How Encapture Reached 50 Bank Customers and $10M+ ARR with Machine Learning Document Processing (Interview with CEO Will Robinson)

Introduction and Founder500 Event Announcement

Nathan Latka

00:00Hey guys, recording this here on what is it? Friday the nineteenth. Maybe you're seeing this on Monday at the latest, but wanna let you know we are almost sold out for Foundercomp Sorry, Founder500 in Austin, Texas here in about a week. It's gonna be an amazing event. 500 B2B SaaS founders. I'm looking at the attendee list. There's almost 60 founders with more than $67,000,000 in ARR. It's an incredible group of group. There's over one and fifty

00:27with more than 1,000,000, more than a million revenue. It's an incredible group. You don't wanna miss it. Grab your hotel, grab your flight, grab a ticket right now. I'll put the link in the bio in the description here on YouTube. And I think there's only about three tickets left. Okay, about three tickets left. I'd love to see you guys there. Don't be bashful. Grab your ticket now. Hey, folks. My guest today is Will Robinson. He's the

Introducing Will Robinson and Encapture

Nathan Latka

00:48CEO at encapture, a high growth SaaS platform that helps banks automatically extract important information from documents. Launched twenty years ago in Dallas, Texas. Encapture helps companies such as Wells Fargo, Frost Bank, and Truist save time and money by using machine learning to process large amounts of data. Will, you ready to take us to the top?

Will Robinson

01:03>> Let's do it, Nathan. Thanks for having me on.

Nathan Latka

01:05Alright. You don't look like you you can't be that old. So this company is founded twenty years ago. How old are you?

Company History and Will's Path to CEO

Will Robinson

01:11>> Yeah. I was not the founder. That's the short answer. So that's what's funny. This company, yes, started back in '98, actually twenty four years now. And I I joined as CEO about three years ago as part of a big transformation that we made as a company. The company started as a professional services company that worked and partnered with some legacy automation software companies in our industry. And, those software companies would bring us in, and we would

01:35>> help sell and implement their software. And over time, we started building our own product internally to kinda fill some gaps in the market. And I joined about three years ago, we made, kind of a big decision to pivot away from these legacy guys and focus purely on the product that we had built, over the previous decade. So I had a really, fortunate opportunity to step into a business with a lot of folks who knew, what they're

01:59>> doing, knew the market we were selling into, and a lot of it was just kind of reprioritizing, you know, how we're going to market, where we focus, making sure we have the right folks on the bus.

Nathan Latka

02:09And and what was the team size when you joined in 2019? Just so we get a sense of the operation.

Will Robinson

02:14>> Yeah. It was in the mid thirties.

Nathan Latka

02:1635.

Will Robinson

02:16>> I think 36, 37 folks. And, you know, it's, yeah, we're now up to 75. And, you know, and and and look, Nathan, people don't like to talk about this a lot. And and, you know, I don't say this as like a, you know, this is not a badge of honor. But I think transitioning the business was hard. And, you know, you know, making sure that we had the right folks here, was important. And some folks were just

02:40>> like, hey. You know, what I've been working at, and where you were going is not not a fit for me. And so we've had, you know, a lot of change kind of top to bottom in the organization to bring in people that are excited about the vision that we've set and kind of where we're going with our own product.

How Encapture Works: Mortgage Document Use Case

Nathan Latka

02:54So let's fast forward to the product today. Right? So give me a use case. What's an example of a document a bank would need your software to extract data from?

Will Robinson

03:01>> Yeah. Great question. So I use the mortgage example a lot because most people have bought a house. But when you go talk to a loan officer about buying a house and applying for a mortgage, they're gonna ask you for a copy of your driver's license, a recent pay stub, probably the last two years to your tax returns. And they're building this financial profile on you to understand how much money do you make and how much of

03:19>> a how how big of a mortgage can you qualify for. Typically, in a bank, there are people in the back office that as you send in those documents, they're manually typing in your data. They're manually reviewing all the data to make sure it's correct, it's accurate, you know, that if you say you make $80,000 a year, your pay stub actually pencils out to an $80,000 a year income. Our system can come in and automate that entire

03:42>> process. So we use machine learning, make it easy to collect those documents. And once we have them, we can read the documents automatically. We can extract the data. We can do these calculations. We can verify that the data is consistent across all the different documents so that people don't have to spend and candidly waste a lot of time doing that.

Nathan Latka

04:00Oh, what's going on there, YouTube? Good to see you guys. Now imagine this. You love watching these interviews with SaaS founders, but imagine if we took all of the valuation data out from over 2807 interviews I've done manually. Saves you a lot of time. Well, we've done this. We've built it into the beautiful interface inside of Founderpath. Check this out. I'll show you how you can access this in a second. But you log in, you connect

04:23your Stripe account, you see your valuation real time, you can see what changed over the past eighty eight days and even set goals for valuation this year. Now the secret evaluation is there's many different ways to value a SaaS business. So the reason you're gonna see three or four different valuations inside of your Founderpath dashboard, this is all free by the way, is because depending on who's doing the buying of your SaaS company, you're gonna get

04:47a different valuation. A VC is gonna pay a different valuation. Private equity firm is different. If you're gonna do a minority sale, that's different. And if you sell the whole business, that's a different valuation. You can see all those when I hover over here. Right? So the teal is what a VC would pay. Yellow is what private equity and red is if you sold the whole thing outright. Now what's cool about this is this is not

05:09built off random data. Again, you guys hear these interviews on YouTube. All these datas are built from real time valuation data points founder share with us on the show. So traction, 1,200,000 seed round, 3.7 raise. They sold 22% of their business. Go in here and filter by the event. Maybe you only wanna see companies that have sold the whole business. Well, here are a bunch that have been acquired the valuation and the multiple. Maybe you're going

05:35out right now and you're raising your seed round. We'll go in here and look at all this recent seed deals that went down, what they raised, what valuation they raised at, and what percent that they sold. There's never been a larger dataset of SaaS valuations than what you can get now inside of Founderpath. And we're thrilled to bring it to you. Alright. We're gonna go back to the YouTube video here in a second, but if you

05:57wanna check this tool out, if you wanna jump in and sign up, you can check it out for free to get your valuation at this link. This link, founderpath.com/products/valuations. Or if you go to founderpath.com and hover over products, click on get your valuation here, and go ahead and sign up to give it a whirl. Again, all that valuation data live right inside the platform. I hope to see you there. Alright. Let's jump back into the interview.

Training the AI: Supervised Machine Learning

Nathan Latka

06:24Power of AI usually is a direct correlation to the power of the the testing set that was fed the machine in the first place to learn on. So it's really hard sometimes to get your your, you know, a grasp of a large enough testing size to make the AI actually useful. What did you guys use to train the AI in the first place? What was your testing cohort?

Will Robinson

06:44>> Yeah. That's a great that's a great question. And it's funny. There's standard documents, you know, things like driver's license that are that are very standardized. There's a lot of them. It's easy to get trained up on what we call, you know, structured content, but documents that come, in the same format every time. But something like a pay stub, there's a lot of variety in that. The the layout, where the data is, where it's coming from. And

07:07>> so, you know, we've been able to train on hundreds, if not thousands, of datasets, of sample sets to really improve our machine learning. And I'll tell you something else, Nathan, kind of a secret in our world. When people think of machine learning, they think a lot of kind of what they see in commercials, which is unsupervised machine learning, where you just feed massive datasets into a system, and then the system naturally gets you know, understands patterns

07:31>> and gets smarter on its own. We actually employ, supervised machine learning technique, which is where, we feed data to the system, the system starts to look for patterns and trends, and then we as humans can come in and actually influence the models that we build and help either, you know, affirm certain decisions the the the system has made, or we can correct maybe bad errors or assumptions based on what we see in the data as well.

Nathan Latka

07:54Understood. Yeah. And so how many on your team of 75 are full time engineers?

Team Size and Engineering Headcount

Will Robinson

07:59>> We've got probably 25 or 30. Yeah.

Nathan Latka

08:02Okay. Interesting. And then I guess give me a a sense of sort of how you price. So what is your average customer gonna pay to use this technology today?

Will Robinson

08:09>> Yeah. So we have these broad we we we price based on the volume of pages, the number of pages or the number of documents that come through our system. So if you think about it, our the whole value prop of our system is saving you time and money. The more, the more documents that you can run through our system that people don't have to mainly review, the more value you get out of the system. So that's

Pricing Model: Volume Tiers and Fixed Annual Fees

Will Robinson

08:30>> really the that's really the drivers. We have these broad based buckets of really millions of pages. If you think about it, a lot of these banks are these are high, high volume situations. And, we're pricing it just as a fixed annual fee based on the volume tier you're in. The good news is the more volume you send through the platform, the incrementally cheaper it gets for you as a customer of ours. So we really encourage our

08:50>> customers, hey. Send more and more stuff through. Let's let's, you know, put more lines of business, onto our platform, and it gets cheaper, and that ROI, gets a lot stronger.

Nathan Latka

09:00Okay. That's all well, I understand that, but that doesn't answer my question. So what what is the sweet spot? Are we talking, like, a $100,000 a year contracts or a million dollar a year contracts or something lower?

Will Robinson

09:07>> Yeah. No. No. It's, yeah. Typically, a 100, a 150, $200,000 is our starting spot. We've got several customers spending more than a million dollars a year with us.

Nathan Latka

09:15Okay. And so for someone paying you guys more than 1,000,000 a year, how many pages are they probably processing?

Will Robinson

09:22>> You know, they're it's at least you know, it could be twenty, thirty, 40,000,000 pages a year.

Nathan Latka

09:2840,000,000 pages per year. Interesting. Okay. Got it. So a million dollars would get you 40,000,000 pages sort of processed per year. That's sort of the right ratio to think about. Your volume pricing is that.

Will Robinson

09:38>> Roughly. Yeah. Yeah. We've got guys yeah. I mean, we've got guys way over a million too.

Nathan Latka

09:42So Yeah.

Will Robinson

09:43>> It's typically you know, again, yeah. If if you're thinking about it kind of on a per page basis, it gets a lot cheaper the more you can scale with us. Kind of that original that original model training and setup and stop, you know, and kind of the lower volumes, you really start to scale as you can layer on more and more volume.

Nathan Latka

10:00Yeah. Yeah. A million divided by 40,000,000 pages. What is that? Like, point o two five cents per page. Yeah. It's not something like that. Yeah.

Will Robinson

10:07>> It's pretty inexpensive, which is nice. Yeah.

Nathan Latka

10:09Interesting. Okay. And then so so launched in 1998. You joined in 2019. 35 employ I guess, I wanna get a sense of, like, sort of what you've done at the company. Right? Since that's what you're best able to speak to. So how many customers was the company working with back when you first joined day one?

Customer Count: From 15 to 50

Will Robinson

10:25>> Yeah. We probably had 15 or 20 on the platform, and now we're at I think we're gonna cross 50 this year.

Nathan Latka

10:34Oh, wow. So help me through that. I mean, signing up banks is not an easy process. That's quite a sales cycle. How did you land, you know, go from 15 to five zero?

Sales Motion: Building the Business Case for Banks

Will Robinson

10:42>> Yeah. A big part is that process. Like you said, banks are they you know, they're conservative, organizations. They move slow. The big thing for us is helping we're not here to actually sell the software to the bank. We're here to help them build the business case for the for the software. So it's really funny. A lot of our competitors will do, like, ten, twelve, 15 demos. We do, one demo or two demos just to, like, help

11:04>> people be aware of how the thing works. But then we spend a lot of time understanding their business process, understanding the opportunities they have for automation. We have benchmark data that that we have from other, clients of ours where we can say, okay. For this type of loan process, here's where we can save you money. And then we do a big math exercise that says, okay, you have this big of a team. This is how much

11:25>> time is being spent. Here's where we can eliminate or reduce certain tasks. So here's how here's how much money we can save you every year. And, it's it becomes a really powerful sale to, you know, to bankers who are very, dollar kind of bottom line numbers driven driven.

Nathan Latka

11:39That makes a lot of sense. Now how have you capitalized the business? Are you guys bootstrapped it, or have you raised capital?

Will Robinson

11:45>> So we are backed by a by a private equity firm here in Dallas. So that's when I came on, bought out the the original founders and and kind of management team. They put a little bit of money in in the business, but I would say we are running, you know, since then, we're running kind of more on the conservative scale and kind of organic growth, organic funding. We've been able to grow, great capital efficiently through that.

12:08>> And but I would say in the next year or two, we'll be in a spot where bringing a new capital partner. We've just got big dreams. There's a huge market here, and, we have a lot we wanna go do.

Capitalization: Bootstrapped Origins and Private Equity Buyout

Nathan Latka

12:17Well, okay. I guess, let's talk pre private equity because I know that, obviously, the cap table changes drastically when private equity comes in. So pre pre private equity, had had the company raised a bunch of equity, bunch of capital?

Will Robinson

12:27>> No. No. Just been completely bootstrapped by the original founder.

Nathan Latka

12:30Okay. Okay. Got it. So it's bootstrapped by the original founder. He was like, I'm, you know, I'm tired. I'm getting older. I want out. Whatever. The private equity firm comes in. Now is he still on the cap table or the PE firm, was it a a 100% buyout, majority buyout?

Will Robinson

12:41>> Yeah. Yeah. They bought out a 100%. And so he was he was at he was he was ready to retire kind of at that stage in life. And so it was a good opportunity for him to get liquidity. It was a good opportunity for me to join. I had a prior relationship with these guys, and, it was, you know, it was Who was

Nathan Latka

12:56the firm, by the way? Who was the private equity firm?

Will Robinson

12:58>> Called Alturus Capital. They're they're here in Dallas. Great.

Nathan Latka

13:02Alturist. Alturist. Interesting. And did they line you up before the deal was done? In other words, they weren't gonna do the deal until they knew the CEO was gonna be?

Will Robinson

13:10>> Yeah. We had partnered together, actually. And so I had I had approached them probably a year prior, and I have a background in software and tech. And I said, hey. Look. I'd love to go find a small software business that we can grow together. And, again, I knew these guys. We were really aligned on kind of how we think about investing and values. And, and so we kind of came in and did this deal together, and

13:28>> that was a big part of doing the deal was, hey. Could I build conviction as the guy stepping in as the CEO on growing this business and creating a lot of value, which we were ultimately able to do.

Nathan Latka

13:38Yeah. Interesting. And you said this was the firm it's a a a l t r u I s t, Altruist?

Will Robinson

13:45>> It's Alturus, a l t u r u s.

Nathan Latka

13:48Oh, u r u s. Got it. Got it. Got it. Very cool. Now do they have they done this historically a ton sort of SaaS PE?

Will Robinson

13:55>> No. This was one of their first, they've done a couple technology deals, but this was their first SaaS deal. And so that was my background. And, you know, they've been super supportive. And, you know, it was a good opportunity, to kind of come in and get them excited about it. And I can tell you now they they wanna do a lot more.

Nathan Latka

14:12Yeah. I was gonna say, so, obviously, the the motion here usually is organic growth is fine, but inorganic growth is way more interesting. If you're the hub, what are the next six spokes? So how are you thinking about m and a, and how much capital do you have at your disposer disposal via Alturus to go do a roll up strategy?

Will Robinson

14:27>> Yeah. It's funny. We talk about this a lot. This is a highly fragmented market, so there is a lot of opportunity for m and a. We have been growing organically so well, like, 100% year over year for several years. And so, candidly, we don't have time or or effort, and the equity value we're creating through our organic growth is and the time that we're able to put into that is worth a lot more than us maybe

14:49>> breaking away from that and and doing m and a. So I think we wanna get a little bit bigger and really kinda prove out our process because I think a lot of the the value in a roll up is gonna be around finding maybe some legacy, you know, business models that are processing documents either manually or partially manually, being able to bring their clients onto our platform and provide a lot of lift for them.

Nathan Latka

15:11Yeah. And then, Will, in terms of sort of scale today, I mean, we can sort of estimate based on what you shared with us. Right? Five zero customers at a sweet spot of a 150 k per year. Obviously, with the caveat that it sounds like you have some whales in there. Right? Some million dollar plus accounts. But if we take a 150 k a year times 50, that puts you at a minimum of about 7,000,000 in

15:29terms of run rate. Is that fair? You guys are north of that at this point?

Revenue Scale and ARR Discussion

Will Robinson

15:32>> We are north. Yeah. One fifties are minimum. So Yeah. Average deal size, you know, we're yeah. I'd say we're probably 4 to 500 k average deal size.

Nathan Latka

15:40Oh, got it. So you're much I mean, you're you're pushing, like, thirty, thirty five million in ARR then, something much bigger.

Will Robinson

15:45>> No. We're no. I think we're no. We're we're not that we're not there yet. Okay.

Nathan Latka

15:49Can you break 30,000,000 this year?

Will Robinson

15:52>> I would love to. No. It's probably probably eighteen, twenty four months out.

Nathan Latka

15:56Alright. Fair enough. Fair enough. I guess last question I've got, before we wrap up with the famous five. You know, guy like you again, how how old are you?

Will Robinson

16:03>> I'm 35.

Nathan Latka

16:04I'm just guessing. Right? You're a young guy. Right? So how does a PE firm sort of convince you to come in and do this when you could go, you know what? I'm young. I've got energy. I could just go start my own thing from scratch and own a 100%.

Will Robinson

16:15>> Yeah. I had to convince them actually, Nathan. That was my strategy is, you know, I it's part of it like figuring out yourself and kinda what are you good at. And I had done some really early stage stuff or been involved in that. And I felt like I do best in my experience when I have something to start with and grow. And so kinda that zero to one, was like, hey. Start me at the one, and

16:35>> let me take the one to five or the one to 10. And so, you know, that was the model they were comfortable with. I think, you know, they're the type that don't wanna do the zero to one. And so we were able to come into this business. We had enough traction. We knew we had product market fit. There was a lot of work to do around kind of repositioning vision and culture and strategy and people and

16:54>> process. But in terms of the the pain we were solving for our customers and having enough traction, we we felt very confident that that we could do something with this.

Nathan Latka

17:02And when you look at your success so far the past three years, what was on a percent basis? What's total revenue growth since 2019 up through today?

Will Robinson

17:13>> Well, let's see. Inc the inc 5,000 came out yesterday, and I think that's a three year period. And I think we're at 270%. So that's probably

Nathan Latka

17:21Okay.

Will Robinson

17:21>> That's probably close to what it would be.

Nathan Latka

17:23So does this inc 5,000 you have to do you do they publish the revenue figure?

Will Robinson

17:28>> No. Just the percentage.

Nathan Latka

17:30The percentage. Got it. Two seventy. Got it. Well, look. If you're north of 8,000,000 and south of 30,000,000, can sort of back into something. Right? But 270% growth is impressive. Would you argue that most of that revenue growth has come from adding, again, those new customers, or is it more from getting the current customers to process more pages per year?

Growth Drivers: Upsell and New Logo Engine

Will Robinson

17:46>> I think it's been both. You know, one thing we had to validate coming in was our historic model, we hadn't done a good job of going out and finding new customers. We had been relying a lot on these legacy customers from legacy partners to to sell our product. And so it was really a two pronged approach. Let's go back to our existing customers. And, we we had a a bunch of r and d to do on

18:06>> the machine learning side to really get the product to where I felt like we needed to go and get full value out of it. And so a lot of it was us, you know, adding these capabilities, going back to the existing customer saying, hey. This is gonna provide a ton of value. Let's, you know, let's get you guys, you know, spending candidly spending a lot more money with us, but doing a lot more with us, which

18:26>> resulted in a lot of upsell. And then, you know, restarting or really starting from scratch kind of our our go to market, for net new logos. And so it's been it's been, a balance. Would say in the early days, it was a lot of working with existing customers while we got our go to market figured out, and now we've got a really nice lead engine, for new logos.

Nathan Latka

18:43What is lead gen? Where do get leads from?

Lead Generation: Outbound and Events

Will Robinson

18:45>> It's a heavy outbound and heavy event driven. That's been the best for us. And you think about our deal size and you think about who we're selling to, there's a very targeted group of people who buy what we what we sell, and they're not you know, it's it's not it's a very enterprise b to b motion. So we spend a lot of time and effort getting to know people, finding the right people, crafting, very kind of

19:08>> curated messaging towards them. Events have been great, especially kinda coming back over the last year, coming back from COVID, people really wanna be at be at these events and, see what's out there. So, that's been that's been our strategy so far. We probably need to diversify and will, but it's, you know, it's one of those if it ain't broke, don't fix it kinda situations.

Nathan Latka

19:28Very cool. Let's wrap up here with the famous five. Number one, Will, what's your favorite business book?

Will Robinson

19:34>> Man, about six well, I'll take my most recent that I love. It's called No Ego. And it was about how to build high performance teams, how to make sure that people are thinking about your business, thinking about teams in the right way, having a pot positive assumptions about what they do. We actually had the whole company read it. And, I don't have favorite business books because I read a ton of them, and I feel like I

Famous Five: Books, Sleep, and Life Lessons

Will Robinson

19:52>> get a lot out of everything. That's the most recent. I will say this. My the the most impressionable one I ever read was probably ten years ago. It's called Servant Leadership. It was written by this guy Robert Greenleaf back in the seventies. He'd been a longtime IBM guy and then went to academia, and he talked about kind of successful traits of of servant leaders. That's really been probably a core of of of how I try to

20:11>> run the business where I'm not the guy at the top. I'm the guy at the bottom. And my whole job is to empower people around me to be successful in what they do.

Nathan Latka

20:17Number two, is there a CEO you're following or studying?

Will Robinson

20:24>> I feel like do well if you don't have a I feel like these days, it's just what not to do. And so I won't name names, but, it's folks, you can read about in the paper. I think that's been my key thing over the last six to nine months is watching kind of spectacular implosions of certain, especially high growth software companies have how not to lay people off, for example, how not to,

20:43>> maybe leave your current company and go do a new company. So I'll I'll leave it at that. I think that's been the most interesting for me is I feel like we got a good thing going here. We got a good we got a good culture. Don't screw it up.

Nathan Latka

20:52Number three, what's your favorite online tool for building Encapture?

Will Robinson

20:57>> Say that one again? Online tool?

Nathan Latka

20:59Favorite online tool.

Will Robinson

21:03>> Slack, I guess. I mean, it just keeps us so connected.

Nathan Latka

21:06No. That's good.

21:07Number four, how many hours of sleep do get every night?

Will Robinson

21:09>> Oh, this is a big one. I get eight to nine.

Nathan Latka

21:11That's good.

21:12And situation, married, single, kids?

Will Robinson

21:14>> Married, two kids, one on the way.

Nathan Latka

21:16So Oh, very cool. So three total, like, with the one on the way?

Will Robinson

21:20>> Two, three no. I have two kids and one on the way.

Nathan Latka

21:24So Got it. So three total here shortly.

Will Robinson

21:26>> Yeah. That's right.

Nathan Latka

21:27Very cool. And 35 years old, last question. Something you wish you knew when you were 20.

Will Robinson

21:32>> That, career paths are not linear. The most successful people in the world have very winding unusual paths, and it's okay to hold on to things loosely and just let it happen.

Closing Summary

Nathan Latka

21:43Guys, there you have it. Encapture.com legacy player in the bank space, services and software to the banking space launched in 1998. Will came in when a private equity firm came in and bought the firm. Alturus Capital bought it in 2019. He came in. He's since grown at 270%. The company today is now serving 50 customers at an average price point somewhere between 250 k and 500 k. Revenue call between sort of 10 and $20,000,000. He looks

22:05to continue to scale, doing it very efficiently. No outside capital raise since that private equity deal. It was a 100% deal back in 2019. 75 on the team today, 25 engineers, obviously heavy engineering when they're doing machine learning and AI, helping banks process documents faster. Their biggest customers are processing, call it, forty, fifty million documents per year. Will, thanks for taking us to top.

Will Robinson

22:23>> Awesome. Thanks, Nathan.

Nathan Latka

22:26One more thing before you go. We have a brand new show every Thursday at 1PM Central. It's called Shark Tank for SaaS. We call it deal or bust. One founder comes on, three hungry buyers, they try and do a deal live and the founder shares back end dashboards, their expenses, their revenue, ARPU CAC, LTV, you name it, they share it and the buyers try and make a deal live. It is fun to watch every Thursday, 1PM

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23:35for that at nathanlatka.com/slack. In the meantime, I'm hanging out with you here on YouTube. I'll be in the comments for the next thirty minutes. Feel free to let me know what you thought about this episode and if you enjoyed it, click the thumbs up. We get a lot of haters that are mad at how aggressive I am on these shows, but I do it so that we can all learn. We have to counter those people. We

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