No Priors: Artificial Intelligence | Technology | Startups
00:01:10 · Jake Stauch, Elad Gil, Sarah Guo
[00:01:10] S0: Thank you. Great to be here. Maybe you could tell us a [00:01:13] S1: little bit more about your history and how you started Serval, and what what is the problem that you started with, or why did start working on this to begin with? [00:01:18] S0: Yeah. So background, Serval is enterprise automation, enterprise service management. The idea is you can ask questions and get help at work and automate a lot of the parts of the job you don't enjoy, and we do that through cogen agents. So that's the... What the company does. We compete with companies like ServiceNow, is historically the big legacy provider in the space. I discovered this category actually working at a previous company called Verkada, where we sold enterprise physical security products to IT teams, spent many, many years talking to IT teams, figuring out what to build for them. And one of the things I love to to do with these teams is ask them, what do you spend the most money on, and what do you hate the most? And ServiceNow would top a lot of those lists. And so I was always interested in this category of maybe there's a better solution here, but it wasn't clear for most of my time there how you'd ever beat ServiceNow. They're a great company. They've done so much so well, and why would you be able to compete with them? [00:02:10] S2: What was frustrating for users about it?
00:02:10 · Sarah Guo, Jake Stauch
[00:02:10] S2: What was frustrating for users about it? [00:02:13] S0: I think so hard to build in the platform. So it's a very robust, customizable, powerful platform that can map to all these enterprise workflows fundamentally. It's a workflow automation platform, and so it can do so much, but every idea you have for doing something cool, you have to go and implement, and a lot times you're bringing consultants to implement it. So it's always kind of running six to twelve months behind where you want it to be, and it's fine if you're building something that's gonna last five, ten years, like a big onboarding flow, and nothing about your business is gonna change, so you can invest all that upfront effort, and then it gets deployed, and it works for a while. But for just trying to keep up with all the automation that you wanna build, ServiceNow is not able to catch up. And and then it's very expensive, and it keeps getting more expensive over time, so that frustration of it's not keeping up with where I wanna go as a business, and it keeps getting more expensive, that's what was driving a lot of the frustration.
00:02:59 · Elad Gil, Jake Stauch
[00:02:59] S1: I think one thing that you mentioned that's really interesting about what you do is if you look at the world of AI adoption, a lot of the really early adoption is coding agents. Yes. And that's engineers using it to build applications for their business, for products, other things. And what you folks have been doing is really pioneering expansion of the category in terms of saying, We should let anybody internally, an IT person, etcetera, build internal products or tools that can be used broadly across the enterprise. You're basically providing coding agents for all the internal stuff for people who often aren't that adept at coding. [00:03:32] S0: So can [00:03:32] S1: you talk more about that? Think it's super fascinating. Yeah.
00:03:32 · Elad Gil, Jake Stauch
[00:03:32] S1: you talk more about that? Think it's super fascinating. Yeah. [00:03:34] S0: Love this idea of coding agents for the rest of us, because we think so much of the early enterprise adoption in AI has been engineering teams. And if you think about why that is, there's a lot of reasons. Obviously, early adopters and a lot of different technologies, but also they're able to build and experiment with these tools because they have this process of code review. It's very safe for even a 19 year old intern to build something very powerful and then go through this process before it goes into production. We wanna bring that same capability to the rest of the enterprise, so we build a platform that brings coding agents to IT departments who then can bring it to HR and finance and people teams and legal teams. And they can do it because under the hood, it's coding agents, but then you have all these abstractions and guardrails and controls. So things go through approval. They go through a staging and, approval cycle and audit trail and all of that. And so it's how do you build a system that makes it easy for everyone to be a builder. And the core insight here was that IT actually wants to build stuff. And for most of the world, they don't have an engineering department. They have an IT department. Most companies have an IT department, not not a a team of software engineers. And so IT is what's building and configuring technology for the company, and we are building essentially the AI platform for them. How does how does that
00:04:41 · Elad Gil
[00:04:41] S1: run into things like all the different more, like, workforce agents that are being developed, like Dot from OpenAI or, you know, some of these things where you almost have a virtual assistant that will go and do things for you?
00:04:51 · Jake Stauch
[00:04:51] S0: Yeah. Think those really work well for consumers and SMB and even a lot of the mid market organizations. In a large enterprise, there's so much, and I didn't even really appreciate this going in. There is so much process around approvals, around what you can and can't do. The permission sets of any individual are so limited that even if you were allowed to bring your agent to work, which a lot of cases you're not, what it can what it can do is so incredibly limited. It can it can barely even read your emails. And so when you think about all the stuff you need to do at work and all the systems you wanna touch and all the interesting automations you could build, your personal permissions end up being so limited that these personal agents are also incredibly limited and all... Not all that useful. So what we can do, because we work with these IT teams that can connect with, you know, service account credentials and admin level permissions, we can create a structure where people can access elevated permissions and do all these crazy things in the org, but with very, very strict deterministic approval cycles, workflows that are deterministic, and an audit trail, because under the hood, we are also a ticketing system. So you say, okay, I want my agent to go do x, y, and z. Well, most organizations are gonna be very uncomfortable with that at the large enterprise scale. But with Serval, that's gonna create access requests that go through an approval process, and all of that's gonna be documented and logged, and then you can get just in time access to be able to run that deterministic workflow one time, and all of it's gonna be tracked. And, yes, it's a little bit more annoying than the magical personal agent experience we love with Instinct and Muse and all these tools, but it's what has to be true for a large enterprise, especially a regulated enterprise, to ever adopt these kinds of technologies.