DualEntry's Co-founder Santiago Nestares shares why Mid-Market CFO are ditching Legacy ERPs
In this episode of Future Finance, hosts Paul Barnhurst and Glenn Hopper talk with Santiago Nestares, co-founder and CEO of DualEntry, about the reinvention of ERP systems through AI-native design. After scaling a global e-commerce business to over $100 million in revenue, Santiago experienced the frustrations of legacy finance systems firsthand. That pain sparked the vision for DualEntry: an ERP platform built from the ground up for the AI era.
Santiago Nestares is the CEO and co-founder of DualEntry, an AI-native ERP platform. Before DualEntry, he co-founded Benitago, a leading Amazon brand aggregator. His experience running a multi-entity, high-growth business exposed the inefficiencies in traditional finance systems, inspiring him to build a next-gen ERP solution from scratch.
In this episode, you will discover:
Why legacy ERP systems are fundamentally broken, and how AI changes the game
How DualEntry enables 24-hour ERP data migration
The role of AI in journal entries, reporting, and reconciliation
How auditors are embracing automation and AI for compliance and accuracy
Why ERP systems must evolve beyond just being "AI-enabled" to truly AI-native
Santiago Nestares joins Paul and Glenn to reveal how DualEntry is transforming ERP for the AI era. From eliminating painful migrations to building systems finance teams actually enjoy using, Santiago makes it clear: the future of ERP is AI-native, fast, and built with users in mind. ERP doesn’t have to be a burden. With the right tools, it can become a strategic advantage.
Join hosts Glenn and Paul as they unravel the complexities of AI in finance:
Follow Santiago:
LinkedIn: https://www.linkedin.com/in/santiago-nestares/
Website: https://www.dualentry.com
Follow Glenn:
LinkedIn: https://www.linkedin.com/in/gbhopperiii
Follow Paul:
LinkedIn - https://www.linkedin.com/in/thefpandaguy
Follow QFlow.AI:
Website - https://bit.ly/4i1Ekjg
Future Finance is sponsored by QFlow.ai, the strategic finance platform solving the toughest part of planning and analysis: B2B revenue. Align sales, marketing, and finance, speed up decision-making, and lock in accountability with QFlow.ai.
Stay tuned for a deeper understanding of how AI is shaping the future of finance and what it means for businesses and individuals alike.
In Today’s Episode:
[01:36] - Lessons from Scaling to $100M & ERP Frustrations
[03:37] - Why ERP Migrations Are So Broken
[07:47] - How DualEntry Raised a $90M Series A
[10:51] - AI-Native vs. AI-Enabled ERP Systems
[18:23] - Challenges and Realities of 24-Hour ERP Migration
[23:26] - Why Building a Mid-Market ERP Is a Bold Move
[28:18] - Using AI in Audits & Real-Time Reporting
[31:45] - Rapid-Fire AI Questions
[35:52] - Final Reflections & Wrap-Up
Full Show Transcript:
[00:00:42] Host 2: Glenn Hopper : Welcome to future finance. I'm Glenn Hopper, along with my co-host Paul Barnhurst. Today we're joined by Santiago Nestares co-founder and CEO of DualEntry, an AI native ERP platform that's reimagining how finance teams manage accounting, reconciliation and reporting Porting before launching DualEntry. Santiago co-founded Benito. An Amazon brand aggregator that scaled to more than $100 million in revenue. That experience, running a global multi-entity operation exposed the bottlenecks and inefficiencies in traditional finance systems and ultimately inspired the creation of DualEntry. The company recently raised $90 million in series A funding from Lightspeed Venture Partners, Khosla Ventures and GV to build what it calls the first ERP designed for the AI era. Santiago, welcome to Future Finance.
[00:01:33] Guest: Santiago Nestares: Thank you for having me.
[00:01:34] Host 2: Glenn Hopper : So you guys have been in the news lately. That's a big funding round. And um, before we dive into that, though, uh, you built and scaled to over 100 million in revenue before this. So I'm wondering, were there decisions from that journey that kind of shaped how you think about building this AI native ERP? And what did you learn about finance ops at scale that you're now productizing?
[00:01:56] Guest: Santiago Nestares: Yeah. I mean, thank you. And yes, we did finally come out of stealth and were very happy to tell the world what we built. But a lot of what we built comes from those days. You're not born into the world and, you know, get to experience what a mid-market ERP is. Uh, that is something that only those fortunate enough to be in a medium or large company get to interact with. But almost every one of those interactions, if you ask around, are quite negative. And, um, our last company was our first window into that, into, you know, we thought at least we went into it thinking like, as the company gets larger and we have economies of scale, we'll be able to afford better software and these softwares are going to be more tailored, you know, smoother. It's like, you know, it's like if you pay for a better car, in theory, the car should be better. But it was to our surprise that it's actually quite the opposite. You know, consumer grade software feels great. But then you get to these ERPs in the mid-market, and they feel like they were built 25 years ago, and nobody's done anything to them.
[00:02:50] Guest: Santiago Nestares: The tailoring or the fact that they're custom made is actually more like a euphemism for you need consultants to do everything and you are not dependent on them, almost like tech debt, but with consultants. And that experience was so horrible that I said, there's no reason why this has to be like this nowadays. You know, like where you mid-market ERP are uniquely in the position where almost every customer comes from two places, they're either coming from QuickBooks or zero. Why is that transition not super smooth? Even today, if you buy a mac or if you buy a windows, there's a migration tool that helps you go from one to the other. Why does this not exist for ERP? And so we said we're going to build something in the 21st century that is AI. First, that takes into consideration how the world has changed and feels like a consumer grade software. But we're also going to make that transition seamless, which is where half the pain is today.
[00:03:37] Host 2: Glenn Hopper : Yeah, I've actually, uh, been involved with a QuickBooks to NetSuite migration and the data, it's amazing. And there are some consultants out there who specialize in that and have built their whole careers on it. And it's crazy to think that something that specific data that is structured and in one format that it's not already, just instantly mapped over. I was really kind of shocked at the amount of work that went into moving simple GL records over from one system to another.
[00:04:06] Guest: Santiago Nestares: And the interesting thing is that you're going from a simpler system, so it's not like you have to really think about every edge case. You just have to think about the edge cases from a simpler system to a more capable system. But, you know, it doesn't take a lot of research. And this we figured out over time that it all comes from an incorrect design from the very beginning. Back in the day, the way you bought software was not by going on the App Store. You know, even the way you bought hardware was through consulting people. They would come in, they would set it up in your network rack. So naturally, the way that these legacy ERP softwares came about selling their software, distributing was through these like partners. It was, you know, this distributor model and you would buy the CDs from them. They would come and install it for you and configure it and manage the upgrade. So of course, you want to create an ecosystem in which the partner also wins big by selling your software. The thing is, now that's taken us and steered off the market And it's such a such a bad direction where these big ERPs. Like, even if they woke up one day and they had all these modern engineers and they said, we're really going to now revamp it and change the way we do things is, well, they're going to kill half their distribution. We even spoke one of the CEOs of one of these legacy ERPs before starting DualEntry, and he bragged to us about how one of the years they grew 20% year over year by making the software harder to implement, because then that would incentivize the partners to pitch their software over the competitor, because then the partners will make more billable hours.
[00:05:29] Guest: Santiago Nestares: So it was almost a competition of who can make the crappiest software. And I was like, they can only take you for so long. Eventually, like, the customers will realize that they're being played. So the pain is there. It's not clear in hindsight, but nobody's there to say we're going to build something that's easy to implement. We're not going to encourage partners to keep you in this billing cycle where it's almost like ransomware. And we're going to get you up to live in DualEntry, uh, in instances, even in 24 hours, you know, live. The data is the data is live. We still work with you for a few weeks to make sure that you're, you know, all the workflows are comfortable in the training, but at least the data, which is where the most amount of risk lies can happen in 24 hours. By the way, doing the data migration manually is one of the riskiest processes because you have to freeze it first. Download the information, but the company still has to keep running. It's not like the company where someone doesn't do the books. So while you're not uploading this and you have to upload it in the right sequence, right? You can't upload payments before the invoices. And so you have to do all these uploads in sequences the company still runs. Somebody has to close the books. Well now then you have to go back and bring over the stub of data that was left over from the moment that you froze that initial data. And then maybe you don't have all the vendors in the new system and vice versa. So all these issues happen because this is a manual process that shouldn't even be manual in the first place anyway.
[00:06:47] Host 2: Glenn Hopper : Yeah, and not just NetSuite implementations, but I've been involved with several implementations and it's always maddening. And the you want to make employees really mad. You tell them they have to do all of their entries in two systems as you're going through that stub data period. And it's, you know, it's sort of a testing period and all that, but it just kind of becomes part of the process and everybody just understands. Yeah, we're going to be logging everything in two different systems during this transition period or whatever, and it just adds complexity and more opportunity to mess up and have and have records that don't match across the systems. And yeah, it's it's it can be a mess.
[00:07:22] Guest: Santiago Nestares: And then if you can't reconcile in time, then you got to take a step back and you go back to the old system, catch up and then try again in a few months. And it's like such an avoidable problem. You might think.
[00:07:32] Host 2: Glenn Hopper : Migrations.
[00:07:33] Host 1: Paul Barnhurst: Are painful. Data can be painful. It's definitely something we want to simplify. So, you know, kind of speaking, we talked a little bit about learning and migration. Obviously you guys just announced I believe it was the beginning of October. I know it was sometime in October, you announced your 90 million series A, which was led by Lightspeed and others. What milestones did you guys have to hit to earn that level of conviction? Write a 90 million series A is a big number, so maybe talk talk a little bit first of how that process went with the VCs.
[00:08:08] Guest: Santiago Nestares: Yeah. So we actually raised a seed round while we were in stealth. We probably even saw 80% of the funds left from that seed round. And the first thing we did was like we said, we're not going to go and think about what the next one's going to be. We're just going to put our heads down and build. What is the best software that we would have loved as customers of a mid-market ERP? And while we were in that process, you know, kind of six months in, we skipped all the design partners like typical in VC. They explained to you, you have to go to design partners and spend time with them, and then they'll kind of dictate the future of the product. We said we will be the first design partner for this because we would have been customers, and that just increased the speed of development by orders of magnitude. And then, you know, six months in, insecurities start to kick in and people are like, you know, are you actually building something that people want? Or is it just like you're, you know, building in isolation. So we said, let's go out and try to get our first customer. Let's actually pick a publicly traded customer because they'll have the highest standards.
[00:09:03] Guest: Santiago Nestares: But it was most important because they'll say no to us and explain to us very clearly why we weren't good enough. And then that'll give us a build list to then go and keep building features. And to our surprise, we went to this customer and we might have a few customers before then, but this is like the first big one. Uh, and we won the process and a few months process, but we won it and, and they started running a DualEntry entirely. It wasn't like a division, just the phone company. And then we were like, Holy shit. Well, we built this actually pretty good. And they were happy they were closing the books much faster than we were doing before. Um, they were able to go live within like a week or two. And that was before we had the migration engine entirely developed. And, you know, that was like a big accolade. And then we said, okay, let's go out and hire a team of accountants to go and sell this. Um, most of our sales team today is CPAs or retired accountants. Um, and we started getting customers left and right, and the customers were onboarding and being very happy. And somehow, to answer your question, the venture community found out about this and they're like, there seems to be the new wave of AI ERPs, and there seems to be this one that's been quiet and just building in the background.
[00:10:08] Guest: Santiago Nestares: And lucky enough, we got so many offers during it was all, you know, initially preempted. It wasn't that we went out with the intention to to raise up around, uh, because we still had 80, 70 or 80% of the funds left. And, uh, and then we just got to select what we thought were the world's best partners for this. You know, we got to pick three. Normally you pick one. And we were lucky enough to be able to pick all three and go take on this big market. Uh, but it's, you know, back to business as usual. Like, thanks for the money. We'll still spend it mostly on a product, which is what we think is the leading indicator for everything. And, yeah, we'll make more customers happy. And up until now, NetSuite had no alternative. It was that or sage. And it was like you're picking between two bad apples. And for the first time ever, they can look at what a modern consumer grade ERP looks like.
[00:10:51] Host 2: Glenn Hopper : Yeah, it's so interesting out there right now. And Paul and I have had, um, a lot of the other companies, including NetSuite on the show, just to talk about, uh, you know, NetSuite just had Suite World and they've got their NetSuite next and the promises of what they're going to offer. But we've had campfires, relate and others on the show too. And it's interesting. It's interesting but not unexpected. There is so much noise right now. And not just noise. I mean by my account. I think something obviously you guys have a big chunk of this, but close to half $1 billion raised in the ERP system in the last, like what, 12 to 18 months? An interesting thing in the challenge that you have to be up against is it's a paradigm shift. People have kind of the systems that they know and the world that they live in, and they have to shift their thinking around that. But I'm wondering about CFOs or hearing all this and they say, you know, I've got this incumbent system, but I don't love it, but I'm this is what we use and it's stable. And if it's stable, it's not always but trying to break into that market. And I'm wondering if you're talking to a CFO who's hearing all this marketing noise around ERPs, and they don't really understand the difference. What's the simplest test to tell an AI native ERP from a legacy suite that, you know, added later, added AI later, or is talking about adding AI? And I'm wondering about that, like, where do you see the first real ROI showing up for finance teams going with these systems in the next, you know, 12 months or whatever?
[00:12:21] Guest: Santiago Nestares: Yeah, I mean, look, anyone who's living in a legacy ERP knows the kind of BS around those AI features that are getting sprinkled on top. It's almost like some, you know, ex senior VP or super senior VP is asking some senior VP to ask some VP to get some like manager to get some AI feature. Like it's like they don't even care if it actually works. They'll just get to brag to their, you know, you know, in their public earnings calls that they did the AI thing. Think. I wonder if some of these people even even like, use ChatGPT daily, but, um, look, the most important thing when we go about building software is because we get to rethink everything from the very beginning. Is it okay, does this transaction need to be touched by a human? And if not, does it come from an integration? Can it be done via rule based deterministic engine? That way you have the certainty that will always be treated in the same way. And then if not, then how can I suggest to the human rather than the human having to create it from scratch and the human still ultimately like there's still a in other words, like the integration was still either created by human ultimately, or the rules was also ultimately created by human. The human also has to approve the AI transaction. So there's always a traceability. But you don't have to do all the work of creating this journal from scratch. I look back at our prior company, and there was a time where even moved some of the M&A team moved into finance, and we had like almost almost 12 people at the peak running, running the finance, trying to catch up with the ERP.
[00:13:50] Guest: Santiago Nestares: We had 5 or 6 people that were doing repetitive tasks every single month, whether it was categorizing transactions from our credit cards or or categorizing invoices and bills and knowing, knowing who had to approve what. And then at one point, I saw someone taking a journal entry from the past, dropping it in ChatGPT early versions and then saying, but this is the new allocation schedule, the new accrual. And I was like, why is this not already part of the software from the very beginning? Why does the data not become available? So it's hard to kind of point that like this is, you know, it's not a copilot. And this is what the legacy ERPs get wrong. It's like not a thing that you can easily replicate. It's how in each individual experience, like look in DualEntry, even the drop downs when you select things, the first 2 or 3 options that you get are prompted by AI. Then you get the full search. But it's more likely than not, we're measuring how many times the first option is the thing that the user was going to select. How can we be 1 or 2 steps ahead? That doesn't sound like a big deal, but when you add them all together, you know you're increasing the amount of transactions via integrations and roles. And then in AI, every little interaction we try to be one step ahead.
[00:14:55] Guest: Santiago Nestares: It just makes things easier, right? Like from the very beginning to the moment that you finished your workflow, it might have taken you like 10 or 20 times less. Even creating reports, right? Like, we know that all these legacy ERP systems take so long to create reports. In fact, you can just talk to it and have it give you a version one. And then you also see what parameters were changed. So you can easily then go and adjust all those things. All those micro interactions make your experience less. Remove the friction. I like to tell the team it's like it's consumer grade ERP. It's an oxymoron. So yeah, look, I think the CFO is the best test that we give. And this is why we spend such a big time making the bet on the next day. Migration is when we let them use the software. We say, hey, go play with it. And this is something that the legacy ERP players will never let you do. Like I remember, even when I took our original NetSuite demo, you had to tell them beforehand what you wanted to see and let them have time to prepare it. And, you know, it's almost like rehearsing it so that nothing went wrong. And then they would show it to you. And I was like, well, what if I just didn't think of every case that could have come up? Like, you know, even in the car, I would say, hey, can you click on that to see how that dropdown looks? No, we're not allowed to go off script, you know, how can that be a good experience.
[00:16:06] Guest: Santiago Nestares: And um, and with DualEntry we're, we're getting to the point now where it's almost, almost uh, on self on board to the point of a demo. We're not there yet, but we're very close where we just give you a click, the data will flow through. And with 99.9% accuracy, you can go in there like look at your data live in DualEntry to test out all the edge cases, and then decide for yourself if this is the ERP that you want. And by the way, one big thing that we've done very early on is we spend a lot of time making sure that DualEntry is not just for one vertical, that it works for multiple verticals, because when people get to the mid market, you know your business is becoming bespoke, right? Like if you're running a barbershop in one location, you're probably going to run it 99% similar to the other barbershop. But if you're now at a 70 location barbershop, you might be doing something like a shampoo line. You know? So now your inventory company or, like, you know, if you're a SaaS company, you might have a hardware line or or you might have like a big office with tons of fixed assets. So companies get bespoke as they become larger. So you want an ERP that just is not just SaaS focused. You want something that's focused on multiple verticals because it gives you the certainty that you're not going to have to then do yet another migration to another system.
[00:17:15] Host 1: Paul Barnhurst:Ever feel like you go to market teams and finance speak different languages? This misalignment is a breeding ground for failure in pairing the predictive power of forecasts and delaying decisions that drive efficient growth. It's not for lack of trying, but getting all the data in one place doesn't mean you've gotten everyone on the same page. Meet QFlow.ai, the strategic finance platform purpose-built to solve the toughest part of planning and analysis of B2B revenue. Q flow quickly integrates key data from your go-to-market stack and accounting platform, then handles all the data prep and normalization. Under the hood, it automatically assembles your go-to-market stats, makes segmented scenario planning a breeze, and closes the planning loop. Create air-tight alignment, improve decision latency, and ensure accountability across the team. No, I appreciate that. I have a question. I know you guys promote the 24 hour. You've talked a little bit about that next day migration for initial data loads kind of walk through that where that holds what the challenges are around that. And what's the realistic plan for someone to be fully up and running right? It's one thing to have your data there, but there's training. There may be rework whatever, especially for, you know, Mid-Market companies that have multi entities. You get over into Europe multi-currency that complexity. So maybe talk a little bit about that.
[00:18:53] Guest: Santiago Nestares: Next day Europe migration. It works with not every single system but the most popular ones. So it will connect via API. All the data will be drawn. And then there's a few steps that we do. And it's all like product LEDs. So you first go in and create all your entity trees. So it can be multi and can be international. And then you would connect that to the origin system. If the system is a single entity you would connect it multiple times to each entity. And we'll map that into a unified GL so that we'll create a GL every time we see a new account. And every time we see a repeated account, we'll map it. You always confirm these mappings. So we got something semantically wrong. You can always override it. Ai will also take a pass. So if you named AR and accounts receivable we'll still try to make that match for you. And once everything's mapped it's now locked in a deterministic way so that you don't have to trust that AI will get it wrong in every transaction. And then we'll start syncing the data over that, depending on the throttling that the origin system has on their APIs, I might take a few hours or up to 24 hours. And now the data is going to live in DualEntry for the first time already for you to consume. So you can then run a PNL and a balance sheet and you can start segmenting. We'll even pull on all the classes, all the dimensions, all your items, your vendors, your customers. Everything is going to be pulled over, and then you can just see it and then test it on your own, see how you'd run a PNL or a balance sheet.
[00:20:10] Guest: Santiago Nestares: Now we recognize that implementation is much more than that, right? Like we want to move over. We also want to show you around the things that you can do now, like whether it's period locking or rules or or the new AI features. So we'll schedule a few calls with your team. So we actually have two migrations that go on. One is during the demo phase where we do this and we just give you access to it, and then you get to play around with it. Um, and then we guarantee like 99% accuracy because sometimes there's some edge cases that would fail and then we'd treat those manually. There's always a human, by the way, in the implementation side that verifies the final ledger. But in implementation we'll run the same script and then we'll verify it in the end with a human. And then we'll schedule all these calls with your team to make sure that you're ready to go up and running. We actually schedule calls, but if you want to do more, they're free. We're here for you. We want to make sure that you take the most advantage of the software. And that's what we call the broader implementation process. Some companies or some teams decide to like, use the opportunity of the migration to redo their chart of accounts, to redo how they do workflows. Sometimes they even use it to change external providers, and they might go to a different spend management software.
[00:21:16] Guest: Santiago Nestares: And we're there for that journey too. And we'll connect you, you know, once you feel ready that the data is now flowing through, by the way, this ERP migration, you can refresh it every 15 minutes. So you can still get the data. So there's no stop. That data can keep on flowing. But once you're ready, you unplug your integrations from the old system and you plug them into DualEntry. We verify that things are flowing, that there's nothing left in between, and then you're off to the races and it's all transparent in product lead. So you can see how it worked. You can see how we got to that answer. You don't have to talk. You know, if you want to log in at 11 p.m. and see why. Why things happen the way they happened. You can see it right there. It's all accessible. So it's not like, you know, you don't have to depend on a foreign team that's doing spreadsheets. And, you know, you don't even know what stage of the process they're in. And that's been a winning feature. And it's allowed us to really demo the product in depth. It's also surfaced bugs early days, because then you give them the product and say, hey, this doesn't work. And they're like, oh shit, we got to fix that. But I think it's good. Like transparency and having people use the software is just the best and most healthy thing that you can do is that you really earn the software. I like to, you know, when we started in building DualEntry, we said Europe is like chronic back pain.
[00:22:19] Guest: Santiago Nestares: It just gets worse over time. People know they have it, they're aware of it, they want to get rid of it. But they've also heard of horror stories of people who go get back pain surgery and then come out with more pain with a 100 K bill. And it's a nine month recovery. So imagine showing up to somebody with back pain and saying, hey, I am a new guy. Like, you know, you've never heard of this hospital, you've never heard of me as a doctor. I'm the new guy in the block. But trust me, I have a better method to get rid of your back pain. But you have to go through this implementation process, which is surgery. You'd be like, no way on earth I'm doing that. There's just no way. So we said, like, how can we fix that? How can we make it so it's not back pain surgery, and maybe it's more like a massage where people can actually see the results quicker without it being high risk and high cost. So, that's how, you know, the next day you're willing to get a massage from a random person that you've never met, whereas you've never you're not willing to take back surgery for somebody that you've never met. So that's how we build trust. We try to show you the benefit up front and get rid of the stigma that it's going to take nine months and cost you 3300 K, and it's going to get you fired.
[00:23:24] Host 2: Glenn Hopper : Yeah. So it's refreshing to hear you say this because I think about these migrations it's structured tables everybody. This table contains this data in this system. And this data is where it needs to go over there. It shouldn't be rocket science. So anytime you've made the move in the past, it's dumbfounding. And the first time you do one of these implementations, it's shocking to think, wait a minute, this isn't solved for this is a bespoke process to do the migration. So that's a huge thing to just get the data over. And when you say it, obviously there's an API, there's a map, you understand the schema of the database and each side it should be seamless. But this might be telling of my age and my willingness to do the startup where I spent a good part of my career in startups myself, and thinking about deciding you're going to build a mid-market ERP. To my mind, that's such a bold thing to do. I would, you know, if I were going to build a gel system, I might say, let's start with SMBs that don't have audit responsibilities. And it's, you know, it's a lower stakes game and we can do it cheaper or whatever. And we could just have it provision themselves and all that. But then when you start thinking about mid-market companies and public companies and you have to factor in so many things, the entire finance and accounting process.
[00:24:44] Host 2: Glenn Hopper : But then things like maker check approvals, period locks, reversals, exportable evidence for these AI generated entries and I'm I think about how challenging it is to go after that market when whether it's the devil you know or whatever, where you're going against them. But people are nervous because you have auditors who are going to be looking at everything. And I'm wondering and I'm imagining that you talked a lot to auditors, and I'm wondering what kind of feedback you might have received. And I'm thinking about Icfr socks, all these things that these larger companies require. And if you hit any roadblocks there, or how do you even. I'm just rambling now, the whole thing just seems so complex to me to take on just, you know, we're going to build this from scratch and cover everything that an ERP needs to do. Like how do you map that out, work with the auditors, get that level of trust and that I know you said you know AI where AI makes sense and then deterministic in other areas, but there's just so many steps. And unless you are an auditor who also happens to be a coder, it just seems like a lot. So I don't know. I threw a lot at you right there, but I'm trying to figure out building it, making sure that it meets the needs of these high demand clients that are in that mid market space.
[00:26:01] Guest: Santiago Nestares: Yeah, we broke all the rules of entrepreneurship by starting with larger mid market companies. Every advice we ever heard was like start with smaller companies and work your way up. But if you talk to smaller companies, they don't have as much pain. They do have some, but they don't have as much pain as these big market teams have. As things start to scale, you need to add controls. You need to go through audits. So we just went straight to the pain. Like when we were running our company, we didn't have that much pain early on. It was really when we hit the mid market and the growth inflection that things started to break and we were willing to pay way more money than what we were paying even NetSuite to solve the issue if it was actually going to be solved. So for us, it was like it's an obvious problem. It's just a hard solution. And those are the problems that we like because we like to build good executing teams. The thing about engineering and building products is that the difference in the bell curve between teams is not just like 1 or 2 X, right? You know, you can get an engineer and a product team that can deliver 20 times better, faster, higher quality features on another team from another company.
[00:27:00] Guest: Santiago Nestares: I mean, the contrast between the average engineering team and one of these legacy companies and our engineering team is just mind boggling. You could probably put a whole stadium of these people, and it wouldn't ship as fast as some of our teams ship, but the attention to detail that we ship. So if that's true, then finding a hard problem and then just trying to build around that problem in a 10 or 20 times faster team, that is the winning formula. Because the problem is obvious. You either have market uncertainty or you have, you know, hard problems and competition. And we'd rather pick the latter because at least, you know, you're solving something that matters to the world. And yeah, look, we involved auditors from the very beginning, and we asked them what they would like to look at. And, you know, auditors.
[00:27:40] Host 1: Paul Barnhurst: Are.
[00:27:40] Guest: Santiago Nestares: Don't have the same negative incentives that some of the other partners have. And they're actually quite, quite benevolent. They want to get the audit ready. They're competing to try to give you the audit standard at the lowest cost and make you happy, so that you return next year. It's quite a different relationship, and we've gotten to know some of them have invested in DualEntry, like tons of great feedback from what we've seen when they see the product, because I think they see how it's actually easier to do their job in a modern system. They also have to log into some of these systems, and they also, you know, they live the same pain that we lived as users. So we're also helping them. And it's quite fulfilling to be on this, on this journey.
[00:28:18] Host 2: Glenn Hopper : Something you said resonated with me because I do a lot of, um, AI and finance training for finance and accounting professionals. And one of the things when I talk to audit groups that I talk about is, what if AI could help you? Instead of doing sampling, you get the full data set and AI helps. You know you're not just dragging samples, you're looking at everything. Um, I'm wondering if that's something we're not going to change regulations and all that on this, on this podcast. But I'm wondering if that's something that you've talked to auditors about because of the AI nature of the platform and the data that you have, sampling versus doing an AI run on the full data. Could be an option. Is that something that you guys have looked at or considered or talked to them about?
[00:28:58] Guest: Santiago Nestares: Yeah, I mean, AI and technology in general is making this better and more accessible. And I think software also can play a good role there as an intermediary of trust. Right. Like the thing about these legacy ERPs is that people have just built so many workflows around them and have used so many external tools because they're so deficient that the auditors have to go into your spend management tool and audit that, and they have to then go into your equity tool and audit that, and then make sure that the data on that tool matches the data on the ERP because it's manually inputted. So in that manual input a mistake could have been done or fraud could have been done. Who knows. But if the software takes the broker of trust in here where it says, look, no, this invoice came from here and we have a link and we're hashing these transactions. So you actually can modify it from when it came out. If it doesn't be modified, then we can flag it as modified. And actually we're pulling it over the attachment. It's all centralized. And high resolution is much easier for you to do this audit. You go into one system with one set of permissions. Then you can look at what you need to look at and sample what you need to sample. Or like you said, just feed it to a model and say, hey, you know, AI agent, pick up 30 invoices and pick up 30 transactions and see if they actually match.
[00:30:05] Guest: Santiago Nestares: And the categorization is accurate. So like software, it can actually be a good broker here. I mean we're inspired by a company called Vanta who does a lot of compliance. They're a broker in the Soc2 compliance space. Right. Like they're the ones doing the check with your AWS, making sure your infrastructure is correctly set up or with Google Cloud, right, making sure it's properly set up. But then those reports are then used by auditors to come up with the conclusions a little bit faster. And it makes everyone's lives easier. And you trust that Vanta did the API check with this system, right? You start to see this happen more and more and make the auditors more efficient. And as the data becomes easy to query and easy to validate, the room for error is going to go down and down. And in theory, this should make you know, there's all this talk about reporting being, you know, semiannually or annually instead of quarterly. Like reporting should actually be more frequent than it should be more data available to public markets or to your investors. So I think we should be heading in that direction.
[00:30:59] Host 2: Glenn Hopper : It just opens up so many more possibilities when you have this level of baked AI on it. And it's a completely new paradigm. Okay, so we've hit the part of the show. So this is what we do. We take our guests' background, company background, and we feed it into our favorite AI machine. I'm using GPT this week. Um, and uh, we have to generate 25. Random prompted something with, uh, you know, fun loosely finance and tech related questions and have the AI spit them out. So, uh, we asked two out of the 25. So you're not about to get a full, like, uh, 25 question, uh, speed round, but, Paul and I have different approaches to how we select the questions. So, Paul, maybe I'll let you go first with your methodology and then I'll do my selection next.
[00:31:45] Host 1: Paul Barnhurst: I believe in choice unlike Glenn. So here are your options. No, I'm kidding. Um, one you can randomly pick a number between 1 and 25, and whatever number you pick, you get that question. Or I could have them randomly pick a number and I'll ask that question. So which do you want?
[00:32:07] Guest: Santiago Nestares: I'll do nine because of the 90 million. Let's do it that way.
[00:32:10] Host 1: Paul Barnhurst: All right. Interesting. I'll be curious to see what you say to this one. What's your superpower inside a spreadsheet? Oh, wow.
[00:32:22] Guest: Santiago Nestares: Trying to get you out of it. Um.
[00:32:24] Host 2: Glenn Hopper : Yeah, that's probably the perfect answer right there. Right.
[00:32:29] Guest: Santiago Nestares: Although I will say, you know, it is a very hard thing to replace because it's ultimate malleability ability and our job at DualEntry is how do we get one step further in that process so that you do less things in that spreadsheet? But we ultimately recognize, like taking away the canvas from an artist. You need to have that at the end of the day. And yeah, so our goal is to take most of it away from you, but not all of it.
[00:32:55] Host 2: Glenn Hopper : Yeah, but let the FP&A guys still have their spreadsheets because they love them. Exactly. Yeah.
[00:32:59] Guest: Santiago Nestares: We take every account out of the spreadsheets and just leave that to the FP&A team is maybe, maybe the way to say it.
[00:33:05] Host 2: Glenn Hopper : Um, okay. Well, for my question, I feel like, um, I'm just handing over the keys to the AI and it selected or it came up with the questions. So I just let AI select. So let me hang on one second. Let me have an AI pull up here. And that was a weird one, Paul, that I don't know where that came from. The spreadsheet question. Yeah. The first time.
[00:33:24] Host 1: Paul Barnhurst: We've had a spreadsheet question, but hey.
[00:33:26] Host 2: Glenn Hopper : It might have had some injections. I'm gonna blame it on being there.
[00:33:30] Host 1: Paul Barnhurst: Glenn.
[00:33:30] Host 2: Glenn Hopper : Yeah, probably to do.
[00:33:32] Host 1: Paul Barnhurst: That since I didn't create him this time. You know, I'm gonna going to stick with my story of blaming you. Or hallucination. I hallucinated it's one of the two.
[00:33:40] Host 2: Glenn Hopper : That's right.
[00:33:41] Host 1: Paul Barnhurst: What? Our audience decides.
[00:33:43] Host 2: Glenn Hopper : This one's way better. I don't know, maybe I should have. Maybe I should have proofread the other ones. I like this one, though. Would you rather debug a broken AI model or explain the gap to a room full of engineers?
[00:33:55] Guest: Santiago Nestares: Gap to a full room of engineers? That's what I do all the time. Yeah, you'd be surprised. Like, accounting is quite like an engineering discipline. It's very logical. It follows very clear rules. The rules can get updated, but they're there. There's no like there is room for subjectivity, but it tries to remove room from subjectivity. And. And it all adds up to zero, which is the most magical part of it all. So I think it's actually quite compatible. Or most, I don't know, at least they say that to me. But most of our engineers actually quite enjoy accounting.
[00:34:29] Host 2: Glenn Hopper : Makes sense. I mean, it's that same sort of engineering mindset. And it is deterministic and.
[00:34:34] Host 1: Paul Barnhurst: It's logic rule based, right? You know, if you got it wrong.
[00:34:38] Guest: Santiago Nestares: Exactly.
[00:34:38] Host 1: Paul Barnhurst: At least in total, you might not know where the individual problem is, but in total, you know, if you got it wrong.
[00:34:43] Guest: Santiago Nestares: If it's not adding up to zero, there's something wrong with the software, you know. Yeah, yeah. No, I think it's actually quite compatible. I wouldn't even try to debug a model because the model, you know, the bug might be there once and then you run it again. It's not there anymore. So that's why we always prioritize DualEntry. It's always first, you know like can I come from an integration. So this work has been done before. And then if not can it rule determine it? Can we create it? And I can be helpful in creating these rules. But ultimately you prove the rule and then only thereafter then AI will take care of it, which is the most malleable. It's kind of like a catch all, but a human always, always has to verify it. Because even if we test it and say, you have to write 999 times out of a thousand, you know, that one is very important. So humans always need to be the one giving it the thumbs up. Now, the beautiful thing about accounting and marrying it to the model is you get instant reinforcement, right? Like if somebody says this transaction was great and then post it. That's a thumbs up. You can use that to feed it back into the model and say, hey, you did a good job. Do more of this. Whereas the person has to go in and edit it. It says, you did a bad job. But by the way, here's the right answer. So next time, now you know what the answer should be like. And that's a very unique thing to accounting, which is why we're so bullish on AI, on accounting in general.
[00:35:52] Host 2: Glenn Hopper : And so with every transaction is what you're getting. Well, Sandy, this has been great. We appreciate you coming on and hearing about DualEntry and uh, and everything you guys are doing. And, uh, we wish you the best of luck going forward.
[00:36:04] Guest: Santiago Nestares: Thank you for having me. This is great.
[00:36:05] Host 1: Paul Barnhurst: Thanks for listening to the Future Finance Show. And thanks to our sponsor, QFlow.ai. If you enjoyed this episode, please leave a rating and review on your podcast platform of choice, and may your robot overlords be with you.