, there's there's actually a lot in this that I don't like. And we want to be the tool that that actually closes the gap between what you think your job is going to be and what your job actually is.

0:43All right, I'm here with Jake, the founder and CEO of Serval. Jake, welcome to the show. Thank you so much. Great to be here. We're going to start with a high-level question. You guys are building kind of the next generation ServiceNow, like the AI Native ServiceNow, so to speak. That is right. Why does the world need an AI native service now?

1:01Jake Stauch:Yeah, employees need help at work. That's kind of the idea here is we are a platform for employee support. The technical term is enterprise service management, but what it really means is getting help at work. And the ideal way to get help at work is you ask for something and you get it instantly, automatically. You don't have to wait on somebody to track down a ticket and assign a ticket to somebody. You just get help immediately. That requires you to have some kind of automated support. and that automation is best built with AI. So we think about this from first principles. How do we support employees?

1:32Jake Stauch:You automate all the requests. How do you automate all those requests? AI is really a good tool to bring that automation employees. Awesome. Let's say more about that. So I remember back in the day when we first met Fred Letty, who is the founder of ServiceNow in like 2007, 2008 sort of timeframe. And at that time, people thought that ServiceNow is amazing because it was this big step function change over Peregrine and Remedy. And the key thing that they got right was to think about enterprise software as an abstraction is just workflows on top of a database, right? So they built kind of this flexible workflow configuration engine on top of a database.

2:11And at the time that was amazing. And, you know, IT people loved it and they were off to the races. What I'm hearing you say is that that's not enough. With AI, there's kind of a new generation of automation that can be done. Can you just say more about the thing that ServiceNow built and the thing that you guys have built and kind of the side by side, like what is truly different this time around?

2:32Jake Stauch:They got it right. We also built workflows on top of databases, and that is the right abstraction that those are the right primitives. The problem with their workflows and databases is that they require a lot of manual effort to build and maintain. So building those workflows often requires dedicated development resources to put those together. And while that sounds fine, you invest those resources and you get a beautiful automation on the end of it, that can take weeks to months. And in an era where business processes are changing very rapidly, by the time you get that workflow implemented, your business may have changed and moved on and you want a different workflow.

3:06Jake Stauch:And so your automation kind of runs behind where you want it to be. And that's more true now than ever before. Same with the database. If you need to manually update those entries and look at your IT assets and make sure they're up to date in those systems, that's going to be very, very painful to have to bring in consultants or internal developers to update. we took this unique approach of let's keep those primitives, workflows on top of databases, but allow you to use AI to build the workflows and use AI to update the databases. And the way we do that is this code gen engine where you describe the workflow that you want, all the different steps and permissions and approvals and logic.

3:45Jake Stauch:And we take your natural language description and we turn that into code. And so your workflow appears instantaneously. There's practically zero time to develop those workflows. And the same thing goes for our databases. You can describe exactly what data you want to take from which sources, and our system will actually generate the code to fetch that data and keep it up to date without you having to do any kind of manual intervention. I remember you said months ago, this is one of the things you said when we were first getting to know each other that really piqued my interest. Something along the lines of if you really want to drive enterprise automation, you have to make the process of building the automation just as simple, if not simpler, as the workflow being automated.

4:28Do you still believe that? Is that still true?

4:30Jake Stauch:I still believe that. And that insight came from just putting yourself in the shoes of someone in IT or another function and you're presented with a task. Somebody asks you to reset their password and you've got two options. You can go into Google Workspace, find the user, hit a button that says reset password. Or you can open up a workflow builder and you can drag the trigger and then you can drag the response and then you can build out this custom workflow. And when you're presented with that choice, you're just going to reset the password. You're going to do the manual thing. But if it were actually easier to build the automation, you would build the automation because it's just why wouldn't you?

5:05Jake Stauch:And I think that it comes down to that. People in the moment have to make that decision. And you want that decision to be very, very easy to opt for the automation, not opt for the manual step. Now, is there such a thing as being too simple to automate? Like, is there, you know, people talk about the vibe coding and all the slop that it produces. Is there such a thing as slop automation? Yeah, it is. It's real. And we've had to build some really interesting things around that because, yeah, when you make it so easy to automate, somebody might build the 20th password reset workflow this week. Yeah.

5:34Jake Stauch:And it's basically the same as the 19 that came before it. And now you've got all these duplicate workflows and the AI gets confused on which one to run. Yeah. How do you guys manage that? We built a new agent basically on top of Serval that we're really excited about that has full contextual awareness of all the workflows you've ever built before, how they work, what they're going to do. So when you say, hey, I want this workflow that does X, Y, and Z, it says, hey, actually, you've got 19 that already do that. I could modify one of these, but here's what I think you should do. I think you should actually delete 10 of them, separate these remaining nine into these different categories, add these approval steps.

6:08Jake Stauch:And so it actually walks you through the system and is kind of your assistant that's an expert in our product to help you translate the business requirements into the actual product implementation. Speaking of the product, so you're a product guy. And one of the things that we've heard about you consistently from every possible source is that you're extremely customer focused and like really good at listening to the customers and figuring out what they need. Do you have a North Star metric that you rely on that just tells you the product is getting better and more useful? Or is it more you collect all the anecdotes, you synthesize them, you kind of have an intuitive sense?

6:41Like, is there a number that you can look at? I think it's the latter.

6:44Jake Stauch:Like I try to be embedded with the customers, like full immersion. I am in every single customer Slack channel. Yeah. I think most of our customers will get a Slack from me and that channel every single day. Wow. And that is a huge... And just to set the context, a listener might think like, okay, you probably have four customers. How many customers do you have? No, over 100 customers and a lot of large enterprises. And it is overwhelming at times to be in all those conversations all the time. And sometimes I feel like, oh man, am I wasting my time? But I feel like I really understand what's going well, what's not going well.

7:16Jake Stauch:And I just have my finger on the pulse. And there's just no substitute for that, especially when a lot of the implementation work has gotten very fast. More and more of the moat for any startup is the customer insight, the empathy of like actually understanding what they want. And if we can have a differentiated advantage around the customer insight, that's going to be much more valuable than having a product advantage, which is copyable overnight. Let's talk more about that because this has been a hot topic for a few years now. ChatGPD comes out in November of 2022 and immediately people start deriding application layer companies as wrappers on top of a foundation model, right?

7:57And what you just said kind of plays directly into this theme of there's always a school of thought that says, well, the foundation models can just do everything. And then there's a school of thought that says, sure, but you can build a company on top to close the gap between raw capabilities and like actual customer problems. How do you view your role running an application layer company versus the role of the foundation models? Like where do you think the moats form around your business over time. Basically, to the extent somebody listening is interested in, how do you build an application layer company in this era?

8:32What are your thoughts on that?

8:33Jake Stauch:I think you have to be happy when the new models come out. And that is kind of the guiding principle that we have is, how do we make sure whatever we're building is actually not made obsolete by whatever the labs and hyperscalers come out with, but actually is made better? What's a good example? For us, we think the product is the boundaries. The product is the controls. The product is actually what limits the capabilities of the model. Because the question now is not, can Opus, can GPT 5.5 do these amazing things? Can they do the things I want to do in my enterprise environment? The capabilities are practically unlimited.

9:09Jake Stauch:It's the limitation now is how do I get comfortable as a large enterprise that cares about security and deploying this company wide without elevating my security risk? And so we think about it from the boundaries. And so that means really boring old school enterprise software things around permissions and approvals and limiting the scope of your API integrations and having visibility into that, having audits and reporting and logs and alerts and just all of the things that make you feel comfortable letting the models run wild in your environment. And so one of the fundamental things we did from an architecture perspective is kind of divide the agents into two parts.

9:52Jake Stauch:So our customers, when they experience Serval, there's really two agents they work with. One is the admin agent that helps build all of these tools and skills that configure how the help desk, what things that the help desk agent can do and what it knows about. And so it's the admins that build that. And then there's a help desk agent that end users talk to that resolves their problems. the help desk agent can only use the tools and skills that have been expressly built, published with approvals and permissions and all of that by the admins. And that architecture, that kind of like two pronged architecture ends up being really, really powerful because you can let the help desk agent run wild, right?

10:33Jake Stauch:Because the end user can ask it anything and it can use its reasoning ability and its full intelligence to be able to solve user problems. but it can only use the tools that the IT admin has expressly said are okay to use. And those may have approvals attached, permissions that gate certain users from doing certain things. All of that is done on the admin side, but then you get like kind of the full ability and intelligence of the help desk agent to use those tools appropriately. What's under the hood? Is there anything you can say about which models you guys are having luck with and maybe how that's evolved over time?

11:04Jake Stauch:Yeah, we use open AI and anthropic models today, always experimenting with the latest models from all providers to make sure that we're on the cutting edge, running eval suites on everything that we do. Today, it's still open AI and anthropic models. We find different models are better at different applications. So for the interaction with the end user, we're seeing the most luck with open AI models. and that has remained consistent for quite a long time. That actually calling the correct tools and responding to the user in the appropriate way, that's still we're having a lot of success with various GPT models and always keeping those up to date depending on the latest release.

11:47Jake Stauch:But on the automation side, which is mostly co-gen automation, having the most success with anthropic models. So continue to use those, Sonnet, Opus, and tons of trade-offs between the different models. And I think what's interesting in recent times is the new releases oftentimes are not just like plug and play. You know, sometimes you get some advantages and some things that were working really well don't work as well anymore. So that's become an interesting challenge as things go. Actually, yeah. How do you guys manage that? Like how long does it take to incorporate new models into the production version of your product?

12:20How much of that process is like automated in some fashion? How much of that is somebody just has to sit down and figure it out? Like how do you manage that?

12:26Jake Stauch:It's not as automated as I'd like. We have the evals automated, but then in every situation, when we have a new model that we're testing, some things get better and some things get worse. And a lot of things that get worse, not necessarily the model got worse, but we built a lot of prompt tuning and a lot of infrastructure around the known quirks or behaviors of that model. and those make less and less sense when the new model comes out or where you're swapping models. And so that's where a lot of the adjustments have to be made. And then you kind of run it through the evals and then you do a slow release across customers.

13:00Jake Stauch:So we're getting better at this, but I think there's certain cases where the trade-offs have not been worth it, where we've actually upgraded models and then downgraded the models and said, you know what, the old models are maybe they're a little bit faster or maybe they're reliable in a way that the new models are. And so maybe the new models are a little bit smarter, but they misbehave in ways that are less predictable and we have less predictable guardrails to prevent against. And so we're like, Hey, this model might not be as smart, but we know it's going to behave the right way for these customers.

13:28Jake Stauch:So it's been an interesting challenge and it's changed over time. How much are you guys factoring cost into the equation right now? Because I think, you know, step one, you got to make sure the product does something magical. Step two, okay, now let's make sure we got a business. And we can, you know, we can extract an appropriate amount of value for this functionality. Do you guys think much about cost at this point? Or we kind of know where that's going over time. Let's make sure the product is magical and we'll figure out the cost element later. It's the latter. And I think one of the reasons that give us this flexibility is that our unit economics end up looking much better than a lot of AI companies because we are not in the business of reselling tokens.

14:07Jake Stauch:The way that our product works is that you build these automations, which are basically type scripts. And once they're built, you don't have to rebuild that. And so every time the end user asks for a password reset, it's not going and regenerating code to reset a password. It's actually just running the code that's already been generated. Over time, users have to generate less and less actual code because we have a growing library of automations that cover the very long tail of things that you might want to do. And so there's not as much, especially in the kind of very expensive code gen token consumption, there's not as much as you'd expect.

14:45Jake Stauch:And so the Uniacal Micros are very strong, even though we haven't done a lot of optimizations around that. So I tried to tell the team, spend more money, use the best possible product. We know long-term where this is going. We know that there are all kinds of optimizations we can make down the line. So that's been our focus to date. I think that though, where it starts to get more interesting is as we explore more and more applications of like background agents, long running agents that are not just responding to help desk requests or not just building quick scripts for you, but investigating all of your historical tickets or investigating logs from devices and doing all this work in the background and maybe generating solutions to problems you didn't know you had.

15:25Jake Stauch:That's where it becomes a little bit more interesting where maybe the costs become more relevant. So that's where we'll probably start to think about costs a little bit earlier in the journey because those could run away pretty quickly if you're not keeping an eye on it. Yeah, that makes sense. Let's say somebody at OpenAI or Anthropic wakes up tomorrow and they're like, wait a minute, I found this company called ServiceNow. And it seems to be like a major system of record, major center of gravity inside the enterprise. I think we should build as a first party product, you know, the OpenAI ServiceNow or the Anthropic ServiceNow.

15:57If they set their sights on you and come after you directly or come after your category directly, what would that mean for Serval?

16:04Jake Stauch:Yeah. I mean, this is always a really tough question because on the one hand, any response to like, okay, this company with infinite money and the best engineering talent and AGI wants to do what you do, how are you better? It's kind of like any response that is going to sound pretty naive of like, well, we're just going to like beat them. But I think the history of startups tells us that often the smaller company does beat them. I mean, the existence of open AI and Anthropic are kind of proof that you can beat the entrenched incumbent with infinite resources. And I think it comes down to, you know, maybe divine providence favoring startups or maybe like a lack of focus is often what makes it hard to execute.

16:47Jake Stauch:And so, you know, when I was starting my first company in the whole, you know, every VC would ask me, won't Google just do this? And yes, Google will do a lot of those things, but it actually is very hard to do a lot of different things really, really well. And it's hard to divert your focus into all these categories. And I don't think ITSM makes the most sense as a focus area. One reason is that I think in the past couple of months, Anthropic has added more ARR than ServiceNow has in the past 20 years. Good point. And so does it really make sense for them to take their best and brightest people to throw them at this problem that even if they are very successful would not be really – it would take them years to get what they could get out of the rest of their product portfolio in a matter of months?

17:27Jake Stauch:And so I don't think they're going – they'll probably look at this category. I wouldn't be surprised if they built some kind of simple version, maybe a more mid-market or SMB-focused version. But to really devote the time and energy to master the complexities of enterprise service management, not to say they couldn't, but the focus that would require, I think, would be a bad use and bad prioritization of their resources. And I don't think that that's going to happen. Yeah, I think you're right. Let's talk about your customers for a minute. So you got a bunch of the kind of really nice AI native logos, and you're also starting to have some kind of big enterprises.

18:01How do the needs differ from the AI native crowd to the big enterprise crowd? And if you had to pick from each of those, what's the nicest thing that they would have to say about you? You know, Serval is amazing because what would they say?

18:15Jake Stauch:Yeah, I think what's been the biggest learning is how similar they are relative to how different. I expected them to be much more different. But the pain points and the problems end up looking remarkably alike from the AI native to the large enterprises. We work with companies as small as a few hundred employees up to companies as large as a few hundred thousand employees. The difference ends up being how many people it takes to make a decision. And that's what actually makes things really challenging is because more of a go to market thing. It's a go to market. It's an implementation thing more than that.

18:44Jake Stauch:Yeah. So when you're if you're working with a company with a few hundred employees, there's probably an IT leader that can say this is how we're going to do things. This is how onboarding works is how we're going to reset passwords. If you're working with a company with a few hundred thousand employees, no one even knows who that person is, if that person exists. And so you end up in all these kind of committees trying to figure out what should we do here. And that's actually what makes it very, very challenging. And I think you see that in these labs, building out these consulting businesses and more and more services and deployment resources, because that ends up being a lot of the rate limiting step in adoption is coordinating all these folks.

19:20Jake Stauch:So what would the nice things they say about me, I think, or about Serval? In the AI native early stage companies, we often just, we take an IT person that's passionate about technology and we let them spend their time building. They got into IT because they love technology. And so much of their job before Serval, they weren't really getting to experience technology. My favorite example of this is a customer we had that spent a lot of their day fielding ServiceNow requests to provision someone's access to Cursor. Yeah. And that juxtaposition was just so sad. I'm like, I am in this ancient ticketing system helping somebody else at the company get access to a really cool AI tool.

20:02Jake Stauch:And my tools are still stuck in the past. And I don't get to use the cool stuff. And with Servo, they get to use the cool stuff. Like there's actually an AI tool for IT built for them. And so that's, I think, what we see on the AI native side. I think in the large enterprise, you know, generally they're thinking about it at just a higher level of business transformation. And we hear a lot more about the end employee experience because at a small company, even if your IT processes aren't perfect, no one's waiting weeks to get a response back from IT. But a large organization, like you could send a ticket into, you know, into the abyss.