What’s the Future of Vertical SaaS in an AGI World? Jamie Cuffe, CEO of Pace

And I think if you sort of try to shoehorn AI into these like tiny little boxes and then you add, you know, kind of this code layer around it, you're sort of missing the point of what can be done with AI today. I think if you build, you know, for the ability for these AI agents to take any standard operating procedure and be able to run that process end to end, that's the direction that we're still taking at a pace.

On this episode of Training Data, we sit down with Jamie Cuff, the founder and CEO of Pace, to explore how AI agents are quietly revolutionizing insurance and what that means for the future of work in high complexity, highly regulated environments. Jamie shares why the real breakthrough isn't just automating tasks, but architecting AI to handle nuanced end to end processes previously thought to require a human touch and human judgment. We dive into building trust with some of the biggest and oldest companies in the world. Why forward deployed engineers are becoming a secret weapon for AI companies and how AI replacing BPOs could fundamentally rewire entire sectors of the economy. Enjoy the show. Janie, thank you for joining us.

Thanks for having me. It's great to be here.

We would love to dig right into what you're building. I'm curious, you started a company last year. What are you building and why?

Yeah. So Pace is the agentic process outsourcer for insurance. And what that means is, you know, we have seen over the last couple of decades big shift from onshore to BPO work. And our thesis is that over the next decade we're going to see a big shift from outsourcing to outsourcing to AI. And so we focus exclusively on insurance carriers, helping them to automate a lot of the critical back office operations that traditionally are being outsourced to BPOs.

Can I ask about the path that brought you here? So top your class at Princeton, investor at Sequoia, Rising star retool insurance BPOs. Not obvious that that would have been the next step on the journey. Can you explain kind of how you got there?

You know, it's a circuitous path and what's, what's funny is I actually started there. So I grew up around insurance my whole life. I was, I got born in London, grew up between New York, London and Bermuda, which are kind of all the, the capitals of insurance. And the through line there is, my dad actually ran operations for a reinsurer and then a Lloyd's cover holder in London. And so I kind of always knew about this problem. But you know, starting my company out of College and like immediately run towards that until I got to Retool. And actually Retool, a ton of our customers came from financial services and you know, from the outside in. I think a lot of people think of retooling, think startups, but you know, Retool is really attacking a lot of this, you know, bigger market around custom software. And the places where there's most custom internal tooling are the businesses that are most operationally intensive. And financial services firms are a lot of those because they don't have a lot of great software that's been built off the shelves. And so what we were seeing is a lot of like very large, you know, legacy insurers were using Retool all the way through to the most tech forward ones. So, you know, had like the ethos and the vouchs, you know, those types of businesses all the way through to Progressive and Berkshire Hathaway and business like that. At that point it was, you know, being in a forward deployed engineering capacity, you kind of get this front row seat to seeing a lot of the problems that, you know, these companies are handling. What's really cool is a lot of times people would build retail applications that would actually help to streamline some of their BPO operations. And so I got to sort of see a lot of the problems I think my dad was also dealing with in his business of policy administration, claims submission intake, a lot of the core workflows that are critical to insurance operations. And at the time, I think with software you can make a little dent on BPO spending, you can make it 10% better. And I think that was a lot of the promise of OCR and RPA and some of these sort of prior technologies. But what happened after the ChatGPT moment and you know, when I was lucky to be working on a lot of Retool AI and some of the new products coming out of Retool, you saw this opportunity to now completely change the economics of that industry. And so instead of, you know, augmenting outsourced services with software, you could actually go and completely replace the vertical service. Um, and so insurance is a particularly good starting point for that because the lingua franc of the industry is emails and PDFs. Um, and the main reason for that is there are a lot of intermediary players and there is no like, common system on which they all work. And so, you know, with the lack of an API, the sort of alternative is, you know, email, PDFs, phone calls. And so now AI agents really have the ability to Kind of bridge that gap. And so what gave rise to a lot of BPO work, which was this sort of like manual data entry handling of these documents and emails, is now also what's perfectly positioned this for AI agents. And so we see that as a really good starting point within the sort of opportunity to build the agentic process outsourcer across many industries longer term.

So Jamie, how do you build a better business than the last generation of BPOs, even though you're taking over the same workflows and a lot of the same spend that's going on today?

Yeah, people often ask like kind of, you know, what keeps me up at night? And that's the main thing. That's, that's the main thing that I keep the main thing which is, you know, we are very lucky right now to be dealing with, you know, lots of incredible customers and you have this sort of opportunity. We could build, you know, a very large companies being a BPO is there are multiple tens of billions of dollar public companies that are running at 10, 15% gross margin with tens to hundreds of thousands of people doing this work. And obviously those are great businesses and exceptional teams. But for us, we think there's a much, much bigger outcome. And so when I actually talk with our team about what we're shooting for, that's like, that's the failure case. So we build this sort of multi billion dollar bpo, but don't change anything about the economics of the industry. The goal of what we're going for is how can we take that from a 10% gross margin business to an 80% gross margin business where instead of having hundreds of thousands of people, it's hundreds of thousands of AI agents and a small team of insurance experts. And so that I think is a much, much larger company. And then I think the last step is how do we do that not just in one vertical, but across many verticals. And so those are two things I think about. And I think people say like, okay, you know, push gross margins to later or it's not that important. Or you know, I think people have a lot of perspectives on gross markets. I think it is important and I don't think it's important to necessarily have from, from day one, but you need to have a path to it. And people talk a lot about flywheels. And the flywheel that we want to create is every time we're making improvements in the product, the gross margins go up. And so that's the big thing that I think we're pushing on is even if we're lower gross margin today as we take on harder and more complex tasks. How are we just constantly seeing that number shift up over time?

As you're building with agents and starting to plug into these email and document processing workflows, I'm curious what you're seeing is working in practice. We've talked to folks from OpenAI here, we're doing fine tuning. They have all the data, they have this amazing infrastructure. We've seen companies with much lighter processes in the backend. What actually works for you in practice?

Yeah, so a lot of the types of workflows that we focus on are these sort of like mission critical processes that are, you know, have been outsourced. And so they want you do that work sort of end to end. And so I think, you know, the prior generation pre AI, when you're sort of tackling these problems. Yes, like extracting data from unstructured documents is great, but it's sort of just like one part of the problem, it's extracting it. You know, it's maybe reasoning over that, applying this sort of human judgment and then it's also being able to sort of write back into those internal systems and really sort of solving what previously could only have been done with humans. One way you could think about this is hey, let's build one of these sort of like workflow DAG kind of builders and sort of infuse sort of AI nodes in different places where you're like, okay, now we can do, you know, document extraction or we can do an email sending back and forth or something like that. Part of the reason that, you know, BPO work exists is it was so hard to codify that work deterministically. And the reason is there was this sort of human judgment component and lots of edge cases that came him up. And I think if you sort of try to shoehorn AI into these like tiny little boxes and then you add, you know, kind of this code layer around it, you're sort of missing the point of what can be done with AI today. I think that's actually like if that product stance is actually like relatively short on AI, I think if you build for the ability for these AI agents to take any standard operating procedure and be able to run that process end to end, that's the direction that we're sort of taking at a pace.

So do you have a human in the loop right now or no?

So for the vast majority of our workflows, there's no human in the loop. It's AI processing end to end. We do have our own operations team that we're building out for sort of the most complex workflows. And kind of the way we see that sort of like role of human, the loop is we have our operations team to do it. We can escalate to our customers. But primarily where that is used is in the onboarding process. So as we're sort of getting customers up and running, we basically want to be able to just label a bunch of examples and make sure that the agent is doing this, you know, at, you know, starting out of the box, is it, you know, maybe 90% accuracy? And then how do we get it up to, you know, the 99.5% plus that our customers require to be in these critical workflows. And so that's sort of where we see this sort of human labeling component and then a self improvement loop that sort of helps us get there. And then over time we just sort of reduce the amount of human in the loop there and move that sort of operations team to work on the next most difficult task.

So when you engage with an insurance carrier and they have X number of workflows that they're currently outsourcing or that could be outsourced to some sort of traditional insurance bpo, I presume you can't do all of them today. Yeah, pretty amazing. If you could, which can you do today? And how do you engage such that you kind of pick off the workflows that are sort of really applicable to AI today and then how do you sort of grow over time to take up more of those workflows?

So we tend to focus on the use cases that are one already really nicely outsourced. So they have to already be using a BPO at scale, scale. The reason for that is it's already codified as a standard operating procedure. There's already a way to check for accuracy because you know, this is already being reviewed usually by someone internally or at least sort of like QA'd on a sampling basis. The last part is sort of, as you think about the change management, it's much easier to sort of spin down a BPO service than it is to sort of retrain or move a W2 workforce. So we start with operations team BPO, the large carrier. We tend to focus on the most high volume use cases where we think there's the biggest roi. So you just look at kind of the line item for bpo. Where are they sort of spending the most and where can they get the most ROI from AI? Usually what that means is we start at the very top of the funnel. So submission intake, where they're getting in new risk, extracting that data, running their business logic over it, and then writing back into their own new business underwriting platforms. The same can happen also on the claims side. So first notice of loss or claims intake, getting that information into their internal systems. And then once you do that, customers ask you to sort of work down the policy servicing lifecycle into endorsement processing and other policy administration tasks all the way through to billing. And on the claims side, the same thing from claims intake all the way through to payout and quality assurance. That's usually the path we take.

What's a typical before and after situation? You know, Human EPO and then pace AI bpo, like what, what sort of, what sort of metrics change, you know, when they go from before and after.

Yeah, so it's interesting in the BPO world, I think a lot of where you start is, okay, there's a new technology that can now help me save a lot of my costs. And you know, with common VPOs, I think, you know, the MPS is quite low and it's a massive line item, you know, for a lot of our customers, you know, eight figures plus of spend and it starts oftentimes with cost and that's big. And you know, for, for some of our customers, we've been able to achieve, you know, 50 to 75% cost savings, you know, in a very short period of time working with them. But usually what happens after that is they realize that there are a number of other things that are actually much more important to them. And usually it's the things that previously just couldn't be done with BPOs that now with sort of AI working at superhuman speeds, you now have this sort of capability to do so. An example of that is if you think about a submission intake process, usually that carrier that's receiving that submission is not the only one that's receiving it. And so if you can get back much faster, you can actually close business faster. Same thing you may think about scalability. So like around BPO work is not just kind of like perfectly linear over the course of the year, it's highly seasonal. So you can think like a Jan1 enrollment period or hurricane season like we're in right now. And so if there's a massive spike of hurricane claims, you have to go and spin up a bunch of other bpo, you know, workforce to do that work. And now you can have AI sort of working for you 24, 7 to just crank through that backlog before it even becomes a backlog. So those are a few of them. And then I'd say the last thing is sort of observability, which is a lot of this DPO work in the past has been sort of a black box. It was very hard to see kind of like what was getting done, what stage, you know, each task was in where the bottlenecks might be. And some of this is sort of just like good old fashioned software workflows, which is, you know, now that you're sort of able to do this work with agents and sort of be able to literally see exactly the task is taking. And the reasoning and the why is you can now surface a lot of that information back to customers such that they can actually redesign their workflows, not just do the same stuff that they were doing with their vpo.

I'm curious, as you work with these massive companies with these mission critical like core business workflows such as processing claims or payouts, what does it take to actually help them succeed? Like on our end we see studies from MIT saying 95% of AI pilots fail. And then we also see our portfolio companies not failing. What do you see and how do you make sure that you succeed?

A lot of companies not failing.

Yes, exactly.

Yeah, you know, I think the 95% number, I think, you know, hit the headlines and everyone, yeah, it's like, oh my gosh, like what's going on? Yeah, I think that the truth is definitely that getting AI working in production is not like a done deal when you walk in. And there are certainly challenges that we've seen across the industry of other vendors or building in house where they haven't been successful. And so for us, what we really focus on is one of our core values of the company is closing the distance. And we've been lucky that every single one of our pilots has been 100% successful in going through to production. And I'm very proud of that.

Yeah. How did you pull that off? That's not normal.

Yeah. I think the main thing is we really believe in forward deployed engineering. So. And you know, I think that might be a little hot take and I'd be curious for your thoughts on this. But you know, my thoughts on this, you know, I think we're just taking, you know, you talked the other day about kind of like technology out versus customer back and we're just very much taking the customer back point of view. You know, when we're going in there, we're going in with, on site with our customers, we're flying to them, we're, we deeply understand the insurance industry and we are only focused on these workflows. We have, you know, a ton of insurance experience on our team and so that immediately, you know, we go into these customers and they, they talk to us and they're just like, okay, they're speaking our language, they understand what we're doing, but the next thing is like we have to do the hard work. And oftentimes that's doing the hard work upfront. Not necessarily like, you know, once you're live in production with your seven figure plus contract, it's, you know, working with a customer as a partnership to prove out that this can work. And this is even more important in AI where people, you know, there were a lot of, you know, exciting demos early on that maybe didn't pan out. And so we focus on with our customers just really helping them get to value and get to something in production as quickly as possible. So what that means is for our forward deployed engineering team, this was a team I was really lucky to build out at Retool and I think at the time deployed engineering, after me, Palantir Retool had maybe one of the most scaled deployed engineering teams. And the profile that really, really works for deployed engineering is basically former founders. So it's like engineers that want to be commercial or commercial people that are technical. And the critical thing is just do whatever takes to make the customer successful. So it's understanding the use case, it's helping them when you, when they need help with an AI model, we help them prompt, tune and make that successful. It's diving into the operations data and figuring out how to create a really clean eval set for them. It's helping them get integrated into their internal systems and it's all the sort of like hard work that actually makes these, these pilots successful.

Have you one thing I'm curious about because we've seen this in a bunch of the other kind of domain focused AI application companies. Do you hire AI people and teach them insurance? Do you hire insurance people and teach them AI? Do you just hire athletes? And you know, they learn as they go. How do you think about sort of the, the kind of the intersection of the technical skills, the domain knowledge? What do you look for?

Yeah, I think the critical thing is you gotta hire both on the team and you know, sit them really closely together alongside your customers and that way you kind of like get this constellation approach where everyone kind of like learns from each other. You know, for us like that means hiring people, you know, directly from the insurance industry that have worked on, you know, exactly these sort of mission critical operations at you know, top five carriers at scale and then sitting them right next to, you know, a former YC founder that has worked on an AI company that is like at the cutting edge of building AI agents and then them kind of like coming together to get this done for the customer. So I think it's sort of that our experience has been there's not that many people that are already, you know, ramped up on AI agents. And then, you know, in the insurance world we've been lucky to find some know, incredibly forward thinking people that are excited about helping usher in this change. I think part of that is, you know, people resonate with the thesis of many have worked in kind of like the BPO world and they see, okay, like this is where, this is where the world is going. And I've been super, super impressed by people that we've been able to get from both of those backgrounds and their ability to pick this up really, really quickly to sort of get the full set of skills you need.

And back back on the topic of Ford deployed engineers.

Yeah.

Is that a temporary phenomenon because the capabilities coming out of the AI world are not quite there in terms of what we need to deliver a full reliable solution to customers. Or do you think 10 years from now you guys still have four deployed engineers on every account?

Yeah, I think I need to, I'll answer some, I'll flip the script to you of some of your thoughts. So I actually think that you and I both agree on the end state and the short term is, I think you, it's hard to go wrong actually getting more and more deployed engineers into working with our customers. And the main reason is we have this sort of flywheel both within our customers of helping them get successful with the product and then going on to larger and larger use cases where we can you really expand into this sort of like very large set of budgets that they have around BPO spend. And then the same thing we have a flywheel internally which is, you know, our forward deployed engineers can work inside of our product and work with our customers, but they can also ship code and code into the production code base. And they do. And so that's something we like test for the interview, you know, at the on site and then, you know, throughout their work. And so what's really important is you have this just like very, very tight feedback loop of working with the customer and you see some issue and then you immediately go back and fix that. It's not like I create this ticket and I pass it to somebody else who maybe does it and then maybe I get it back to the customer just so you get that done really, really quickly. And so what should happen over time we're already seeing is deployment times are going down. The amount of work that deployed engineers are doing is becoming much, much higher leverage because we're sort of pushing that back into software workflows. So in the short term I'm like very pro continuing to build with our customers and build earlier with forward deployed engineering. But I agree with you that I think the long term is you like software codified workflows is ideal. Right. You know we enable our customers to build inside of our product, you know and, and it's not all just forward deployed led. You know, we maybe get the first one or two up and running and we have customers where we've turn and now they have 9, 10 in production because you know they have a business analyst that has just been kind of like building out additional workflows.

Yeah.

And so that's very much, you know, the way that we think about building the product. And I think the critical thing is when you think about a team, you got to make sure that like kind of the layers and where you're investing in the team match up with that. So if your forward deployed engineering team is like this and your engineering team is like this, you're going to have kind of a problem where you're not actually like really, really investing in the product. For us right now, you know, basically 80% of our team is engineering and then we have four deployed engineering at the very top. We have this go to market team and I think as long as you keep that in balance, we're going to be able to really get that flywheel going. But yeah, I think the timelines is maybe kind of like the big thing that I'd be curious for your thoughts on, which is sort of for me, I think it's okay for us to continue doing this for a long period of time as it just makes our customers successful and then maybe we can check back in 10 years as we're going public. I actually think it's a great story to have in the public markets of seeing you've got this big forward deployed engineering team and you're just constantly figuring out how to make them higher and higher leverage so they can do more and more.

Yeah, I think we're pretty well aligned on this. Our observation on this market has been maybe to overstate it a bit if you think about bottoms up distribution versus top down distribution as two ends of the spectrum. You kind of want to be pegged out at one end or the other. You don't want to be stuck in the middle. Bottoms up ChatGPT is a bottoms up distribution product. Open Evidence is a bottoms up distribution product. Like these are not comprehensive solutions, but they are wonderful tools that people see and they love and they figure out what to do with. And I think on the top down end of the spectrum, you know, Harvey is a solution for loss. Here is a solution for customer communications. You guys are a solution for insurance.

Right.

And I think if you're going to go the solution route, we're at a moment in time in this market where the thing that you're selling to your customers is kind of trust as much as anything else. Like they can see the magic of AI. They want to believe in the potential, but they can also see the issues and they can see where it can go wrong. And I think the role that you guys or Harvey or Sierra plays in some ways is just saying, hey, trust us, we're going to make sure you're good.

Right?

Like, we're going to make sure that this new world of AI is to your benefit, you know, and it's going to make your business better and we're going to do whatever it takes to make that happen. And so I think Ford deploys engineers in that context. You know, not only like really helps to give the customers confidence to your point, it also creates that feedback loop into the organization so that you're not theorizing as to what problems you can go solve. You're actually there where the rubber meets the road and constantly like going deeper and deeper and deeper into their workflows.

Yeah, I think that's spot on. The trust point is so important also, particularly in our industry. I mean, insurance is sort of like the business of trust, you know. Yeah, we actually have a. One of our other values is be the rock, which is basically like our customers are there. Be the rock or the rock. Be the rock. Okay, got it. Um, be the rock. And basically the, the idea there is like our customers are there for their customers. They're insured on their hardest days and we are there for our customers every single day. Um, and that's what we, that's what we want to do.

Love it.

And you know, this industry is like, the industry is built around trust. I think a lot of what we think about is working with like the most trusted brands, the very, very top insurers at the top end of the market, building that brand or trust and credibility and the results that speak for themselves and then, you know, looking to, you know, expand through, through the market. I think similarly to, you know, Sierra and Harvey and a number of other companies have done that really, really well, which is, you know, go and work with the top, top customers in that industry where you can get, you know, they have the highest volumes, they have the highest roi. They're the ones that stand the most to gain. Make them really successful, and then the product sort of speaks for itself as you go to the next set of customers.

We've gotten two values out of you so far. We've got close the gap in be the rock. What else? What are the other company values? So we're two for two on good ones.

What else do you have? We only have one more. Okay, so get them short. The last one is my favorite, which is set the pace. So, you know, you both know me really well, and I think I have, like, a pretty high level of, like, urgency and maybe impatience at times. Maybe just like, you know, I just feel. We feel very, very lucky to be in the position that we're in. I mean, it is such an amazing time to be building a business. I'm sure you all see this from your vantage point as well, to be investing in businesses as well. And we are so lucky to be working with the companies that we're working with and to be, you know, charting this path. And, you know, I think it's. It's ours to go and make this future a reality. And so I feel set the pace really encapsulates that, you know, the goal, naming the company pace. It's all about speed. And that's a lot of the value that our customers are seeing, but it's also what we want internally. And then I think it's also a. It's sort of intentional. It's like this step a pace towards like, kind of our bigger mission. And we're moving as fast as we can to get there. Nice.

Tell us about that bigger mission. I'm curious where you go once you've gone deep in insurance. There's a lot of room to expand. But your. Your ambitions are much bigger than that.

Yeah. And maybe this starts actually when I was lucky to be an intern here. One thing I learned that summer was that Andrew Reed, his favorite company is Constellation Software. I don't know if that's. Hopefully that's still the case.

I'm pretty sure technically we'd have to say that his favorite company is Figma.

Yeah. Okay.

Or Robinhood or Klarna or you know.

We can rerun this as soon as favorite Non Sequoia portfolio company in the public market. Not investment advice is constellating. And so for those who don't know about Constellation and I think what is really, really exciting about that company until actually I think last week was run by this incredible founder, Mark Leonard, who built this company for 30 years. And his vantage point was let's build a business that can span many, many different verticals. And what they did in software was basically aggregate all of these niche vertical industries that in and of themselves were not, you know, scaled enough to, to be sort of like legendary long term businesses, but at scale in this sort of federated model that they have is an incredible business that's, you know, compounded massively over the last couple of decades in the public markets to I think one of the largest companies in Canada, you know, like 70, $80 billion. And there's this really interesting opportunity right now to do kind of the same thing that was done in software, but for the services market. You know, as we've talked about, the services market is like orders of magnitude larger. And so if you look at BPO spend, BPO spend in just banking, financial services, insurance, which is sort of the markets that we see immediately in front of us is about 400 billion. Just a little bit under 400 billion.

About the same as global cloud software every year.

Yeah, 70 billion of that in insurance. And basically the full BFSI BPO spends the same as like all cloud software. And so, you know, if we can be a platform on which you can aggregate all of these niche vertical services across BFSI and maybe beyond longer term, there is just a huge opportunity there. And I think the most interesting opportunity is that it's not just about aggregating them, is also about transforming the economics of the industry. Yeah, yeah.

This is one of the things that I think is interesting about PACE or other kind of domain specific application layer AI companies is a lot of the markets that people are going into are like sneaky big, you know, much bigger than you might realize because they're not household names, they're not B2B SaaS products. Right. The other thing that I think is interesting, and you alluded to this earlier, but I just want to hit on it again. This idea of going after the BPO spend, right? You have a clean interface with your customer because there's already an interface between the customer and the bpo. You have concretely defined objectives because they have to define those to be able to interact with the BPO you sort of have built in eval, because when that work comes back, they're checking it somehow to make sure that it's actually right. And a lot of times they're doing, you know, champion challenger models with different BPOs.

Yep.

And so I feel like this interface that you found, it's not just that the services spend is big, it's also that the sort of the route to market that you've chosen with the BPO interface is also like a really clean way to do it. And I think that that strikes me as an important aspect of what you're building too.

Yeah, I think that's exactly right. I mean, I think a lot of our customers are really tuned into the fact that AI has this opportunity to dramatically transform their business, particularly in areas where there is this high volume of highly repetitive tasks that require, you know, human judgment. You look at your BPO and that's exactly where that is. And the fact that there's already these standard operating procedures that we can model in our product and then, you know, the ability to do the accuracy checking of the built in eval, also do the change management afterwards much faster so that customers can really get, you know, hard cost savings, hard ROI savings is really great. And then it's also helpful, you know, as we go to market with our customers, is that there's a baseline in place. And you know, transparently, sometimes that baseline is not as high as we think it, you know, should be. You know, the, the average accuracy rates in EPOS is like you're making 5 to 10% error rates. And that's like, that's, you know, kind of where I think most people feel it is. But for some of our customers, we've seen it's actually like much, much higher. And the main reason is like, if you're doing, you know, when people talk about accuracy or, you know, with AI, they're usually like, okay, is the, can the AI be as accurate as the human? We think in some, a lot of the use cases that we're working on, it could be way more accurate. Yeah. And the reason is so like, let's say you're doing like a claims QA or a policy QA. You've got a 300 page document, you know, maybe this big claims file, and you've got like hundreds of rules.

Yeah.

And, you know, if you're looking at this thing at, you know, the 15th on a Friday and you're trying to figure out, okay, how do I apply these like hundred rules to this policy document, you know, that's, that's a really hard task for any, any person to do. And as chances are, you're going to miss some, you know, on page 298 of this, you know, a hundred page rule list. And that's something that AI is really, really good at and can do that same thing, you know, at the same quality every single time. Yeah. So the consistency you can have, you can really scale your best BPO rep. That's the things that kind of like promise. Yeah.

I think anybody who's ridden a Waymo in San Francisco, you know, appreciates the benefit of like a driver that always has perfect attention, has seen every corner case a million times. You know, like, I think that we have the existence proof that a properly trained AI can do a lot of jobs better than most people. And I think applying that to all these other verticals makes sense.

Tell us a little about what it looks like concretely when you're on site with a customer. Like what are some of the things that you're hearing from them, how do you get implemented and then what are the metrics that you track to at the end to make sure that you were successful? Like we've talked at the high level, I'm curious just to get some specific examples.

Yeah, absolutely. So a lot of our engagements start with, you know, we usually start with kind of the, the office of the coo. So really understanding, you know, where do they see the opportunity for AI and coming together on some sort of, I hesitate to say proof of concept because I actually think like POCs are not really, you kind of get this bad rap and they're not really the right thing to do in AI. It's basically just like get them to success as quickly as you can. And so what we focus on is like it's not like on dummy or demo data. It's like how can we actually go in and make them successful? So we pick something to work on and then what's really critical is you define the success criteria up front, which is like, where do we want to get to? And sometimes like, you know, we encourage customers to give us like their hardest success criteria. Like we, we want to show them that like, you know, we feel confident in our product and, and we're up for that challenge. And so define a success criteria up front's really, really important. And then it's really about like having this great partnership and we're super lucky with that with a lot of our customers is you can just, we fly on site, we work with Them we, we take in their stand existing standard operating procedures. And these are usually like these documents that are, you know, 50 to 100 pages long. They have like 60 different steps and it's like, you know, it'll be like a highlight in this document of like you got to get this thing out from here and then you got to enter it into this like admin panel and there'll be a little like red box around it. Like it's crazy.

Like are there, are there like shadow sops? So like if you just implemented the standard operating procedures exactly as they're given to you, would that actually give them what they want? Or are there kind of like shadow rules that are not codified in the document that you would figure out over time? Like people are actually doing things a little bit different than what the procedures say.

Yeah. So I think we're lucky that because we work mostly on BPO workflows, they had to be like pretty well codified because like, you know, you got to hand these off to folks. But there is always a little bit of gap there. And that's part of what when you close the distance and ask the customer, you like figure that out really quickly going back to it. Basically we take these standard operating procedures, we get them into our, what we call agent operating procedures. And so in our product that means is there's sort of, it's almost like a notion like document where you can basically write out all the steps that you're, that you need to do in natural language and then use various different tools along the way that are specific, you know, enable these agents to do everything that they would need to do in the insurance industry. So a good example is, you know, we have a lot of tooling for long context retrieval and extracting information from, you know, really, really challenging document sets. We have a tool for, you know, doing human reasoning over lots of rules. We have a tool for, you know, writing back into a lot of the sort of like mission critical vertical systems record. And then similarly, a lot of, you know, our customers deal with systems that don't have APIs. Either they haven't been built out yet or you know, they might not even own that end system because it's, you know, some, some intermediary think like a broker working with a carrier portal. And there, you know, we actually just use web agents to be able to sort of write back to this. And so that's the critical part that I think a lot of the, the FDE work drives is like building out these, these tools with our customers to make them to be able to get these agents live. And the critical thing is sort of doing whatever it takes to get them live. And you know, if there's the ap, if there's an API, we'll use the API. If there isn't, we're going to use a web agent. So we're going to make them successful and then the last thing is sort of getting live into production from there. And so you know, we've been been lucky with a lot of our customers. We've been, you know, we're working on again in the context of their most mission critical use cases, a lot of cases. We have been their fastest company ever to POC and their fastest company ever to production. That's been awesome to see that's just going on site and spending time with.

Them as you build these agent workflows. Technology's come a long way over the last even 12 months here. But I'm curious what's on your wish list for things that you wish were easier or what you want to come next?

Yeah, you know, I think we're, we've definitely like benefited a ton from the scaling curve of reasoning models, particularly for our kind of very complex documents that we handle and that is, you know, not 100% solved problem. But for us a lot of the document traction stuff we are able to get, you know, much better than human reliability. The next frontier I think really for us is web agents. And so yeah, for any folks listening from the big labs here, we'd love the best possible web agents, please send them our way. We are really, really lucky to be, I think is running a lot of those workflows and has a lot of really great use cases in insurance. It's sort of like the canonical kind of like task is basically doing crud operations on insurance admin panels. I'll be honest, they look very different from booking a restaurant or booking a flight or a lot of the sort of like web agent demos that we see. Yeah. And so a lot of our work is just making those successful. So web agents would be really great. And I think the last one is sort of thinking about longer term so the opportunity for RL and reinforcement, fine tuning in what we do because of the way that we've structured our product and because we are, you know, long AI and what it can do longer term. You know, our, our product is set up in a way where it's not sort of this workflow diagram builder which where you know, the AI agent only has context for, you know, that individual block that it's in. And you would never, you know, the, the analogy I draw here is like, if you had your best, you know, person on your team, you're not going to like, put them in this, in this box and be like, okay, you need to categorize, you know, these documents and not have any sense of what happened before or after in the workflow. And so what we've really built into the product is the AI being able to make the decisions end to end and follow these agent operating procedures. And so what that means longer term is it really sets us up well to benefit a lot from RL because the agents are working from input all the way through to output in a way that is highly gradable, where you can see kind of the accuracy of, of the work that's being done. There's an expected output that you can grade against and create a reward function. It makes it very, very easy to actually do RL for each of these individual workflows for our individual customer at scale. And so I think if you think about kind of the long term of the product, it's not codifying all this stuff into determinism to try to make the agent successful. It's really giving the agent sort of this perfectly modeled world that it can work in and being able to do sort of ro end to end across all the tool calls. And so I think that's one of the big wish lists and sort of areas that we see the world going.

What has surprised you about building with AI or what advice would you have for other founders who are building an AI or thinking about doing so?

I think two things have surprised me. One is how much fun it is. I mean, this is like such a, a great time to be building and there's all this stuff people talk about being able to build faster with AI and certainly that's the case. You know, we're lucky to use all, you know, the cursors and cog codes and codexes that help our team be really, really successful. But even more than that, there's all this stuff that was like, really hard to codify in software that is now really, like, easy to codify with LLMs. And so, like, we've been able to just build workflows that like, traditionally I think would have taken decades to build the software and all the sort of knobs and dials and requirements to sort of get those live into production, because you can offload a lot of that to the agent. So it was a really fun time to be building. The second thing, I think that's really, really important. And maybe it's some advice for founders is to go be a forward deployed engineer and ideally come do the base. But I really do think that is like the best training ground for, for becoming a founder because you go and spend a lot of time with customers, you see, you know their problems and you help them get it into production. And that's, you know, really at companies like you're either building or you're selling, and for deploys, deployed engineers get you both. Um, so that's my, that's my big advice. If you're thinking about starting a company will get you close to problems. It'll get you working on the right stuff and you'll build the right skill sets to build a company. I was very lucky without a Retool. I think I was very fortunate. I think both David Hsu, our CEO, and Brian Schreier, who's lucky, we're lucky to be working with again on Pace, really were very kind to sort of take a big bet on me early on at Retool to sort of figure out how do we build out our post sales motion, how do we take a lot of our new products from 0 to 100? And I think that really, really set me up to start a company in a way where it's never easy, but it has felt much, much more tractable this time than the first time I started a company. And I think that is, yeah, I'm very, very grateful for that. I encourage other folks to sort of find that path and find people like that that will kind of back you again and again. And, yeah, I feel very fortunate for that.

Awesome.

Shall we do some hot takes?

Let's do it.

Okay. What was your hot take from interning at Sequoia?

What did you learn other than Andrew Reid's love of constellations?

I'm not sure if you remember this one, Pat, but we were out on the, you know, like the back patio in the 2800 office, and I think you asked me a similar question, which was sort of, what did you find most surprising about Sequoia? And my answer then is still my answer today, which is, Sequoia is a place that has benefited from an incredible amount of success, but no one ever rests on their laurels here. We were very lucky to be incubated out of Sequoia's office in New York. And every time you walk out of the elevators, there's this sort of graphic that's going on the screen there that says, we're only as good as our next investment and it's awesome. I just love that mentality. And I think that's, like, one thing that permeates also over to our culture at Pace is just like, you know, every quarter, you're set back to zero. Every new customer, you got to make them successful. I mean, they care that you've done this in the past, but, like, you're really meant to, you know, like, you got to make them successful, and so, you know, you're only as good as your next customer. Love it. Love it.

All right, company number two, what are you doing differently this time?

I think there's a couple of learnings that I think have been pretty critical this time, and there's a couple of things we've done the same, which great. We're also great. So one of the ones that I feel like I was very lucky to learn at Retool is one of Retool's values was Retool as a business. And basically, you know, Retool is very focused on delivering value for our customers and revenue. And I think we take a very similar perspective, which is, you know, there's a lot of things you can do when you're starting a company, and a lot of them are kind of distractions. You know, like most times, fundraising distraction, Most times, like, you know, working with, like, I don't know, employers or sitting at the office or whatever. A lot of these things can be like, kind of retraction. Like, you want to make them run, but you want to be spending as much time as possible working with your customer. And so that's like, one of my biggest takeaways is just go. And you can't spend too much time on that. Working with their customer, making them successful, and focusing on moving the metrics that really matter, which is revenue growth, gross margins, a lot of the things that we think a lot about. I think the other big thing is who you work with. And I was incredibly lucky in my last company to have an amazing co founder. And this time with Pace, I actually started the company as a solo founder and have this incredible team that we've been able to build. And it was one interesting thing, which is the hot take people, like, really, or there's like a sentiment that people don't really like solo founders generally seeking as like a. As like an investment. And I actually think that makes complete sense when you're at the very early stages, you know, this sort of, like, existential stages of a company, because, you know, it makes a lot of sense to have a co founder there to, like, help you through, you know, wandering the desert or whatever and making the company successful and very thankful to my co founder for that. My last company. But once you have figured out exact, you know where you're going, it's all execution risk. Like for Pace, everything is execution risk now. Like it's so clear what we're doing. It's just executing you. There are a lot of benefits of starting a company solo founder and the team that you can build. So we've been super lucky. You know, our first engineer was like retail. Second engineer, Yogi, second hire was also in the Sequoia portfolio. Luis who was head of corporate engineering at Loom and both of them and also our broader team, they take on a lot of the roles that in the sort of capacity that I think traditionally might have been kind of just a conversation with like a co founder where you don't actually like involve the team. So a lot of our team was involved in shaping our values. A lot of the team is involved in critical go to market hires or our teams involved in product decision making that might traditionally have just been made by co founders. I think it actually lets you hire a much, much stronger team and build a stronger culture. And the last thing that we definitely kept the same is working with Sequoia and working with Ryan and you Lauren. It's been fantastic and I'm very, very thankful for that. I think one of the amazing things is being able to work with partner that has known you almost over a decade. I was lucky to meet Brian when I was still in college. And I think this is kind of a funny story of how we met was Brian gave this talk and the talk was basically about mentoring and how to build these great mentor relationships. And I think his advice was basically lead with value and then you'll figure out kind of like how to, how to go from there, like kind of giving upfront basically. And I think at the time he dropped this like small Easter egg of hey, I'm actually going to the Princeton campus to talk with the like the Princeton president about you know, entrepreneurship on, on campus. And I'd love to know what to say. And so you know, I kind of was like, hmm, interesting. I think I could probably help with that. So I pulled an all nighter and basically sent in this like 20 page report of, you know, here's where we think, where I think, you know, Princeton Entrepreneurship could, could use your help. And that became, you know, a lot of what, you know, he's since seen basically every step of my career along the way, you know, from, from Sequoia to starting my first company to retool and going to many board meetings where I learned a ton from him all the way through to starting this company. And to be able to have amazing partners like, like, like Brian, like you, Lauren, where we can have a really strong sort of bored relationship, where it sort of almost feels like that sort of like co founder relationship, we're very much like in the trenches together, I think is incredibly unique to me and I think makes Pace a much stronger company because of it.

Thank you for joining the show. It's been amazing to have you and hear the full story behind Pace. That's just beginning.

Thank you for having me. This is a blast.

SA.