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

0:00And I think if you sort of try to shoehorn AI into these tiny little boxes, and then you add 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 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.

0:45on 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.

1:21Enjoy the show. Jamie, 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, you know, a couple of decades, a 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.

2:06Can I ask about the path that brought you here? So top of 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 funny is I actually started there. So I grew up around insurance my whole life. I was born in London, grew up between New York, London, and Bermuda, which are kind of all 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.

2:43And 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 i think a lot of people think of retooling think startups but your retool is really attacking a lot of this 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 um were using retool all the way through to the most tech forward ones so you know had like the ethoses and the vouches you know those types of businesses all the way through to progressive and berkshire hathaway and businesses like that at that point is 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 that you know these companies are handling um it was really cool was a lot of times people would build retail applications that would actually help to streamline some of their BPO operations.

3:59So I got to sort of see a lot of the problems I think, you know, my dad was also dealing with in his business of, you know, policy administration, claims, submission intake, you know, a lot of the core workflows that are critical to insurance operations. And at the time, you know, I think with software, you can make a little dent on BPO spend. You can make it like 10 % better. And I think that was like a lot of the promise of, you know, OCR and RPA and some of these sort of prior technologies. But what happened after the chat GPT moment, 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.

4:44And so instead of, you know, augmenting outsource services with software, you could actually go and completely replace the vertical service. And so insurance is a particularly good starting point for that because the lingua franca of the industry is emails and PDS. 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 with the lack of an API, the sort of alternative is 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 manual data entry handling of these documents and emails, is now also what's perfectly positioned as for AI agents.

5:36And 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 the main thing that I keep the main thing, which is, you know, we are very lucky right now to be with lots of incredible customers. And you have this sort of opportunity.

6:08You could build, you know, a very large company. Just, you know, being a VPO is, you know, there are multiple tens of billions of dollars of public companies that, you know, are running at, you know, 10, 15 % gross margin with, you know, tens to hundreds of thousands of people doing this work. And obviously those are great businesses and, you know, exceptional teams. But for us, we think there's like a much, much bigger outcome. And so when I actually talk with our team about kind of 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.

6:48The goal of what we're going for is how can we take that from, you know, 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, you know, 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 margins.

7:26I think it is important. And I don't think it's important to necessarily have 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 trying to plug into these email and document processing workflows, I'm curious what you're seeing is working in practice.

8:05We'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 to do that work sort of end to end. And so I think, you know, the prior generation pre-AI when you're attacking these problems, yes, like extracting data from unstructured documents is great, but it's sort of just like one part of the problem.

8:43It'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 to 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.

9:17And the reason is there was this sort of human judgment component and lots of edge cases that came 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, you know, So 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.

10:00So 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 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 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, you know, critical in these critical workflows.

10:53And 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 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?

11:32And 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. 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.

12:05So 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 claim side. So first notice of loss or claims intake, getting that information into their internal systems.

12:46And then once you do that, your 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 claim side, the same thing. you know, from claims intake all the way through to payout and quality assurance. That's usually the path we take. What's the typical before and after situation? You know, human EPO and then pace AI BPO. Like 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.

13:30And, you know, with company BPO, I think, you know, the NPS 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 near for some of our customers who've been able to achieve, you know, 50 to 75 percent cost savings, you know, in a very short period of time working with them. um but usually what happens after that is you realize that there are a number of other things that are actually much more important to them um 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 to do so uh an example of that is you know 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.

14:25And so if you can get back much faster, you can actually close business faster. Same thing when we think about scalability. So like, you know, around BPO work is not just kind of like, you know, perfectly linear over the course of the year. It's highly seasonal. So you can think like a Jan 1 enrollment period or, you know, hurricane season like we're in right now. And so, you know, if there's a massive spike of hurricane claims, you have to go and spin up a bunch of other BPO 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.

15:01Those are a few of them. And then I'd say the last thing is sort of observability, which is a lot of this BPO work in the past has been sort of a black box. It's very hard to see kind of like what was getting done, what stage 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 now that you're sort of able to do this work with agents and sort of be able to literally see exactly the tasks it's taking and the reasoning and the why, is you can now surface a lot of that information back to customers so that they can actually redesign their workflows, not just do the same stuff that they were doing with their VPA.

15:41I'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. But then we also see our portfolio companies not failing. What do you see? 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 is like, oh my gosh, like what's going on um you know i think that the truth is is definitely that getting ai working in production is not like you know a a done deal when you walk in um and there are certainly like you know challenges that um you know we've seen across the industry of like you know other vendors or or building in-house uh where you know 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.

16:47And 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 anyway, I think that might be a little bit of a 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 um 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.

17:40And so that immediately, you know, as 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. Um, 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 their 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 uh 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 um this is a team i was really lucky to build out at retool and i think at the time deployed engineering you know after me palantir like retool had maybe one of the most scale deployed engineering teams.

18:35And 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 it takes to make the customer successful. So it's understanding the use case. It's helping them 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 pilots successful.

19:13One 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? So how do you think about sort of 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 got to 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.

19:52um 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 uh you know top five carriers at scale uh and then sitting them right next to you know a uh you know former yc founder that has you know worked on you know an ai company that is like at the cutting edge of you know 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.

20:30And then, you know, in the insurance world, we've been lucky to find some, you 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 the world is going. And I've been super, super impressed by the 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 on the topic of forward-employed engineers, 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?

21:13Or 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 this and 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 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, you know, get successful with the product and then going on to larger and larger use cases, you know, where we can really expand into this sort of like very large set of budgets that they have around BPO spend.

22:00And 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 onsite 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 it. 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.

22:37Just 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 before deployed engineering. But I agree with you that I think the long term is you like software codify workflows is ideal, right? You know, we enable our customers to build inside of our product, you know, and it's not all just forward deployed led.

23:14You know, we maybe get the first one or two up and running and we have customers where we've turned around and now they have nine, 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 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.

23:46For us right now, you know, basically 80 % of our team is engineering. and then we have forward deployed engineering at the very top we have this go-to-market team um and i think as long as you keep that in balance we're going to be able to really get that flywheel going yeah 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 you know maybe we can check back in you know 10 years as we're going public i actually I 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.

24:32Yeah, I think we're pretty well aligned on this. You know, 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. You know, 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.

25:08Sierra 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.

25:42you 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 i think forward 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 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 have a one of our other values is be the rock which is basically like our customers right 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 is and we are there for our customers every single day um and that's what we that's what we want to do and uh 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 of 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 that 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.

27:18And 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're like, what is 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 um keys are getting short uh the last one is uh my favorite which is set the pace so uh you know you both know me really well and uh i think uh i have like a pretty high level of like urgency and maybe impatience at times maybe just like you know uh uh 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 uh 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 uh you know i think it's it's ours to go and and and make this future reality and so i feel So the pace really encapsulates that.

28:26You 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 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. I mean, there's a lot of room to expand, but 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 Reid, his favorite company is Constellation Software.

29:08I 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. Figma, yeah, okay. Or Robinhood or Klarna or, you know. We can rerun this. favorite non-sequoia portfolio company in the public markets not investment advice is Constellation and