The AI Employee for Banks

1. 0:00 – 0:05

   Intro

   1. DHDiana Hu0:00

      [on hold music]

2. 0:05 – 1:00

   What Kastle Does

   1. DHDiana Hu0:05

      All right. So I'm excited today to welcome Nitish and Rishi, the founders of Kastle AI, who just closed a $24 million-

   2. RIRishi0:14

      Mm-hmm

   3. DHDiana Hu0:14

      ... Series A led by Insight Partners.

   4. RIRishi0:17

      Mm-hmm.

   5. DHDiana Hu0:17

      Congrats, guys.

   6. RIRishi0:18

      Thank you.

   7. NINitish0:18

      Thanks.

   8. DHDiana Hu0:19

      What does Kastle do?

   9. RIRishi0:20

      Yeah. No, thanks for having us, Diana. Excited to be here. So Kastle is an AI employee that automates all the operations inside a bank. We started two years ago, uh, starting with mortgage servicing operations, and today we work with 10 of the top 25 servicers, like Newrez, and help them automate their customer service and collections operations across their business.

   10. DHDiana Hu0:42

       And in terms of volume, where are you guys at now in terms of processing payments?

   11. RIRishi0:47

       Uh, so we recently crossed, uh, $2 billion in transactions processed-

   12. DHDiana Hu0:51

       Wow

   13. RIRishi0:51

       ... using AI agents. It's crazy to think that same time last year we were doing about $10 million every two months, and now we do that every day.

3. 1:00 – 2:50

   Why Banks Need AI Employees

   1. NINitish1:00

      Yeah.

   2. DHDiana Hu1:00

      The interesting approach you have is that you are building the company as an AI employee. Why, why is that?

   3. RIRishi1:06

      Yeah. So this is actually one of the, uh, interesting points that we noticed is, you know, I feel like most of the problems that banks face today is that they, in order to grow, to grow revenue, grow, uh, loans, grow deposits, they also need to grow the back office. And one of the interesting things that we saw is that, uh, banks are also forced-- are really bad at change management. It's very hard for such large regulated industries to completely replace their core systems. And so as we built Kastle, we had an opportunity to either, hey, do we go and execute on building a new system of record, or do we help them-- how do we help banks sort of adopt AI in a reliable way and deliver value quickly, uh, without them having to go through this entire process of, you know, changing their core systems, which might take five, 10 years? We were seeing a lot of, um, you know, rapid improvements happen in, let's say for example, coding, where, you know, we use tools like DevN.

   4. NINitish2:12

      Yeah.

   5. RIRishi2:13

      And they've helped us supercharge our workflows. And we, we found that the, you know, banks really needed something similar in order to, like, supercharge their own operating workflows. But there was, like, no good solution out there that was helping them do that. Uh, it was either you change the existing system of record or you had this, like, one-point solution. So that's when we decided that, hey, we have an opportunity here to, like, really help our customers reliably deploy AI across their operations and realize that value that other industries are seeing without having to go through that rip and replace of their existing core systems.

4. 2:50 – 5:00

   Going Back to Zero Before Demo Day

   1. DHDiana Hu2:50

      Now, let's go back to two years ago. You were here at YC going through the batch in Summer '24, and you guys actually applied with a very different idea.

   2. RIRishi3:01

      Yeah.

   3. NINitish3:01

      Mm-hmm.

   4. DHDiana Hu3:01

      Do you remember what it was?

   5. NINitish3:03

      I think it was real estate assistance for, uh, to help real estate agents close more lo- or to close more kind of deals. So AI for home buying, essentially. I think within week one we proved out that we had a pilot, we lost that, and then we realized that this is not going anywhere. And then we started talking to a bunch of more people, went into the AI SDR route, where we were helping essentially other companies message other businesses to source more business.

   6. DHDiana Hu3:34

      I think it was, uh, AI SDR for specifically home buying.

   7. NINitish3:38

      Home buying.

   8. DHDiana Hu3:38

      I think you were helping-

   9. NINitish3:39

      Yeah

   10. DHDiana Hu3:39

       ... all these brokers-

   11. NINitish3:40

       Yeah

   12. DHDiana Hu3:40

       ... to find more leads to-

   13. NINitish3:41

       Yeah

   14. DHDiana Hu3:42

       ... buy homes, and you actually made progress on it. You were working on that for most of the batch.

   15. NINitish3:45

       Yeah.

   16. DHDiana Hu3:46

       It was around the summer. I think you were working on that through June, July, and beginning of August, and you got to about 4,000 in MRR.

   17. NINitish3:55

       Yeah.

   18. DHDiana Hu3:55

       Just like, okay. But something happened and-

   19. NINitish3:58

       Yeah

   20. DHDiana Hu3:58

       ... you decided to kill the idea one month before Demo Day and went back-

   21. NINitish4:02

       Yeah

   22. DHDiana Hu4:02

       ... again to zero.

   23. NINitish4:03

       Yeah.

   24. DHDiana Hu4:03

       This is the second time you went back to zero.

   25. NINitish4:04

       Yeah. [laughs] Yeah, no, I clearly remember we were actually, um, in Phoenix and we were driving, or, you know, on the way to the airport, uh, in SF, and Nitish and I were like, "W- w- where are we, where are we doing?" Like, we, we don't really see, um... We couldn't see ourselves, like, building a really large company, uh, on that idea. And most importantly, like, we, we, we didn't feel that our customers-- this was, like, existential for our customers. That, like, if we disappeared tomorrow that they would ac- absolutely be, you know, disappointed that, like, they no longer have Kastle. Uh, it felt like we were, like, a nice-to-have for them. And Nitish and I, like, we, you know, we, we really cared about, like, building something for the long term, and we wanted to do, like, go back to the drawing board, uh, no matter how painful that was-

   26. RIRishi4:54

       Yeah

   27. NINitish4:54

       ... and restart over, uh, to build something that, like, our customers, they couldn't live without.

5. 5:00 – 6:43

   Finding the Idea for Kastle

   1. DHDiana Hu5:00

      So you were sprinting. This is only 30 days before Demo Day, and you went on this sprint on doing a lot of user interviews. You were flying all around the country to meet people. You went to conferences for two weeks. And tell us about how you eventually landed in this idea of around agents for mortgages.

   2. RIRishi5:20

      We went all-- to basically every conference that we could find. It was a lot of awkward conversations, just standing outside conferences as people walked out [laughs] and trying to, like, cold meet them and talk about their problems. But we basically did that, and we actually got pretty good at it. And-

   3. DHDiana Hu5:36

      Got less awkward

   4. RIRishi5:37

      ... and less awkward at it. [laughs] And so it was actually at one of the conferences here in San Francisco where we, uh, learned about 10 million Americans making their mortgage payment every single month over the phone.

   5. DHDiana Hu5:49

      You told me that, and I was shocked.

   6. RIRishi5:51

      Yeah.

   7. DHDiana Hu5:51

      It's like, there's people that call over the phone to pay or be reminded for their mortgages? It's like, is it-

   8. RIRishi5:56

      Every single month.

   9. DHDiana Hu5:57

      I was like, what?

   10. RIRishi5:58

       Yeah. And-

   11. DHDiana Hu5:59

       I was shocked

   12. RIRishi5:59

       ... and all of these calls were actually being handled by a human agent. Uh, in the contact center because, you know, mortgage lenders just don't have as much... They are very highly regulated, and it's very risky for them to outsource these operations to other outside countries. So all of these calls were being taken in the United States, in places like Phoenix, Dallas, and it's all the, all that, like, the mortgage lenders could talk about, that if someone could solve this problem for us, like, that would be a game-changing experience. And no one... And voice AI was, like, relatively, like, new at that time, and no one was really doing it to, like, process payments as for such a regulated industry like mortgage, and that's, that's how we got

6. 6:43 – 8:18

   Landing Their First Customer

   1. RIRishi6:43

      our first break.

   2. DHDiana Hu6:43

      Do you remember how, uh, you got your first customer? The, because by that time you were running out of time.

   3. RIRishi6:48

      Yeah, yeah.

   4. DHDiana Hu6:48

      There was only two weeks until Demo Day. You got this funny insight, and-

   5. RIRishi6:54

      Yeah

   6. DHDiana Hu6:54

      ... what happened? I think it took another two weeks to-

   7. RIRishi6:56

      Yeah

   8. DHDiana Hu6:56

      ... get your customers.

   9. RIRishi6:57

      Mm-hmm. Yeah. So we, we were like, "Okay, how do we speed run, uh, this?" So we went to another conference where it was actually... We found out about this pitch competition, and it was in San Diego. Before our time on the stage-

   10. NINitish7:10

       Yeah

   11. RIRishi7:10

       ... Nitish was in the background.

   12. NINitish7:11

       [laughs]

   13. RIRishi7:12

       You wanna talk about that?

   14. NINitish7:12

       I think Rishi was still going around the conference trying to meet people, and I had found a corner of the auditorium and was just kind of finish our demo in time, and we went up to the stage, we started the demo. Uh, it was a phone call and just to show whether we can take a payment. First time he called in, it didn't work. Uh-

   15. DHDiana Hu7:31

       On stage.

   16. RIRishi7:31

       On stage.

   17. NINitish7:31

       On stage.

   18. RIRishi7:32

       In front of hundreds of people.

   19. NINitish7:33

       So we blamed it on his phone. We said, "Okay, it'll work with my phone instead." So I gave him my phone. Second time we called in, it didn't work, and then I said, "Oh, I forgot to turn it on." [laughs] And then the third time we called in, it actually ended up working, and, uh, we, we were really scared that, you know, this didn't go as well. But, uh, turns out we actually won that competition, and that created a ton of inbound for us and helped us land our first customer.

   20. DHDiana Hu7:54

       In spite of a demo malfunctioning.

   21. RIRishi7:56

       [laughs]

   22. DHDiana Hu7:57

       That's a, that's a cool story. So you got a bunch of inbound from that, and I remember you guys just closed on nick of time your first customers just a few days before Demo Day.

   23. RIRishi8:05

       Yeah.

   24. DHDiana Hu8:06

       Cool. Then you went off to the races, and then after that you guys went in to build and close one of the largest servicers a couple months later?

7. 8:18 – 8:59

   Moving Into a Customer’s Office

   1. RIRishi8:18

      Yeah. So we had a f- uh, a few early customers that we were prototyping with, um, and then we, uh, got in touch with Newrez, which is the third-largest servicer in the country. This is the time where we... You know, they had a similar problem. We knew that they had this problem. So to actually make them live, we moved the entire company to Phoenix, and we moved into their ba- offices and, you know, k- uh, didn't come back until they were live.

   2. DHDiana Hu8:45

      So you were cranking it until you went live.

   3. RIRishi8:47

      Yeah.

   4. DHDiana Hu8:48

      Now, fast-forward, now that you're live and now processing 2 billion in transactions, where does Kastle go in the next 5 to 10

8. 8:59 – 11:47

   The Future of AI Employees in Banking

   1. DHDiana Hu8:59

      years?

   2. RIRishi8:59

      What we've been able to do is prove that ap- applied AI autonomous agents can work in one of the most regulated operational environments, uh, in financial services, which is mortgage servicing. And as a result of that, we've actually built a lot of technology underneath it that allows these agents to be highly effective in real-time settings. We're basically taking that operating model and that technology and now applying across the consumer le- lending life cycle and across, like, a bank's operations. So we initially, while we initially started with mortgages, we now have large banks using Kastle's AI employee to service, uh, auto loans, credit cards, personal loans, HELOCs, but also, like, originate loans. We think five years from now that, you know, Kastle can be the, the number-one AI employee, uh, that works a- as a coworker across every operational, uh, financial institution there is, doing the work alongside human agents and automating the, the high-volume, labor-intensive work, and freeing up time for complex workflows to be handled by human agents where they, where they need the judgment. And this is essentially, like, to say that, like, there is a ton of demand for financial products, and banks want to be innovative and lend to their community, build a better experience for their customers. The only thing that stops them from doing that is them not having the operational bandwidth or resource in order to, you know, provide personalized experiences for each of their customers, and, uh, Kastle's AI employees unlock that.

   3. DHDiana Hu10:40

      So one of the hard things you've done is basically you are enabling your customers to basically prompt the agents. But inherently these agents run LLMs, which are non-deterministic.

   4. RIRishi10:52

      Mm-hmm.

   5. DHDiana Hu10:52

      But you're working in a regulated industry, so somehow you build the hardness that makes your customer changes be deterministic and be compliant. That's pretty hard to do. And you guys are growing a bunch, so you're hiring a bunch of roles. Tell us about what are some of the roles that you're hiring for.

   6. RIRishi11:09

      Yeah. So we're hiring across the stack, uh, with, with, uh, the Series A. We're now starting to work with, you know, some of the largest banks, uh, across the country. So we're, uh, investing a lot in, like, applying AI in the real world and building these systems around it, so we're growing our applied AI team, growing our product teams as we get into, like, multiple different products-

   7. NINitish11:30

      Mm-hmm

   8. RIRishi11:31

      ... uh, and then also scaling our, uh, infrastructure teams.

   9. DHDiana Hu11:34

      All right, guys. Thank you so much for coming, and congrats again on the Series A.

   10. NINitish11:37

       Yeah.

   11. RIRishi11:38

       Thanks so much, Ana. Appreciate it. [upbeat music]

Episode duration: 11:47