Pace raises $10M Series A from Sequoia to automate insurance back-office with AI agents

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feed. Without further ado,

we have Jamie from Pace, the co-founder and CEO in the rich room. And let's bring him in with you today. Jamie, how you doing?

What's going on? Hey guys, thanks for having me.

Thanks so much for joining. Uh, first time on the show. Please introduce yourself and the company.

Yeah. Hey, I'm Jamie. I'm the founder and CEO here at Pace. Pace is the AI operations partner for the world's leading insurers.

Amazing. Give us news.

We've been waiting for this company

for sure. We love We love all applications of AI. Uh, give us the news. What happened today?

Yeah, absolutely. We're super excited to share today that we raised $10 million from Sequoia Capital. Whoa.

Thank you. Yeah, we're super excited to have Brian Shrier and Lauren Reer representing Sequoia on our board.

Amazing.

Okay, talk about uh the go to market. How much of this is just like there's a few big partners that you need to get on board versus something that's more self-s serve, bottoms up. Uh what's the customer adoption been like? I mean, what's the overall progress of the business? Are you live? Do you have customers?

Absolutely. Yeah. So we're we're lucky to be live in production today for some of the world's largest insurers. We're working with

large carriers like Credential, specialty mutual groups like the Mutual Group and then all the way through to sort of tech forward brokers like Newfront.

Sure.

Um and I think you know in our industry in insurance about $70 billion is spent every year on on back office BPOS.

Okay. A lot of that is driven by sort of the largest carriers.

Yeah.

So you're basically the BO's worst nightmare. Yeah. So what I think Yeah. Yeah. What what like like walk through a specific back office procedure. Is that like claims processing? Is it scanning PDFs? Like try and ground it for me a little bit more.

Yeah, it's a great question. So we're starting off, you know, with we're primarily focused on this back office operations. And what that really means is sort of three big buckets. It's new businesses coming in, taking in new risk submissions, you know, document processing, calling back when there's missing information, writing back into internal systems. It's a policy servicing, so any changes you're making, adding new products, things like that.

And then all the way through to claims, so whether that's first notice of loss, calling in when something, you know, happened, went wrong, all the way through to quality assurance, how do we make sure that every claim gets paid correctly every time?

Yeah. How much uh like what is the actual tech stack? I mean, I imagine that you're not training your own models, but have you done your own RL environment on top of these? And and do you do that at a company level and then everyone gets sort of like the same AI or are you doing it on a per client basis? How much fine-tuning is going on?

Yeah. So, for a lot of our processes, because we've started by focusing on a lot of these BO workflows, they're already codified as sort of these standard operating procedures. It's a great start because

it's already well chunked up for agents. the accuracy bar for where BPOS's are today is pretty low and there's a lot of opportunity to improve that. Yeah. And to be able to sort of, you know, run more accurately, more scalably and and and uh more efficiently for these customers. And so we tend to start primarily with those and we take these standard operating procedures and we convert them into what we call agent operating procedures. So this is just natural language instructions. You can think of it, you know, like a document,

you write a markdown file, like a skill.

It's a skill.

Uh yeah.

Yeah. And so what the main thing that we're really bridging here is these are processes that are going to be run hundreds of thousands of times. You know for us we're processing tens of thousands of tasks a month

and critically this these agents have to reliably run you know many tens of pages worth of a process

with very insurance specific tools and logic. So that's you know certainly a lot of the document processing pipelines that we've built on top of you know OpenAI anthropic Gemini but also uh it's going into deep industry integrations it's voice calling and multimodality but also uh kind of actually being able to take actions in systems that don't have APIs you know in insurance we see everything from the cloud you know products but a lot of it is desktop apps even like green screen CLI I'm talking like main IBM systems so how do we talk about the uh

one one of the things that's most exciting to me is a potential like speed up effect where you're not deal like theoretically like you're if you're uh trying to get a new policy or submit a claim or anything like that you could get to the point where as long as you're there to provide information in the process as the as the sort of like client of the of the insurance company theoretically you can get like responses I'm imagining like much much faster because the agent is effectively able to like get all the information that's needed to make a decision and maybe there's still a human in the loop. But uh what are the kind of implications of that for the actual insurance companies even just from a revenue standpoint if they can just like speed up all these processes you know by an order of magnitude?

Yeah. So for a lot of our customers thinking about running these agents 24/7 365 days a year and I think the the real advantage here is not ever having any backlogs being infinitely scalable and so that's really important for you know our customers on the front end when they're thinking about new business and new risk. How do we you know win this over other uh carriers? But then also on the claim side, you know, for a consumer and one of our very first customers, we went live right before hurricane season and you know, you can see, you know, there's, you know, cat season, the end of the year, there's just like a massive uptick in the number of claims when there's, you know, a hurricane that happens and people can be waiting, you know, days, weeks, even months to sort of get um, you know, a resolution because, you know, there can just be massive backlogs from, you know, a huge spike. And so agent

that entire time they might be they might be homeless, you know, in a hotel, Airbnb or whatever. It's like an incredibly terrible experience to be kind of in limbo waiting for your insurance provider to like just respond and give you. I mean, we uh we know some people that lost their homes, you know, around this time last year and just like getting responses when that's like if if you're an individual who lost your home, what's more important in your life than like getting clarity from your insurance provider so that you can make so that you can kind of move forward with with your life. So, I think that's that's uh super exciting.

I'm interested to hear about uh your pre previous experience cheer and then retool. uh why go vertical with this particular solution versus something that's more general and industry agnostic? Um what did you see in this particular market that you decided you needed something specific and then do you have a plan to like expand or I mean the TAM seems so big that maybe this is just enough. How are you thinking about that?

Yeah, so way back kind of where this all started is I actually grew up between London, New York and Bermuda. The through line there being insurance.

Yeah.

Wait, are the rumors true? Like what is Bermuda like? I feel like I've heard stories about it's all just like accountants walking around all day long.

I went there I went there for a wedding. It was really nice. I didn't see any

lots clock anybody as an accountant.

Okay. Anyway, yeah. But what was your experience in Bermuda?

I It's incredible. I think the uh but there is a massive sort of reinsurance industry on the island. So that's like very much the home of that. Um anyway, so so you

you were born you were born in insurance.

Yeah.

Yeah. I grew up around the industry for a long time and then um

uh started my first company was a data integration business that was bought by retool.

Let's go.

And at retool um you know I think it's

Thanks. I think, you know, it's not um always immediately obvious that actually a lot of um the sort of custom software that's built in the world is for, you know, a lot of financial services companies because they're highly operationally intensive. And so, Retool had a ton of, you know, very scaled financial services companies. And as a deployed engineer, I was sort of lucky to be on kind of the first lines, you know, with some of our biggest insurers, helping them to handle tasks like policy servicing and claims and trying to make them, you know, more efficient with software. And I think the big opportunity kind of came around chat GBT where we saw you know a huge uptake from a lot of our startup customers u and then there was this this opportunity to build a vertical product that would really serve you know the the largest insurers. So that was the the impetus in terms of where we're headed in kind of the longer term you asked about. I think there's a lot for us to do just within insurance. But if you broaden out to financial services BO more broadly, it's about 400 billion of spend, which is pretty much exactly equivalent to the entire market for enterprise software.

That's insane. Um, so so yeah, I I imagine that when you're concern when you're going into a discussion with a large insurer, you're not like that, whoever you're pitching is not saying, well, I vibe coded this on Sunday and it's working pretty well. why should I go with you? Like they truly don't have anything else installed and so it's sort of a green field I imagine. Is that right?

Yeah. I think you know in insurance we're all in like kind of the business of trust and so you know that's why a lot of our work is with you know the largest insurers on mission critical tasks at massive scale. And so

what about yeah sorry yeah what about tradeoffs on uh inference and different models? You mentioned sort of being multimodel using different frontier labs, but are you finding in the product that you're building that you're uh that you're relying on cheaper models for certain tasks and then harnessing and wiring that all together so you actually can drive down cost or can you just throw the most expensive frontier model at every problem because it's so valuable?

Yeah. So, you know, I think we use a mixture of kind of models to be able to achieve, you know, what we need for the right task. You know, if you think about parsing a many hundred claim, you know, page claim file or policy document, you might use a small model to kind of zoom in on the areas that are most important and then use an expensive model to say synthesize, you know, what's the most important and what's the answer.

Sure.

And then, you know, I think what's more interesting, you talked a little about kind of fine-tuning an RL. you know, we're um kind of like the perfect set of use cases for for web agents being able to navigate um you know, these applications that don't have APIs, be they, you know, green screen CLIs or desktop apps.

And so we've actually been, you know, working on a handful of fine-tunes um and creating basically RL environments that are these sort of CRUD applications.

They're very different from say, you know, the book me a restaurant or this flight type of demo. It's much more about you know take this repetitive task and fill out this form thousands of times. It's kind of the ideal case for I think a lot of these computer use systems. So we're seeing a lot of success there. I think we are just kind of rounding the corner of the scaling laws starting to work on those kind of feels the same way that maybe like voice did you know 6 to 12 months ago.

Y

you guys have pursued a forward deployed engineer go to market. Do you uh sounds like it's working well to date. Do you think this is something that will still be a part of the kind of core motion in a few years or is this something that makes sense to uh kind of get off the ground till you understand kind of like what the typical stacks are within your within your market and then eventually you'd move beyond it.

Yeah, I mean I think the forward deployed motion is definitely something that you know really came naturally to me. It's kind of like the DNA of of our company because that's you know also what we were doing at retool when it was you know much earlier I think on that.

Um

I think for us you know going and working with our customers being able to speak their language getting to you know really understand the comp the complexities of these many page standard operating procedures and make them successful as agents. I think it's really important for us to be sitting you know side by side with them. That being said, I think a lot of companies sort of talk about FDES as sort of being the end state or like that is like, you know, the best part about my business and why I'm like Palunteer. I think for us like it is um you these RFDs are also really able to ship code in the codebase and so they are actually constantly making the product better and that's really important like I think our product should just be continually giving our for deployed engineering team more and more leverage um so that they can you know be able to to complete more for our customers

some FTE goes into a business they're badge they're in there they're interact interfacing with the green screen you know local on premise mainframe software, they write some binding for it or some screen scraper or whatever you need to do and then they can ship that back to HQ and then the next time you run into that you have uh you have an integration effectively. Is that like the basic pattern of that?

Exactly. I think there's just a lot of things that are required to get these agents running at scale that you know

customers don't need to build from scratch every time. I I do think it's really important though that we also enable our customers to be able to build their own, you know, agents. And for us, you know, some of our customers are at tens of agents live in production. We may have built, you know, help them build the first one or two in an FD motion,

but just because it's natural language,

they're able to sort of turn around two through 10 without us having

and they're still running those. And so they're running number three through 10 on your platform through your system, but they're defining them and architecting them themselves. Exactly. Yeah. Just announced good business. I love it. Congratulations. Uh fantastic to meet you.

Yeah. Well, you you put together a great team, too. I'm excited that uh Verun, one of my former teammates, is is over with with you at Pace now. And uh yeah, very I'm sure you'll be back on

this year, maybe this quarter.

Maybe. Anyway,

thank you so much again, guys.

Yeah, we'll talk to you soon. Have a good rest of your day. Goodbye. Let me tell you about Vanta automate compliance and security. Vanta is the leading AI trust management platform. And we have Ben Oh, sorry.

Quick before we bring in Ben, uh some unfortunate news. Palmer uh got tied up uh with DC,

but we got Jeff Miller coming out.

Jeff Miller is going to come out.

It's Miller time.

It's Miller time, which we're excited