Latent Space: The AI Engineer Podcast
00:50:42 · Mihail Eric, Shawn Wang
[00:50:42] S1: you go. There we go. There you go. Okay. That... This is Monaco. This is the Monaco that that I know and love. Yeah. So we are... We're we're a series b company here at Basin SF. We are are building effectively like an all in one sales platform, fully AI native, agents doing all the fun stuff, or at least the stuff that humans don't like to do, but but that agents are... Do like to do because they don't really have opinions, but do in a less air prone fashion. So the salespeople and sales teams can focus on doing what humans are good at, which is, you know, closing deals. [00:51:12] S0: So presumably, Marc Benioff doesn't like you guys. Probably not. But he's, like, also, honestly, very AI native person. Mhmm. Very aggressive, an agent force and everything. Yeah. What are you doing that he doesn't do or he cannot do?
00:51:12 · Shawn Wang, Mihail Eric
[00:51:12] S0: So presumably, Marc Benioff doesn't like you guys. Probably not. But he's, like, also, honestly, very AI native person. Mhmm. Very aggressive, an agent force and everything. Yeah. What are you doing that he doesn't do or he cannot do? [00:51:26] S1: Well, okay. So right now, we are... Started the company in a sort of like... Market that we went after was not enterprise from the get go. Right? We sort of said, like, we're gonna start by... Like, if our goal is to build the all in one, then we need to kind of build this in an iterative fashion with with... As a group of customers, and we need to, like, grow with them into this. It's much harder to pull someone away from from Salesforce than to say, like, hey, if we meet you at the point when you are still just a Google spreadsheet tracking pipeline, and we sort of show you a, you know, more AI pilled sort of answer to the problem, then we can grow with you. And in a way, kind of follows the same the same, model of, like, you know, Brex or all the, like, YC companies, where you kinda sell to your batch mates, you kinda grow with them, and and you... Then you go up up up market. And so we are, I would argue, are the... I claim that among the best, if not the best, sort of like, startup focused sales platform. And so all of our customers are early stage startups. At this point, probably through series A is kind of where we've primarily been focused, kind of seed to series a, and then now we're going more upmarket. And we've kind of learned a lot about the all in one in the last year and a half of doing that.
00:51:26 · Mihail Eric, S2
[00:51:26] S1: Well, okay. So right now, we are... Started the company in a sort of like... Market that we went after was not enterprise from the get go. Right? We sort of said, like, we're gonna start by... Like, if our goal is to build the all in one, then we need to kind of build this in an iterative fashion with with... As a group of customers, and we need to, like, grow with them into this. It's much harder to pull someone away from from Salesforce than to say, like, hey, if we meet you at the point when you are still just a Google spreadsheet tracking pipeline, and we sort of show you a, you know, more AI pilled sort of answer to the problem, then we can grow with you. And in a way, kind of follows the same the same, model of, like, you know, Brex or all the, like, YC companies, where you kinda sell to your batch mates, you kinda grow with them, and and you... Then you go up up up market. And so we are, I would argue, are the... I claim that among the best, if not the best, sort of like, startup focused sales platform. And so all of our customers are early stage startups. At this point, probably through series A is kind of where we've primarily been focused, kind of seed to series a, and then now we're going more upmarket. And we've kind of learned a lot about the all in one in the last year and a half of doing that. [00:52:30] S2: You are the head of AI? [00:52:32] S1: Yeah. That's right. [00:52:33] S2: What's some cool AI stuff you guys have built?
00:52:33 · S2, Mihail Eric, Shawn Wang
[00:52:33] S2: What's some cool AI stuff you guys have built? [00:52:35] S1: Yeah. I mean, we have built our own agentic harness for for sales use cases. So, all this... I mean, to to call back to the to the coding harnesses, taking a lot of inspiration from from the systems like the Codexes and the and the clouds of the world and sort of building bespoke tools that are very sales focused, bespoke workflows that are all kind of geared toward GTM use cases. So we built our own version of that that powers pretty much every single thing that powers the Monaco platform. So from scoring of TAMs, scoring of ICPs, and defining of ICPs, through to pipeline evolution, through to all the mundane kind of things that maybe a salesperson doesn't wanna do, which is like, Oh, update my meeting, pre meeting notes, update my summaries, all these things. We have agents powering all those workflows. So we've done that. It kind of comes out of the box. And yeah, so long long agents, long horizon agents for... Long horizon agents and short horizon agents in some cases for GTM use cases. [00:53:36] S2: And it's pretty domain specific, built on foundation models. Would you do any of your own models for [00:53:42] S0: this?
00:53:42 · Shawn Wang, Mihail Eric
[00:53:42] S0: this? [00:53:42] S1: Yeah, it's a good call out, because so far... I mean, I think this is generally true of a lot of vertical AI companies, is you kinda start where you sort of use the frontier models as third party services to see what's possible, because they are the very frontier. And then, I think we've seen this, I guess, in the past week with the whole Harvey sort of where they... Plus 50% to minus 50% margin kind of question. I mean, I think, irrespective of the numbers, I think what is interesting is, at a certain point, things do become interesting from a cost perspective. You start to wonder, can you do it faster on, you know, open weight models? And then that's a lot of that. Then a lot of what I think the purpose of the AI pieces are is just to post train those models, build environments for, you know, kind of to post train using RL and things of that nature. So we're kind of in that phase right now. [00:54:25] S0: Oh, fun. Yeah. I've been calling this table through the agent lab playbook, but that is kind of my coining of, like, you know, like, start from the user first and then they they they use whatever models you need to to build products, but then you can backfill the the model side once you have the user base. And then the the model labs go... [00:54:44] S1: Kind of [00:54:44] S0: go the other way. They they start with the big pre trains, and then they try to find users. And it's like very... You know, it's hopefully working as a as a as a split. The Monaco would be on the sort of revenue GTM side. [00:54:55] S1: That's right. Yeah.
00:54:55 · Mihail Eric, Shawn Wang, S2
[00:54:55] S1: That's right. Yeah. [00:54:56] S0: And I think it's interesting. They would want to serve you while also try to compete with [00:55:02] S1: you as monolabs. That is correct. [00:55:05] S0: One of the things that I've been... Obviously, I dabble in some of this. I think one of the hard problems is actually enrichment. Mhmm. And I would say I spent a lot of my time doing that for for basically SEO and, like, research and what have you. And I imagine for you, it's actually, like, a core part of that. That's right. Do you find that models are good at it? Do you have any vendors that, like, you like working with, you wanna give them a shout out, whatever? [00:55:33] S1: Yeah. Yeah. Absolutely. Yeah. So we we have used... We were early adopters of Parallel, and then I think they're very strong. [00:55:38] S0: Okay. So just general search? Yeah. Mean... Not like ZoomInfo or [00:55:41] S1: some of No. Those No. No. But, we use Parallel. We also use Exa, so we'll give it to... Give a shout to both of those folks. [00:55:47] S0: Can you compare that? [00:55:48] S2: They're both [00:55:51] S0: friends. [00:55:51] S1: They're both They're both friends. They're both folks that, that we [00:55:54] S0: like to You'd be have very interested if you like one of [00:55:56] S1: them. Yeah. [00:55:57] S2: I'll keep