No Priors: Artificial Intelligence | Technology | Startups
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.
00:06:29 · Elad Gil
[00:06:29] S1: And this also, I guess, feeds into the traditional diffusion of this technology, which has been consulting firms, the Accenture's and Deloitte's and the like of the world kind of owning a piece of this, doing the implementation for people. You're kind of taking that and putting it in the hands directly of the IT department. Are you also exploring this more sort of forward deployed motion, or how do you actually get this really built out in the enterprise or used effectively?
00:06:51 · Jake Stauch, Sarah Guo
[00:06:51] S0: Yeah. Absolutely. Diffusion is obviously the gap here. The technology can do so much, and and enterprises have barely scratched the surface of what's possible. And a lot of that is just understanding the people, the process, what workflows actually exist, how you want them to exist in the future. And we think that you still need people for that. So we deploy both a a deployment strategist, we call them, and a deployed software engineer that go on-site with customers, figure out how do we implement this, how do we solve all your problems as a business, and then walk you through the implementation. What we found though is that a lot of times, after we've got this implemented and deployed, customers then have a long tail of more stuff they want. And and as we've discovered with software engineering, there's actually unlimited demand for work, and we've started to build out a team. We call it ProServal, or or automation engineering is is the field, and we bring in these automation engineers to kind of do ongoing continued support and services because there's so much that they want to automate, and they need the help to be able to figure out what can be automated, what do the processes look like, how do we navigate all of our systems, and then put it into production with a product like Serval. [00:07:55] S2: How old is the company today? [00:07:58] S0: Two and a half years.
00:07:58 · Jake Stauch, Sarah Guo, Elad Gil
[00:07:58] S0: Two and a half years. [00:07:59] S2: And, you know, if you just look at ServiceNow as a business, I feel like Serval's one of the only companies that is like, we are direct attack replacing enterprise systems of record, including for large enterprises. At the beginning, did you think you were going to do that? Like, do you... How do you eat the elephant? Yes. Just in terms of, you know, how big that product suite is. [00:08:20] S0: I like to say everything has gone exactly according to our very delusional plans. So we set out from the beginning to be the largest enterprise software company of all time. We actually made a list of all the companies. We circled ServiceNow, and we said we're gonna beat them, and we're gonna build something much bigger. We said that on day one, when it was two people in my cofounder Alex's living room, and we just said we're gonna do it. And so even though that was crazy, that was always the intention. We didn't grow into that ambition. We set out from the beginning. I think that that actually [00:08:45] S2: A lot of people would have started with, like, you know, automation on top of their ticketing system, [00:08:50] S0: for example. Yeah. So the the way you typically go after these these large enterprise startups, at least, they they build, like, the layer [00:08:56] S1: Mhmm. [00:08:57] S0: Or they go down market. And those are kind of the two common approaches. Let's build a small layer, play nice with the big incumbents, AI agent on top of Salesforce, which, like, you know, it's it really caps out.