0:03Welcome to the Credit Bubble, the podcast about the people and decisions behind the debt capital shaping our economy. I'm Derek Brunel. In each episode, I talk with credit officers, originators, and finance professionals about the moments that matter, from deal structuring to market shifts to the personal journeys behind the numbers. In this episode of the Credit Bubble, I sit down with Jared Shulman, founder of Daylet, to trace his path from the hedge fund world into AI native lending and collections. Jared tells the story of visiting a Manhattan trading floor early on in finding a hundred Bloomberg terminals staffed by just two people. Technology, in his words, had completely wiped out the trading desk and going home that day to teach himself to code. That conviction shaped Dalit, formerly Lendika, a business built on the real time transaction data between buyers and sellers that a balance sheet and a Dun and Bradstreet report don't capture. We dig into why character matters as much as the financials and small business credit, why forty five percent of b to b invoices get paid late, and how AI collection agents do the work overworked receivables teams can't. We close on agents negotiating both sides of the supply chain and finance teams moving from operators to architects. Now let's jump inside the credit bubble with Jared Shulman. Alright, Jared. Welcome to the credit bubble. How you doing today?
1:29I'm I'm well. Alright. So are we in a bubble?
1:35Is that start with a hot take? There we go. Yeah. Just kidding. That was it's kind of a play on words there. It's cheeky. It's cheeky. I try to be a little clever with my media. But It's good. Maybe for for starters, give me a little bit of a background. I know you went to Babson. Yep. Um, I grew up in Vermont. I'm familiar with New England schools. K. So, uh, and my brother-in-law actually got an MBA from from Babson. But, uh, maybe you can just give me let's just start at the at kind of that that stage of your life and then walk me through where where you are today.
2:09Sure. Yeah. I I I so graduated from Babson College. I I love Babson. This we won't use this as a a plug for Babson, although, like, I I certainly would love to do that. I actually grew up on the campus. My my, you know, dad was a professor at Babson and moved the family from Detroit. And so I I went there. My family went there, all my sisters. So we're we're hard for Babson. I even now, you know, do some guest lecturing in the finance department there. So Babson's, you know, very important part of sort of the background. But, you know, out of school, you know, I I knew I wanted to be an entrepreneur. I didn't, you know, have you know, I I would say I didn't have all the the the tools ready to go and build a business. I I had started, like, some small, you know, tech startup out of college called Angel Intro. You know, we were basically trying very early days of machine learning, trying to match founders with investors and, you know, more than just, like, their network, trying to understand, you know, interest and use case and etcetera, but never got it off the ground. I said, alright. Let let me have a a hard skill to fall back on. And so I, you know, found myself in the hedge fund space. I, um, you know, am a CFA charter holder, so I went through that process. You know, I I I found my way into credit, but I I I think that, you know, may maybe one of the defining stories you know, I I don't know. I'm happy to go into details, but it's one of the defining stories of of my career, I think why, you know, I'm I'm super passionate about AI today was about six months after I graduated. You know, I'm at a hedge fund. And, you know, my my my thought was I'm gonna be a trader. I'm gonna be you know, whether it's buying, you know, bonds or trading equities, I'm gonna be a trader. You know, at at the time, we were trading through Raymond James and the, you know, broker that we were working with. He brought me to his trading floor in, you know, Manhattan. It was, you know, tallest building in the city, you know, top floor, nicest thing. And we get to the top, you know, floor, and he's gonna show off his trading desk. And it was a hundred Bloomberg terminals with, you know, two guys in sneakers. And he was shocked just like I was. You know, technology had completely wiped out that trading desk. They they've been replaced so fast. They couldn't even churn the Bloomberg contracts. Mhmm. And, you know, this is young kid, six months out of college. You know, here's I think this is my career, and, you know, immediately hit me in the face. Like, if you don't learn technology, learn to embrace it, like, you you're gonna have some challenges. And so Yeah. You know, that that was the day that I, uh, logged on to YouTube and said, let's start learning to code. And I I basically taught myself, you know, some Python and, you know, the the career kinda took off from there. So Okay. Wow. And so and then from that that fund, did you go any I mean, what was kind of the next steps, or what were you doing at that fund? Yeah. So I I was, um, you know, build building, um, you know, stock portfolios. You know, I started, you know, just, you know, basic bottom up fundamental analysis that ended up going into, you know, a little bit more, like, portfolio theory. You know, this is where, you know, start started to build technology skills. And, know, I eventually started my own technology consultancy where I worked with a, you know, group of different hedge funds, even did a a project for a money manager called Wellington Management, their, you know, trillion dollar asset manager. Basically, you know, at the sort of nexus of investing in technology, and my mind was always between equity debt. You know, I just love solving problems, and I ended up meeting these professors at Harvard Business School. You know, six months out from launching a fund, and they needed someone to come in and build risk reporting and trading systems. And it felt like a great challenge, and so I started with, you know, a small project. I, you know, ended up doing a, you know, couple, I I would say, kind of cool things along the way, and they they brought me on on full time. And that that was really where I got very deep into the credit market. Worked your way into a seat. Worked worked the way into the seat. It was uh, you know, at the time, I was, you know, I was really happy as a consultant. I was starting another I I had a a small software. It was actually kinda cool. It's called stump the street. We were taking DraftKings, and we were combining with trading stocks. And so I I actually launched this product, and the CFTC shut it down because we're creating a security. And around that time, you know, I was focused on it, and the hedge fund was really trying to pull me in full time. And so that was you know, I said, alright. Let's you know, we we can fight this CFTC later. Let's go and, uh, you know, help the fund. So Tell me the maybe the origin story of Lendicar, which is now Dalit. It was, um, so maybe I'll I'll share a little bit about this hedge fund because that's, you know, what brought me into, you know, what was formerly Lendica, now Dalit. So, you know, it's very interesting fund. It was started by these two Harvard Business School professors. Their expertise was behavioral economics. And the idea was there was mispricing in the credit markets, both in the, you know, business credit markets and, you know, at first, you know, main focus is the consumer credit markets. And and we could use, you know, the this research and behavioral economics understanding that operators or consumers just aren't rational to to pick up on these mispricing and, you know, generate alpha over, you know, basic debt, you know, public debt, uh, benchmarks. Okay. And so, you know, always helpful with an example, you know, in the consumer space, you know, FICO, which is the main benchmark for credit of a consumer, it's basically looking at you know, it's a variety of things, but the the biggest, you know, the the biggest factors are how much credit you have outstanding, how much of that credit are you using, and then are are you may you know, are you paying on time, which you know, that that's basically the driver of credit, which is great, but, you know, it's also a little bit counterintuitive. You know, in order for you to get more credit, you have to have credit to begin with. And Right. What happens if you have a thin credit file if you're, for example, a first generation American or, um, you're in, like, a a credit desert, like, for example, the city of Detroit does have has very limited access to credit. You know, how do you build credit? You know, you can't get credit available if no one's gonna give it to you. And so we started to look at alternatives to FICO score, um, again, taking into account these behavioral, you know, behavioral economics. And so one one of the great ones and, you know, generated some alpha on this trade was looking at honesty. And this works in the consumer and the business space, but it sounds, you know, very intuitive to actually, you know, come up with it and model it and perform. It was, you know, very complex. But if someone is going to lie to you about something on their credit application, turns out, you know, not surprisingly, there's a much higher probability that they're gonna default on that loan. And so, okay, how do you tell if someone's dishonest? Well, you ask them free form questions, and then you verify it from what we would call truth file. So I might ask you, Derek, hey. What's your FICO score? And you say, hey. It's, you know, seven eighty. And in your case, it probably is. But if we ask your friend and they tell us seven sixty and it it's actually six fifty, even if you're an average credit and average FICO, six fifty is the average, you you would be a very bad credit bet because you would would It's like some moral filter more. Like Yeah. Yeah. In a way? It it you know, the the, um, um, the the way that, you know, we we've now personified it and, you know, we talk a little bit about this at at Lendica, but, you know, if you introduce yourself with a lie, like, what what else are you hiding? And, um, you know, Lendica, which is, you know, started as this, you know, credit and liquidity provider in the accounts receivable space. You know, we we had that same philosophy. You know, you're you're underwriting the business, but you're also underwriting the operator behind it. And if, you know, things can look good on paper, but that's where you get in trouble within the small business days. Yeah. Yeah. I mean, ultimately, the the the money that's moving that you're looking at is being
10:10decisioned by somebody. Correct. Right? And so, um, um, that character of the team or the individual making those decisions is is arguably maybe more important than just the actual quantum that's moving and and and whatnot.
10:27Not a question. I mean, we we can get into details, but, you know, there there are there's so many avenues for a small business to, you know, fraud fraud you out of a loan. And, you know, it can be a business that that is operational and maybe even growing, but the operator decides to set up a new bank account or, I guess, maybe for for my own benefit, I probably shouldn't list all the ways that we get frauded. But point being, you know, there there's, uh, uh, there there are so many ways that you can getting around paying a loan. And so you're you're really looking at character, and you're looking at data. You're looking at, um, historical trends that just don't show up on the balance sheet, and that's where Lendika really took off. It was understanding that beyond just how the business looks on a, you know, a historical financial perspective, really understanding the real time data which lives in their operational system. That that was really the big unlock to, you know, getting Dalit where it is today. So are they Lendika and Dalit same, or was it different strategy which drove the decision to rebrand? Or what was kind of the history there? Dalit started as Lendika. We we started the business in around August of, you know, twenty twenty. The business was very simple. We, you know, would meet these lower middle market up to, you know, um, some cases, you know, small enterprises. You know, we we would meet the demand of their liquidity in their accounts receivable. And so, you know, sharing things that, you know, I'm sure your entire audience knows, but even very successful businesses that are growing and are stable, they still run working capital, uh, constraints. And in fact, when they are growing, sometimes that's the biggest need for liquidity for their working capital because, you know, that two hundred million dollar top line business that is, you know, a distributor, they might keep half of that in their working capital cycle. And so fluctuations of five to ten percent or five to ten million, and, you know, that's the difference between delivering a good and and, you know, an unhappy customer. And so what was so critical for us was to understand, hey. If we're gonna, you know, provide capital against their receivables, we need to know that, one, those customers are credit quality. That's a given. We need to know that our borrower was quality. You know, high quality. That was a given. But what what's most important, what doesn't live in Dun and Bradstreet, what doesn't live on a balance sheet is the transactions between the two. And that's really what we became expert at was using, you know, very early stages of AI. I mean, again, the business is five years old, but, you know, AI is, you could argue, in production three years old. So, you know, you would probably consider it back then as machine learning, but we we became very good at understanding the trade patterns between the buyer and the seller. You know, is that order volume continuing to grow? Is that customer paying them on time? You know, is there anything in that relationship that would indicate, hey. This customer is unlikely to pay or this product is not going to continue to produce. Um, and that supply chain data was incredibly valuable. Um, you know, we we wrote hundreds of millions of dollars of loans, and default rate was under half a percent. You know, it still is. And, um, you know, a big part of that is related to just, you know, using that data that doesn't live in So you were you were plugging into ERP
13:54flows because I mean, I did I did a bunch of ABL lending when I was at Silicon Valley Bank, and I would I would characterize it as kinda like old school where, you know, you're getting, um, monthly reporting. You're doing quarterly audits. You're getting borrowing based certificates, but it's all, like, paper. It's exactly right. So you you guys are, like, plugging right in, and so it's almost like real time, not only insights into the collateral pool, but insights into the sales velocity and payment flows, which from a from a traditional bank perspective was always, like, a lag. It was like it was never real time.
14:37Exactly right. The, yeah, the the three biggest SMB lenders, small business lenders in the country, um, we call them the big ass lenders. They're now probably called the big abs lenders, but the big ass lenders are um, ASS, Amazon, Shopify, and Square. Those are, like, the three largest small business lenders. Right? And so you asked the question, you know, like, why is Amazon lending money? Or, you know, how does Square well, like, when you think about it, a a lender is, you know, very simply access to capital, which, of course, these guys have it, the lowest possible rates, you know, access to a customer, which, you know, they have hundreds of thousands or millions of customers. And then, yeah, make the argument, it's really just data. And, you know, from the kinda old school perspective, you know, that data is, you know, stale loan documents and stale financial documents. For Shopify, it's, hey. How many sales have you made over the last thirty days, and is that growing and shrinking? And is your customer review increasing or decreasing? And they they just have this incredible, you know, moat to be able to offer, actually, very expensive products, and they're right there. It it's you know, we we would call it embedded lending. And so that that's really, you know, the the the the main goal of, you know, Lendika, how we started was, you know, sitting right on top of the ERP. That data is incredibly instructive for underwriting. And, um, it it was, yeah, very valuable, at least, you know, in the early days of valuable origination channel as well. So And then from a from
16:18a capitalization, you were you had a pool of capital that you were using to to leverage or you were using to deploy and lend out to these, uh, different and these were all SMB clients.
16:33Well, these are probably more middle market. Middle market. Middle market. Yeah. Middle market. When when you start a business in the lending space, you don't snap your fingers for the most part and, you know, get a a hundred million dollar term sheet. And so we we started, you know, sort sort of a a bootstrap way, which very difficult to do. So, you know, the the story goes our our first customer, we we actually we we literally met them at a bar. And, um, it's my cofounder and I in Western Massachusetts, so we had some networking event. Went to the bar after. And, you know, these guys are sitting there, and they have this the largest whiskey glass I've ever seen in my life. I mean, this thing is like, you know, you're you're this isn't safe. And so, alright. There must be a story there. And we look closer, and they had a napkin with three hundred thousand dollars circled with Sharpie on it. We go, okay. These are the guys we need to yeah. So we're we're it's you know, it's a Tuesday night. We got let let's go talk to these guys. And it turns out they were a small business, you know, medium sized business that was growing. They were in, like, the gummy space. They're producing gummies. And, um, they needed three hundred thousand dollars or they were gonna go out of business. They needed it in, like, forty eight hours. And they were sitting there, you know, drinking, lamenting, miserating. You know? How are we gonna get this money? Everyone's rejecting them. And, you know, my cofounder and I are sitting there, and we we start digging into the business. They had some data there. And it actually turned out if they just didn't really understand their supply chain that well. Like, if they just you know, they thought they were trying to solve for a big order, but if they solve for the small order and got a checkpoint payment, that would actually unlock more than than enough capital to go and get the final goods that could deliver the next checkpoint. So it wasn't a financial issue. It was actually just like a, you know, a deep understanding of the supply chain. And it turns out they only need thirty grand, and my cofounder and I, you know, we we happen to have it in our four zero one k, and so that became our first loan. Now we we sobered up before we wrote the loan. We said, talk to us in the morning. But, um, we we saved that business. You know, today, they're they're producing the gummies for you know, they're they're selling it to CBS. They're producing the gummies for, uh, Deuce Tatum, who the son of Jason Tatum's, uh, you know, these so, you know, it's, um, it it was it was actually, like, that was the point very early on in our career at Dalit that it it made us realize, you know, there's this basic financial understanding, this you even called business understanding that, you know, is sometimes lost, especially in, like, the lower middle market small business. Like, we kinda take for granted sometimes that we have it. But it it it, you know, sort of opened our eyes and and began this passion for, you know, some of these, you know, complexities in the business and, you know, our our focus right now is in, you know, the collection side. But, you know, the the there are things that, like, humans can do and, like, manual work that needs to be done, but there's a lot of stuff that, like, technology can really just help with. And, you know, we we believe, like, the future of AI moves people from, like, operators to architects, and that that was kinda, like, the first stage where, you know, hey. These guys are dealing with these, like, very, you know, number like, let let's focus on the strategy. Right? Like, we need to go and grow the business. Let's not really worry about some of these this minutiae. Let's let technology do it. And that that's really where our focus has been is.
20:01I've I for me, not to get too off of a sidetrack, I feel like AI, even in my own, and I was tweeting about this the other day where I think it's gonna unleash a lot of creativity and allow people to pursue things where maybe they were afraid because there were some administrative operating things that they just didn't know about. And it's like, you don't you no longer need to be the expert supply chain manager, although that's important for you to understand. There's like to to your point, I think what this is kinda what you're saying is, like, there's smarter AI can can enable that because it's a it's just gonna be a smarter tool. And you can you can go and pursue your gummy business and make the clients and close the sales, but you don't like, there are solutions now where you can get the optimal supply chain without hiring, you know, thousands of people or whatever. I mean, I think you need it, but I'm just saying, like, the technology component of it now is becoming less of a barrier for people. And I think it's gonna unleash a lot of creativity. I think it's gonna create a lot of SMB company formation, hopefully. Um,
21:15almost like Yeah. I mean, look at Stripe's latest data. It's like, you know, I think something like twenty percent growth last year in new SMB formation, solopreneur.
21:28Yeah. Because, I mean, I'm I'm experiencing that for myself where it's just like I wanted to go do something and then it's like you completely kinda can get bogged down with, but I don't know how to do that, and I don't know how to do that, and I need this, and I need that. And now it's like, well, wait a minute. Like, I can do these other things now more efficiently or I can, you know, at least get a concept built for somebody to to look at. So, yeah, I mean, it's exciting. So you so that was I mean, that's a good that's a good, uh, origin story for for the business. Now and then so so how how do you scale that? Right? How do you go from that one client first check to attracting more capital to feed into the machine that you're now creating?
22:19I think that this is probably in, you know, half the one zero one books out there, but it's just so important that I I always any anytime I I speak in front of a class or or, you know, I'm speaking, but and it's so simple, but it's so important. Just, like, do an incredible job for the limited customers that you have so that they are your sales p your your mouthpiece, your your sales staff for the the next group. It's like That's a good advice. You know, it's just, you know, it's it's one on one advice, but it's so simple and it's so true. You know, with with our lending product, you know, you give someone a fair rate and you give them really great service and great technology, so you're not taking a month to apply for a loan just, you know, couple of you know, in some cases, same day. They're gonna be happy, and they have friends, and those friends have friends. And that's really you you talk about, like, network effects and virality. Like, in the lending business, it it's it's difficult. You know? You either not gonna add your friends on Facebook, you know, the same way you you would, you know, add your, you know, friends on a lending product, but just doing a great job with those customers. And so, you know, the the growth was, you know, like any lending business, you you really don't want this exponential growth, like, that usually runs into to issues. So we were we were healthy, responsible, but somewhere like, uh, you know, three, four x year over year for the first few years. And, you know, the book started to grow, and we we raised, you know, we raised some money and then raised again.
23:56When you say raise money, is it a combination of some equity and, like, a warehouse facility or, like, a a
24:02back leverage? Or what's kind of the cap what's kind of the capture? Well, you know, this this is like you know, if you're out there trying to, you know, get into especially the origination side of credit, you know, raising capital is extremely, um, not just competitive, but complicated because, you know, the you know, your your basic, you know, company structure is, you know, a a corp and and a funding entity that sits junior, and oftentimes, there's several different funding entities for different And so, um, you're not just raising money at the corp level. You're raising it at the SPV level. And then there's typically, like, mezzanine debt that has to sit somewhere in between. And so, like, when you're running a fundraiser, you're usually running two or three fundraises simultaneously, and the groups that care about the debt capital are far less concerned. In fact, sometimes there's, like, an antithesis because, you know, hey. We're gonna grow at ten x, and the thing is gonna be great. And that's what the corp wants to hear, and the SPV wants to hear almost the exact opposite. You know? Hey. We're gonna be very we're good stewards of the capital, and we're only gonna apply. So it's a very complex and, you know, at the end of the day, everyone's incented that you wanna see growth, but, you know, you you also have to be very careful about, you know, the fund the funds you put on the street. That was one of the one of the things about, um, working for a bank. It's like
25:27it's like this paradox of, like, where you wanna loan you wanna grow the loan book, but you don't want it to grow Correct. Too fast. Because that's just gonna be like, oh, well, it's growing because they're lowering their standards or they're you know? It and so it's it's like this interesting dynamic. It's a very, very interesting dynamic. And it was it was always in the back of our head. Hey. You know, like, there's huge opportunity
25:51to layer I mean, you look at some of the biggest fintechs out there, Ramp, Brex, Navin, um, you know, all of these banks, and their financial services wrapped, uh, with software. And so, you know, our our, you know, lending product was a a tech enabled lender underwriting, you know, using technology. You know, customers are funding invoices all without any sort of human interaction, no email. It's all on a platform. Really great platform, but, um, you know, the platform was free. And so in the back of our mind, you know, we always knew that there's an opportunity for a, um, you know, to to sell our customers software or something that, you know, we think it would really benefit. But, you know, really, it was about eighteen months ago, almost like a flippant comment. That's like another just one on one lesson I always, you know, share with my students is, you you always have to be listening, um, because you never know when you know, listening to the customer, you never know when something's gonna spark. And about eighteen months ago, you know, know, a customer on the tail end of a call, you know, said something to the effect of, you know, I love your lending platform. It's so great. But if only my customers would just pay me on time, I wouldn't have to use you guys. And they were saying it jokingly, of course. But, um, there there was some truth there, and it just sparked something with Jerry and I, my cofounder. It's, you know, why why aren't these businesses paying us on time? Right? Why why is there a delay in the supply chain? You you think, like, on paper, it's so simple. I'm giving you thirty days. You have thirty days to pay for it. Just send me the money. Yeah. Set set it and forget it. You'd think it'd be, you know, easier to do that. And yet, you know, forty five percent of every b two b invoice is paid late. And it's, you know, the charge off on, um, you know, consumer invoices, um, you know, in in certain industries is is something like twenty percent, you know, certain medical. And, um, um, and, um, you know, pool pool services is another one, you know, like, fifteen, twenty percent. So, you know, like, something is clearly, you know, wrong. Um, you look at the market of these legacy softwares. They're great at organizing accounts receivable. But, ultimately, the onus to, you know, we say ingest, tag, and execute, right, to read why these things are missing, to figure out, you know, the taxonomy behind it and then go execute it. All of this is done by humans, um, and they're way overworked and they're way understaffed. And the outcome, it it just it isn't good. And so that was the light bulb to say, hey. I think that we we we think there's something here to, you know, build the next stage of the business.
28:34Interesting. K. So so go into that. So what what what did you build? So, um, let let's start maybe again with the problem. So
28:42at at, you know, the the I think this is, like, one one of the most, like, interesting, um, you know, findings from our research. So, you know, what what is collections within a business? What is accounts receivable? You know, basically, you know, in certain industries, you know, industrials, manufacturing, you know, even technology, health care, services. Businesses will sell on credit, and so they're, you know, delivering the goods. They're not expecting payment for thirty to ninety. You know, we've even seen, like, three hundred sixty four days in one case. AutoZone is three sixty four. It's how they get in the zone. And so, um, you staff your team with, uh, people, you know, sometimes onshore, sometimes offshore, uh, to make sure that those net terms are met. And, uh, you know, when you take a look at the amount of staff that are actually staffed, the the amount of people that are actually staffed to do that role, and you compare that number, it's something around, um, you know, let's call it, you know, a hundred fifty k, something to that extent. And you compare that number to the amount of work that's actually needed, you know, to just manage the forty five percent of invoices that are late. Um, there's something like a forty something like a forty percent close to a hundred thousand dollar labor shortage in accounts receivable, which is to say that for every one person in accounts receivable, there's something close to one and a half people's amount of work that needs to be done. And so, you know, there there's no wonder why these businesses are constantly struggling with working capital. They're constantly chasing invoices. There's just not enough people to do the job, and it's a cost center, and businesses just don't wanna put more money into it. So it it's a it's a major problem, and the answer to that problem is not more people. It it's certainly technology. Now the challenge is, um, you know, when it comes to actually chasing invoices, you know, the the the standard dunning tools, sending them the letters, sending them automated messages, that doesn't actually solve the problem. Um, we say they end up in the mental spam box. It's kinda like if I kept, you know, asking you, uh, for payment and, you know, you have the money, but you're not paying me because, um, I have the number wrong or because I didn't actually deliver the service or because, um, the service that I provided was was incorrect. Um, those dunning letters are just gonna fall in deaf ears. They're they're probably actually gonna make you even more upset and less likely to pay. And so that's, you know, the the the that's sort of, like, the, um, you know, the the big overarching issue is there's not enough people to handle this problem, and the existing technology is just missing the mark. And so what what Daylet does is connect to your context links, your invoicing tools, your communication tools so we constantly know which invoices are not paid on time and why they're not paid on time. We know the story behind every invoice. And when we know why it's not paid on time, we know that there's a dispute or they've promised to pay and they haven't followed up or they've short paid because of, you know, some sort of issue. Now when we're reaching out, we're reaching out as a problem solver. We're reaching out, um, to address the issue and solution it as opposed to just ask for payment. And it makes a massive difference in terms of collection rate.
32:12And are you also I mean, are you providing financing on those receivables or or not? Or is that the reason why you're also, like, taking more of a interest, I guess, for lack of a better word? I mean, it's good for the client. Right? Ultimately, what you're trying to do is improve their collection rates. But you're if you're providing leverage on them, then it's also good for you too. Right? You know, so, like, the two products can be sold separately. So,
32:42like, today, the primary focus of the business is actually selling the, um, AI collections, uh, tool. And, um, it it's just a value add to their business. They're gonna shrink their DSO. They're, uh, going to, you know, in some cases, reduce the amount of staff that is required to collect and, you know, repurpose them, or it's gonna catch them up to zero. Now the financing product sits really nicely. You know, there are times where we wanna just sell invoices. There are times when you wanna offer payment plan to your customer. It's embedded into the system. So it's a great even if you're an enterprise and you say, hey. I can borrow from Wells Fargo or JPMorgan at six percent, your customers can't, and they still offer you know, ask for payment plans or ask for extended payment, and it's built directly into the system. Um, so that's the marriage of the the two. But It's one of the things, like,
33:34as I was, like, preparing for this and and thinking about the way you know, I was looking at some of the stuff that you've written in the past. And, um, because you and I are so into finance and I did a lot of ABL lending and certainly you're running this business, it's often you have to zoom out and be like, companies are lending money to their customers. Right. And and it's like, do they see that as a sale, or do they see that as a loan? Like, do they it's like sometimes I'm wondering. It's like like no. They probably don't look at it as like a loan, or maybe they do or may maybe some get it and some don't. I'd be curious to get, like, the breakdown of, like, of all the SMB business owners out there. How many how many associate that thing with a sale and revenue or a loan? And it's like, are you lending at market rate or a submarket rate? Are you are you really getting a credit profile? Do you really understand how much risk you're taking? Um, my guess is it's probably a lot of them look at it as a sale and revenue, but maybe not as a loan. Or they're not thinking I'm just curious because
34:42it's something that we overlook probably because it's kinda like, we'll get the door. Yeah. And and but until it's cash, um, you know, it's, uh, it's still in, you know, it's still in the accounts receivable. So, yes, a a friend of mine, um, I'll call him an acquaintance, he runs a publicly traded credit monitoring platform. You know, he he's in this business for a very, very long time, and he's the first to tell you that the credit report is not a risk tool. It's a compliance tool. So what does that mean? Well, if I'm selling to you, Derek, and I run your name the credit bureau, Dun and Bradstreet, Experian, whatever, I'm not doing it to make a, uh, determination on the, you know, available balance. I'm doing that as a compliance measure to say, hey. We at least did the homework to check because, guess, when push comes to shove, who gets to make the decision if we extend that credit or not? It's it's not the credit department. It's the sales and the chief revenue officer. And so, you know, there there's a a a constant sort of battle and a, you know, a a push and pull between credit and sales, but, ultimately, these businesses have to drive revenue, and that's what they're comped on. You know, all of all of the crap gets thrown onto the collections plate. And this is actually really interesting. You know, we we're, you know, big, um, you know, sponsors and, you know, very close with NACM, the National Association of Credit Management. And you were presenting at their conference a couple weeks ago, and there was there's actually this session about one of the biggest challenges in the in the credit and collection space, and it's about the age of the employees. It's about the age of the staff. And over the last, you know, several years, the the average age of a credit collections professional has grown from about, like, thirty eight to more than forty five years old. It's, you know, almost twenty percent increase. And what does that mean? Well, like, young people just aren't you know, they're they're not joining this profession. You know, people are aging out and retiring out, and it actually leaves this, like, really massive gap that, again, does doesn't get solved on its own. You know, the sales team are gonna continue to push. You know, business wants to drive more revenue. And if no one is there to to clean things up, you know, supply chain suffer. And so It's gonna have an issue on capital. Right? You're gonna need to have more working capital to Yeah. Exactly. So, um, yeah, we we think that, you know, there there are certain, you know, go going back to our discussion just on operators to architects, there are certain jobs that as technology comes in, you know, peep people just don't wanna do, and and we we should, you know, we should reward that. We should celebrate that. This is, you know, this is an improvement of civilization, and, you know, I I have a lot of I give a lot of credits to the collections group out there. I I our first loans, you know, even to stay on the big ones, like, you're looking at the credit depart or the collections department for the big ones. Nothing is harder than trying to convince someone to pay you, especially when they're a fraud or they're in, you know, severe financial constraint. But when technology can at least make our lives easier in this regard and it's a job that, you know, people are know, clearly raising their hand saying we don't want, it it makes for a great opportunity to, you know, innovate.
38:14Now did I read somewhere that are you also doing solutioning on the payable side as well? Like, do you have
38:21products for for that side of the kind of the ledger? Or Our, um, our focus is, you know, entirely receivables that we do have a couple legacy customers who have used our pay later product, but it's really when you think about Abel's, it's at the point of sale or at the point of payment, do I wanna delay the payment on this invoice? And so we'll allow customers to, you know, set up a payment plan with Daylet or even, you know, offer terms that Daylet runs. So it's really you know, that that's where, like, the liquidity comes in at that point of, um
38:57Got it. Almost like microloans.
38:59On a per invoice basis. Yeah.
39:02Yep. Yep. Okay. So kind of going back to what we were just talking about, yeah, I've been hearing, you know, headless CRM and and headless different functions in in organizations. I mean, do you do you envision a a world where agents are on both sides of the transaction where you've got maybe your team of agents, they let agents that people can get access to that are running their, you know, collections, receivables, invoicing function, and the data that gets generated is not what people historically have seen, but it's like markdown files or some other file exchange the agents are reading on both sides, customers and and your customers and their, you know, their their customers. This is the future that we're building towards. That's exactly right. And so here's what is, you know, happening,
39:55I think, a lot faster than the market is pricing. Try to build the arc here, but we talked about this on, you know, kind of our initial chat. But the the amount of money that was poured in by VC to AP platforms relative to AR platforms is something like twenty five to one. And, you know, Ramp, Rex, Maven, you name it, these companies are now spending majority of you know, they they continue to raise money. Majority of this, you know, r and d expense on building and providing agents to manage your accounts payable process. Now what does that mean? Well, every time I submit an invoice to a buyer, I'm no longer having a human kinda do a cursory look. I'm having a swarm of agents that will look for any possible way to reject the payment of that invoice and kick it back to the AR department. Right? That's their incentive Okay. Is as an AP is to manage and procure and ensure that only only the perfect invoice are getting through because we have no incentive to pay them early. So what is that gonna mean for the AR department? It's not gonna create less work for them. It is arguably gonna explode the surface area of responses. An invoice that a human would, oh, I know Derek. He he messed that up. He's good. Well, the agent has no incentive or or even right to do that. And so the It's it's very black and white. Right? It's zeros and ones. It's it is or it isn't, or maybe there's some And even if these, you know, models are sophisticated enough to sort of understand, they don't have the incentive to to to let things go the same way that you might. And so we we, you know, envision or or we anticipate an explosion on on the surface area of responses from the accounts receivable department. And we we think the the only logical future is to have agents that are, you know, battling on your behalf to, you know, ensure payment, and it it's very logical to think that those agents will go back and forth. The other thing that I I think is really interesting in, you know, where, you know, we we believe that we're building towards, you know, how often does this happen in the supply chain if you, you know, customers that are, you know, listeners that are are on the credit side of their business. You know, you hear from your your buyer that says, look. I'll pay you in ten days. I'm just waiting for something for my client. Well, what does that mean as, you know, the the seller? Right? You're not getting paid for ten days, and, usually, that means that you're gonna have to correspond with your suppliers and let them know. Right? Some blow up, you know, in the Middle East is having this huge downstream effect. Well, that's a lot of communication to keep track of from an organization, and it's a a constantly updating communication. Right? You know, as the supply chain changes, you gotta go down. And so, you know, we we imagine a world where, like, these agents are communicating these updates in real time, negotiating for days, negotiating for early payment. Hey. You know, I need ten days over here if my buyer can you know, a different buyer can give me ten days. I'm willing to give them one percent discount because my discount to the supplier is dependent on it. It gets very complicated, but these agents have the, you know, the the the context to soon be able to do that. So that's the future. Because in in
43:17yeah. In in in theory, you could you could you could you could you could have, say, someone's delayed in paying you. But then if you can if you can match the delay in the quantum with one of your own vendors, then it's like, okay. Get neutral. But then there's there's that orchestration, but then there's also differentials in quantum and discounts and stuff like that. So there's a lot to kinda, like if you wanna optimize that, that's a lot of brain
43:47brain brain.
43:48And and not only at that, but how do you scale that? With with under resourced teams. And so what you're kinda describing is, like, a way better way to solve that problem. Imagine if Jane Street hopped into
44:01the CFO desk of a mid market distributor. Seriously. Right? Imagine that. Like, they they throw some compute at that. If if Jane Street was operating the supply chain of a mid market distributor, that's exactly what they would do. They'd be constantly trying to generate alpha on every single transaction. And, um, you know, like, this is a, um, just so I I just came back from my honeymoon. I I was married last week. Oh, congrats. And thank you. And we were in, um, Milan for, you know, a night on the way, and we're looking at the Duomo of Milan. So the this was an analogy that my wife told me to, you know, stop talking about because she wanted to enjoy her honeymoon. But, you know, the the Duomo took about six hundred years to build. It has the most statues out of any building in all of Europe, maybe the world, three thousand statues. No one would ever conceive architecting a building like that today because we just wanna move quickly. But if AI is taking care of all of the the baseline architecture, right, taking care of, like, the, you know, ninety percent of what is needed, think about the time that we have as architects to build another Duomo. Maybe it doesn't take six hundred years. Maybe it takes sixty years or six years. And so this is, you know, this sort of fantastical future of putting Jane Street in the suite of the CFO for mid market distributor, it almost feels Duomo esque. Right? Like, why even do that? But when AI can take care of the normal stuff, then, like, we now have all this extra capacity for excellence and,
45:45um, It seems attainable at a price point that's makes makes it make sense. Right? Like Correct. Looking at looking at that, um, functional process that happens in every business.
45:58Yes.
46:01Giving people the tools to be to be able to optimize without headcount, without elaborate systems, without, um, and and make it work. You know, candidly, I think there's a lot of ideas out there, and people will implement them, and they're like, this is not working. Right. The ROI is not there. No. Now that's interesting. Yeah. Jane Street in the SMB finance department.
46:28And guess what? I can promise you everyone in the finance department that's listening would much rather be doing cool Jane Street stuff than trying to close the books. Okay? I can almost assure that. And, you know, I think that this is the excitement. This is, you know, what what, uh, get gets us really motivated is, you know, that future is not that far off if we sort of focus on it. Well, I think it's that's a good that's a good place to end. But before we before we go here, is there any,
46:57um, where can people learn more about Daylit? And are you are you growing? Are you hiring? What's kind of the the, um, state of the state of affairs for for for the business right now?
47:08Businesses, you know, very happy to say growing, continuing to, uh, you know, onboard new customers, new partners. You know, one thing that we found, which, you know, is really interesting, so listeners, um, in the private equity space with friends in private equity, we found that, you know, large and small private equity firms can really generate significant value for their customers by implementing DayLit. You know, working capital focus is, like, so critical for private equity, and this is, like, the, you know, bet best ROI, um, to improve working capital in your port goes. So that's a new initiative that we're launching with private equity, and you you can always, you know, reach out online or, um, connect with me on LinkedIn. Um, very reachable. Sounds good. Alright, Jared.
47:51Really appreciate your time today. It's been a great discussion and look forward to following you and continuing to, um, see what you guys are building because this is a really exciting space. And now with with AI, um, capabilities that we all have access to, um, which are only gonna get, I think, better, um, this is an exciting time.
48:10Oh, totally. No. This is a great discussion. Thanks for having me on the on the pod.
48:17Thanks for tuning in to the Credit Bubble. You enjoy today's conversation. Please follow the show and leave a review. You can connect with me on LinkedIn, x, or through my website at thecreditbubble dot com. You'll find all the links and additional resources in the episode notes. Until next time. Thanks for listening.
