02:32:20 路 John Coogan, Markie Wagner
[02:32:20] S1: Mhmm. [02:32:20] S7: And I think that's the u for a lot of these biz processes that are at the heart of these, like, really big and old businesses. So you gotta do two things. You got a system that can learn all the rules and then run them with nines. And if you have that, you can get from where we are now to this version of the future that I think everybody's really excited about, but you need to build that bridge. [02:32:38] S1: Mhmm. Not everyone's excited about it. AI has very low approval rate, But tech leaders are certainly excited about it. But so why have you started with such big companies? Like, I feel like you've been you've been, you know, at the heart of the startup ecosystem for so long. If you came on here and said, oh, we have so many, you know, my friends' companies on board. These are the named customers SoFi, Chime, AIG, like these are large institutions at this point. Why start at the top? Feels harder.
02:32:38 路 John Coogan, Markie Wagner
[02:32:38] S1: Mhmm. Not everyone's excited about it. AI has very low approval rate, But tech leaders are certainly excited about it. But so why have you started with such big companies? Like, I feel like you've been you've been, you know, at the heart of the startup ecosystem for so long. If you came on here and said, oh, we have so many, you know, my friends' companies on board. These are the named customers SoFi, Chime, AIG, like these are large institutions at this point. Why start at the top? Feels harder. [02:33:15] S7: Yeah. I think our view was when you're building something like this, if you build something that's less powerful, it's hard to sometimes make it more powerful. Mhmm. Like, lot of these drag and drop tools, they were simpler, they were easier, they could do simpler things faster, but they just couldn't handle super complex things. Mhmm. And so, you kind of get kneecapped in terms of what you can represent and build. And the view is, hey, AI is gonna be doing a lot of this, like, writing of the software. You'd be in a better place optimizing for the power of the thing. Mhmm. Can it express and run these five hour processes require those nines and then everything else becomes really easy actually. Mhmm. And I don't think that people are going to be having a platform for the easy stuff and the hard stuff. I think they're just gonna be running all of their processes in one spot.
02:33:15 路 Markie Wagner, John Coogan
[02:33:15] S7: Yeah. I think our view was when you're building something like this, if you build something that's less powerful, it's hard to sometimes make it more powerful. Mhmm. Like, lot of these drag and drop tools, they were simpler, they were easier, they could do simpler things faster, but they just couldn't handle super complex things. Mhmm. And so, you kind of get kneecapped in terms of what you can represent and build. And the view is, hey, AI is gonna be doing a lot of this, like, writing of the software. You'd be in a better place optimizing for the power of the thing. Mhmm. Can it express and run these five hour processes require those nines and then everything else becomes really easy actually. Mhmm. And I don't think that people are going to be having a platform for the easy stuff and the hard stuff. I think they're just gonna be running all of their processes in one spot. [02:34:01] S1: Mhmm. I mean, you mentioned like tens of thousands of hours of information stuck in people's heads. Like what is data collection? What does data collection look like? Conversations? Like it feels like, yeah, like is it the McKinsey model or is it the self serve software model? Like where do you want to sit?
02:34:01 路 John Coogan, Markie Wagner
[02:34:01] S1: Mhmm. I mean, you mentioned like tens of thousands of hours of information stuck in people's heads. Like what is data collection? What does data collection look like? Conversations? Like it feels like, yeah, like is it the McKinsey model or is it the self serve software model? Like where do you want to sit? [02:34:21] S7: Yeah. I mean, what's interesting is it's evolved over time, but the place that it has ended up going is looking closer to data labeling. So what happens is a big com company will say, here's my biggest process. It's a 100 pages of documentation, and you know that that's only 20% of it. So the question is like, how do you get that other 80? Mhmm. Well, what we'll do is we'll take that operating procedure, we'll generate the AI operating procedure, it's written in step by step English. What our system does is turn that into code under the hood, and you run it. Mhmm. And when you run it, and you put it in front of those experts, they have a ton of opinions. They have a lot of feedback to say, you you forgot that the threshold's actually a million, not 10,000. Right? All these little things [02:34:57] S1: Sure. [02:34:58] S7: Which get merged back into that document, and you're sort of doing that more and more automatically so that it looks almost like training a program or something like that, rather than sort of like long calls or or process mining in, like, the the normal sense. And so, more and more, it looks like people giving feedback into the system directly and that updates the rules that are written in it.
02:34:58 路 Markie Wagner, Jordi Hays, John Coogan
[02:34:58] S7: Which get merged back into that document, and you're sort of doing that more and more automatically so that it looks almost like training a program or something like that, rather than sort of like long calls or or process mining in, like, the the normal sense. And so, more and more, it looks like people giving feedback into the system directly and that updates the rules that are written in it. [02:35:19] S0: Yeah. And so you're using AI models to generate that code that is then deterministic, which gets you the reliability that these companies need. Correct? [02:35:29] S7: Yep. Yep. Exactly. So the, you know, the source of truth is that AI operating procedure. It's English, but what are some of those turn into code. So when it runs, if the if the world's the same as yesterday, it's just going to run as code. Great. Nothing to see here. But if something changes, like the column name changes from month to month, or the save button moves, only then will AI go in, repair it, look at what the English goal was, and then update it. And so if you do that, you can get the best of, hey, it's code, it's precise when it runs. But if something changes, instead of breaking, it will actually just kick back and recover. [02:36:06] S1: Mhmm.
02:36:06 路 John Coogan, Markie Wagner
[02:36:06] S1: Mhmm. [02:36:06] S7: And that's important because a lot of this work, code couldn't do alone, and agents couldn't do alone. So code is very static. Right? It's like very brittle. So even one small date change or something could break the whole thing. And so that's one side of it. Agents on the other hand are pretty improvisational and they think step by step and, you know, when you're figuring out what to do as you go, eventually you're going make a mistake. This is something that's kind of in the middle where it's code of things the same, AI is there to test, heal, recover if things are different, and that's how you can kind of get those nines. [02:36:38] S1: So are you hiring forward deployed engineers? Like, what is the role of engineering in your organization at this point? [02:36:45] S7: Yeah. So we do we hire tons of forward deployed engineers from all the best spots, you know, whether it's, you know, a place like Palantir or Mhmm. You know, even like, retool or scale and all these other sort of new places that have their own versions of forward deployed. And, yeah, I think it's interesting. It has over time looked less like extremely just only focusing on engineering, like hard engineering. It requires being able to change how people operate. Right? [02:37:10] S1: Yeah.