00:01:15 · S2, Dan Lee, Ruby Justice Thelot
[00:01:15] S2: Yeah. [00:01:15] S0: If you ask our engineers, we were just talking about this. Their job looks completely different than it did six months or a year ago. And the same has been happening in legal, in customer support, in other back office jobs. Sales has not changed as much, though, but it's actually a way bigger market. In The US, there are actually 12,000,000 Americans who sell for a living, and it's more than all these other jobs combined. It's... Hasn't changed as much. We think it's because it's a harder problem. It's all about, like, persuasion and... You know, rather than reasoning. It's not as much a model capability. But, the pace of the frontier, both, like, from the labs, as well as our own applied research, makes us really confident that sales is gonna be the next job to inflect. [00:01:54] S1: Yeah. I wanna understand the larger landscape when it comes to sales. Because my understanding of sales is, again, it's persuasion, but it's also your network. It's the people you have authentic relationships with. So how does AI come into play here?
00:01:54 · Ruby Justice Thelot, Dan Lee
[00:01:54] S1: Yeah. I wanna understand the larger landscape when it comes to sales. Because my understanding of sales is, again, it's persuasion, but it's also your network. It's the people you have authentic relationships with. So how does AI come into play here? [00:02:08] S0: Yeah. I think in a lot of jobs, AI, you know, makes... In a lot of jobs, AI is making you want fewer people. Mhmm. But sales is interesting because when you can make a sales team more efficient, you actually normally want more sales. Right? Whereas in support, if you can make a support team more efficient, you normally don't want more support tickets. Or if you can make an accounting team more efficient, you normally don't want more audits. And I give this example in, like, recruiting. If you use AI to completely automate an interview process and another company uses AI to actually save the interviewer's time and, you know, which job would the candidate pick? Like, know, they would probably pick the one where you have, like, a better candidate experience. Sales is kinda like that, right, where you're building trust and relationships and, like, actually solving, like, real customer problems. So I think that part of, like, that part of the job is still human. That doesn't go away. But AI can do all the stuff behind the scenes to help people spend more more of their time on those real human parts of the job. [00:03:03] S1: So where exactly does AI come into play here?
00:03:03 · Ruby Justice Thelot, Dan Lee
[00:03:03] S1: So where exactly does AI come into play here? [00:03:06] S0: Yeah. So I'll I'll share... Like, Nooks, we are helping the best companies in the world grow faster with our revenue agent platform. It helps sales teams... You know, it deploys sales teams' AI agents across the revenue life cycle from customer coming inbound to the website to, like, doing outbound sales to growing and expanding accounts and closing deals. And our agents have access to all the same tools that a rep would, like writing emails and doing research, generating decks, even, like, answering technical questions on calls. And in a lot of cases, our agents are actually able to come up with better deal strategies than a rep would because they have access to way more data. [00:03:44] S1: Mhmm. What do you mean more data? So how exactly does a human do it versus how an agent does it? [00:03:51] S0: And [00:03:51] S1: then what can the agent not replace?
00:03:51 · Ruby Justice Thelot, Dan Lee
[00:03:51] S1: then what can the agent not replace? [00:03:53] S0: Yeah. So the agent, in most cases, is not replacing, like, persuasion. Right? Yeah. I think there's, like, easy sales problems. Like, if someone comes to your website and mostly wants to buy your products and you just need to answer a couple questions to get them over the line, that's an easy problem that's gonna be fully automated with agents where, you know, we we have agents that can demo the product, that can talk and hold a full conversation with a customer because that's an easy persuasion problem. But there are also hard persuasion problems where you have 20 stakeholders in a deal and you haven't talked to 10 of them yet. They don't know what your product does. They don't... You know, they don't have any budget. Right? And you need a... You know, that's a hard persuasion problem where that's gonna need a human. And in that case, have agents that are doing a lot of the work behind the scenes so that the the person can focus a lot more on the the strategy and the relationship building. [00:04:43] S1: So at the start of this interview, you brought up a really important point. It takes an enormous amount of capital to transform an industry. Of course. But why? Just break that down for me.
00:04:54 · Dan Lee
[00:04:54] S0: Yeah. There's there's a lot. So for example, we've started with a lot of the most advanced companies in the world using Nooks. So, yeah, over 2,000 companies from AI native startups like Cursor and Notion to enterprises like Toast and ServiceTitan are using Nooks to augment their team... Their sales teams with agents. But then there's... You know, a large part of the market is, you know, maybe less advanced or less AI pilled companies that can still get probably even more value from Nooks. So, you know, a large part of the investment is, like, accelerating growth in these verticals. We are selling into new geographies. So, you know, we started, you know, mostly in in The US, but we're opening offices. We're opening a New York go to market office in UK and Ireland and Australia and other parts of the the world. And a really big focus is on new products. So where we started is we started by building tools for sales reps. Think like calling, emailing, doing research. We translated those tools to also be used by agents. Mhmm. And we're expanding across the entire revenue life cycle, where we started a lot with the top of funnel, where we can show really quick results. And now, though, you know, doing work across inbound where we have agents that can, you know, hold full conversations in with the products. We have deal agents that, you know, can basically manage all your deals for you, understand, like, what's slipping through the cracks, and, you know, making sure that you're on top of all your follow ups, and even, like, renewing and expanding accounts. So a a big investment is going across, like, expanding the product suite. And then the the last area, I'll say, is in applied research. There are some really hard, like, kind of human agent collaboration problems that we need to solve. Because sales is more about persuasion, you know, we're doing things like fine tuning our own open weight models on, you know, Nooks proprietary data that outperform Claude and ChatGPT on sales specific benchmarks on things like cost and quality and latency. We're building actually, like, RL environments for sales because, you know, because sales is a lot less verifiable. We're building, like, basically, these simulations where you can simulate what a customer... Like, how a customer might react.
00:07:08 · Dan Lee, Ruby Justice Thelot, S3
So you can actually test, like, hundreds of scenarios to see which one's gonna perform optimally. [00:07:14] S1: Man, this is really interesting to me because unlike something like coding or even law, which law is tricky, those are a lot more verifiable than something like sales, which is a very human persuasion. You have to understand that person. Do you think there is a future where AI can do that better than a human can? [00:07:34] S0: So our agents have been trained on, like, billions of interactions. And I think... So I'll I'll give some examples of things that we've done to optimize this problem, and then I'll talk more about the feature. So, like, in coding, for example Mhmm. When a coding agent is generating code, it'll pull in relevant parts of the code base, and it has some function to determine what's relevant. In sales, when you're trying to prepare for a meeting or, you know, send an email to someone, you actually wanna pull in relevant sales scenarios. But then there's this question, like, what's a relevant sales scenario? Have you... You know, or is it going after the same persona? Is it... You know, they've had similar objections? And we... That's an interesting, like, kind of hard technical problem that we've had to solve in building our own agent harness for this. And then there's also... Nooks sits at this unique position where we own the layer between the sales rep and their end customer. [00:08:33] S3: Mhmm.