Salient: The Fintech Startup Processing $1B+ in Loans with AI

0:04 So today I'm super excited to have Salient here who are announcing a massive series A led by A6C for $60 million. Congratulations. Tell us a bit about what Salient does. >> So Salient is an AI loan servicing platform for consumer lenders. we started off with auto loans but now we help manage credit cards, mortgages, anything that a consumer can take a loan for. So we work with non-bank lenders like Westlake Financial, Exert Finance, American Credit Acceptance, as well as three of the largest publicly listed banks in the US.

0:44 >> And you guys went through YC not too long ago. It was just two years ago. This is a crazy ride to get there. So working with the largest public banks in the US, that's wild. Where did you guys get started? How did you find this idea? >> so it all kind of started from my time at Tesla. So at Tesla, we were giving out loans to help drive new car sales. And what we found was that the cost of matching these loans was very, very high despite the fact that we were giving loans to people with great credit scores all in California. And when we kind of dove into what is it that's really driving the spend, I think look realized that you know we could actually automate a lot of this stuff using AI.

1:31 So I think we were very fortunate with the timing on two crucial aspects. The first one was the advent of closed source models like GPD 3.5 coming out that led us to like pave the way for really really high quality demos. The second big leap came from when it came time to scale. We realized that the advent of open source models that were available for fine-tuning such as the Llama series really really helped us scale from like a few hundred dials per day to over 100,000 dials per day.

1:57 along with that came VLM which was the open source inference library that really really allowed anyone like us to be able to grab one of these llama models serve it up to millions of requests per minute. >> You were one of the first companies to really take advantage of the alternative with opensource big models. So LMA 2 that was released in July 2023 was the moment for you. Tell us about that. What what what was that specific about the model that made it possible?

2:26 >> So I feel like that was a really big week for us when that model came out because for us it paved that big gap between having a high quality demo but still only doing like a 100 calls a day to being able to sustainably scale to hundreds of thousands of calls per day. >> I remember when you showed me a demo. I got this call from the voice of Steve Jobs. it was very convincing. This is starting to work. AI has passed this moment of the touring test and I was like impressed. And you got that working around that summer and with that demo is how you got one of your first big customers. Tell us about that journey around that was in the summer fall of 2023.

3:12 Yeah. so I think we cold emailed every single auto lender under the sun. And >> how many emails were you sending? Like >> 500 a day. I was just like sitting like I was I remember being in a room just looking at a laptop from 8:00 a.m. to 9:00 p.m. just sending emails. And I think this is probably the best thing that's ever happened to the company was getting a response from Westlake Financial and they are a massive auto lender in the US. you know, more than $25 billion of assets and they were down to meet with us and you know, at that time it was just me mccun working out of my bedroom in San Francisco and here was this massive company that I think saw the potential of this technology and knew that we were the people that could actually do it. And so while they were thinking about other vendors, what we ended up doing was just moving there.

4:03 like we packed up our bags, we left SF, we moved right next to their office and we spent I mean a year and a half really getting them live, you know, and I think it's thanks to their embracing technology that they want to take risks, they want to be at the cutting edge and they're willing to partner with us that we're allowed to really build, I think, cutting edge solutions. and so thanks, you know, we've now done more than a billion dollars in process transactions in the US. We've interacted with more than three million unique US borrowers. We dial more than 400,000 times per day. And I think it all all came from that cold email.

4:39 >> Wow. That cold email and that Steve Job call demo was what it took. I think that was definitely one of those magic demos that you you got to like use this. I was I was so impressed. You guys got to a couple million in revenue a year with just the two of you. How did that happen? >> I think it's honestly just being like relentlessly customer focused. Like we had these amazing tailwinds of AI getting better and better every month. I think the thing that you know mun does really well and I do really well is we weren't just in the lab like we were with our customers every single day. We were plotting out all their workflows.

5:19 And when it came to actually deploying the product, we didn't just say here it is, go figure it out. It's like here it is. We'll put it into production with you. And I think that kind of like relentless like commitment to customer success allowed us to scale very very quickly. >> I think the thing that was very hardcore just double clicking on what you said is you move from San Francisco to live next to your customer until they were successful. You were effectively deploying the forward deployed engineer playbook that Palunteer got it down to get this massive enterprise live. And in that process is what we've seen at at YC is a lot of these AI companies like yourself that are going very quickly is what it takes to go live and quickly grow. So you went from then couple million a year passed and you got to tens of millions in revenue and the team was only 10 engineers.

6:19 How do you guys make that happen? >> Honestly, it's due to the fact that how great our first 10 hires were. We hire XYZ founders. We hire really young crack talent and we hire people who want to move really really quickly. And I think what we're learning in this current like in our experience of like deploying solutions is actually it's not the size of the team that really matters. It's like how committed is your engineer to getting a working product out and having that I think like relentless push from every single one of our engineers who makes us better, they make our product better, they make our customers happier, I think is like it's all thanks to them.

6:55 I think one thing we realized early on was that we are going to make every one of our engineers responsible for a major customer. They were not used to that coming in from a couple of years at Google. They became the account manager. They became the forward deployed engineer. they were talking directly to the CFO and to the VP of servicing and they were completely unprepared for that but like three weeks in it was like they've been doing it every day.

7:13 >> So what are the kinds of people that you're hiring now with all this money that you raised? >> Yeah. So I think like now that we raised this money, we've had so much pent-up demand in the market for so long. Like the biggest constraint in our growth has just been ourselves. And where we want to go with this is hiring really, really high agency engineers, high agency product managers, salespeople to help take us no longer from 0 to one, but from one to 10,000.

7:38 >> I think voice AI became a very good foot in the door for a lot of your customers, but now you ended up doing way more. I think one of the challenges that we had not yet anticipated as we were trying to grow was that it wasn't just about having high quality voice AI. It wasn't having like low latency good interrupt behavior. It was about scaling to hundreds of thousands of calls in a safe manner according to US regulations. It's about making sure that we only dial an account when it is legal to do so. And it is ensuring that our system is able to like simultaneously manage thousands of dials we do simultaneously. And as part of that, you figure out a lot of really hard technical pieces. You guys got to PCI compliance in record time.

8:24 >> Yeah. I think the big thing is like we work in such a regulated industry. We have laws at the federal level. We have laws at the state level. We have, you know, in some cases even laws at the district level. And so I think a bulk of our time in building the solution is how can we do this in a way that protects the lender but also makes the consumer feel safe. So it's building in safeguards on bankruptcy protections on legal representation on codifying TCPA.

8:50 So you know we've now become big experts in the US consumer lending space. >> So what does the future look like for you? >> Our goal is to be the system of record for every loan in America. When a lender books a loan, it should come onto our software and that should be touchless for that lender from start to finish from the date of origination to the date of charge off. And that means us building a CRM. It means us building an accounting system. It means us building workflow automation tools. It means us building an an AI contact center.

9:20 And the end vision what salient does is like we should lower the cost of credit for the American consumer. Like those costs should no longer be blindly passed from like you know a lender to a borrower. We should make that spread almost zero. And so that's what we're trying to accomplish. Thank you so much Arian Mukun for coming and congratulations on your massive series A. >> Thank you so much. >> Thank you so much. Really appreciate that.