[Emerging Tech] Eliminating Data Complexity for Growth With Mark Ovaska
- Quik! News Team
- 10 hours ago
- 29 min read
Mark Ovaska is the Co-founder and CEO of Precept, an AI-driven integration platform that helps fintech companies connect to any API quickly and simplify complex data integrations. He co-founded the company to eliminate lengthy and costly integration projects, enabling teams to scale without engineering bottlenecks. Before Precept, Mark led software platform revitalization and other ventures, blending technical leadership with product strategy. His background also includes creative work in photojournalism, with work featured in major publications.
Here’s a glimpse of what you’ll learn:
[1:58] Mark Ovaska discusses why great customer experiences start with great data
[4:41] Real-world example of broken data experiences
[6:09] The challenge of disconnected systems in wealth management
[8:22] How AI simplifies data mapping and integrations
[11:38] Eliminating the need to reinvent integrations and APIs
[13:45] Mark explains how AI changes the economics and speed of building software
[17:25] The strategic impact of AI on business models and growth
[19:39] Predictions on hyper-personalized software experiences
[30:14] Enabling agentic workflows through data connectivity and infrastructure
In this episode…
Many organizations struggle with fragmented data, sluggish integrations, and disconnected systems that create friction for both teams and customers. These challenges hinder the ability to deliver timely, personalized, and reliable customer experiences at scale. How can businesses overcome data complexity without adding more cost, time, or technical debt?
Mark Ovaska, a technology and product executive with deep experience in data platforms, integrations, and AI-driven systems, explores these issues. He explains why great customer experiences depend on connected, portable data and how standardization reduces friction and unlocks scale. Mark shares how AI can map and connect systems in real time, eliminate redundant integration work, and shift focus away from “how many integrations exist” toward readiness and flexibility. His guidance centers on treating data infrastructure as foundational plumbing that enables faster innovation, agentic workflows, and long-term growth.
In this episode of The Customer Wins podcast, Richard Walker interviews Mark Ovaska, Co-founder and CEO of Precept, about simplifying data integrations to power better customer experiences. Mark discusses AI-powered integration speed, the role of data standardization, and how modern infrastructure enables agentic workflows and scalable growth.
Resources Mentioned in this episode
Quotable Moments:
“Great experiences come from great data, and we're working on the data part of it.”
“Data has to live in multiple places at the same time.”
“Integrations can be done in real time.”
“Please don't reinvent the API wheel.”
“AI is built on data, and data has to have that connectivity and portability.”
Action Steps:
Prioritize data standardization across systems: Standardizing data fields and structures reduces friction between platforms, eliminates redundant work, and creates a reliable foundation for scalable customer experiences.
Use AI to accelerate integrations: Leveraging AI for data mapping and API connections dramatically shortens integration timelines, allowing engineering teams to focus on innovation instead of maintenance.
Design infrastructure for portability, not lock-in: Making data portable across tools ensures long-term flexibility, prevents vendor dependency, and allows organizations to adapt quickly to new technologies.
Treat data infrastructure as core plumbing: Strong backend systems enable seamless front-end experiences and allow teams to focus on delivering value instead of fixing operational issues.
Shift focus from integration counts to readiness: Measuring success by speed and adaptability rather than volume helps organizations stay competitive as software economics rapidly change.
Sponsor for this episode...
This is brought to you by Quik!
At Quik!, we provide forms automation and management solutions for companies seeking to maximize their potential productivity.
Using our FormXtract API, you can submit your completed forms and get clean, context-rich data that is 99.9% accurate.
Our vision is to become the leading forms automation company by making paperwork the easiest part of every transaction.
Meanwhile, our mission is to help the top firms in the financial industry raise their bottom line by streamlining the customer experience with automated, convenient solutions.
Go to www.quickforms.com to learn more, or contact us with questions at support@quikforms.com.
Episode Transcript:
Intro: 00:02
Welcome to The Customer Wins podcast, where business leaders discuss their secrets and techniques for helping their customers succeed and, in turn, grow their business.
Richard Walker: 00:16
Hi, I'm Rich Walker, the host of The Customer Wins, where I talk to business leaders about how they help their customers win and how their focus on customer experience leads to growth. Today is a special episode in my series on new and emerging solutions, and today's guest is Mark Ovaska, the co-founder of Precept. And today's episode is brought to you by Quik!, the leader in enterprise forms processing. When your business relies upon processing forms, don't waste your valuable time manually reviewing the forms. Instead, get Quik! using Quik!.
You'll be able to generate completed forms and get back clean, context-rich data that reduces manual reviews to only one out of 1000 submissions. Visit quickforms.com to get started. All right, I'm really excited to introduce today's guest. Mark Ovaska has 25 years of experience leading internet technology platforms across finance, media, and entertainment.
He's a product executive with a proven track record, navigating from early stage to growth and acquisitions, using first principles to uncover true customer demand and achieve operational excellence. And Mark, we all need to be doing that today. Mark is on a mission to eliminate the complexity of data integrations. As the co-founder of Precept. Mark, welcome to The Customer Wins.
Mark Ovaska: 01:37
Thanks. I'm glad to be here.
Richard Walker: 01:39
I'm excited to talk to you. So for those who haven't heard this podcast before, I just love to talk to business leaders about what they're doing to help their customers win, how they build and deliver a great customer experience, and the challenges to growing their own company. So, Mark, let's understand your business a little bit better. How does your company help people?
Mark Ovaska: 01:58
It's a great question. When you have great data, customer experiences tend to be really good, right? If you want to delight your customers, you want to give them the right information at the right time. And foundationally, that requires the right information at the right time, coming from the right source at the right time, and all the complexities that come downstream. So at the end of all of that, great experiences come from great data, and we're working on the data part of it.
Richard Walker: 02:25
Man, I love that answer. You know, I run a forums company, but what most people don't realize is that I run a data company. And it's not that I take in people's data, it's that I make their data accessible and available to the documents they're trying to work on. So when we talk about what the data is you're talking about, there's a lot of data count data, and new account opening information, and client records, and who knows what all. What are you talking about when it comes to data?
Mark Ovaska: 02:51
More specifically, I'm talking about data that needs to be connected to other data because that's where my head's been at for, for about 18 months. It is data transparency data portability. Because if you look at the truly, again, to go back to the delightful experiences, the really delightful experiences, what you have is, you have a threading of data that almost never comes from a single source. And so it's not just the surfacing of the data at the right time. That's kind of like an application level, like, like, you know, Amazon makes sure that you see the right thing at the right time too, you know, you're seeing shoes and not cars or whatever it is.
But all of that data has to come from someplace, and it's usually a myriad of places. And so when I think about it right now, given the challenges that I'm trying to solve within finance, I think about that. I think about portability from different sources, really, and how that bubbles up. You know, you can't lose sight of the fact that, like, it's very, very entrenched. But if you have delightful experiences.
Then you also have growth. You have a growth trajectory. And you know, it's just a great way to run a business.
Richard Walker: 04:04
So I struggle with the word portability because I'm not sure everybody understands what's meant by that. And I want to use an example. This happened to me yesterday to see what your thoughts are on this. So I flew on Delta Airlines yesterday. And I have the app and I have my boarding pass in my wallet.
My flight gets delayed. The app tells me my flight's delayed with a new boarding time. My wallet does not. So whenever I look at the wallet boarding pass, I'm confused because it has the old time on it and it didn't translate the data. Is that a portability problem?
Is that a connectivity problem? How would you view that? Because that's a bad experience, right?
Mark Ovaska: 04:41
That's a bad experience. Yeah, that's a portability. I mean it's most like a foundational sense. It's a portability problem. If you go to one vendor, you go to Amazon and then Amazon puts you over to a shipping company and your information is wrong there, or is blank and it has to be rekeyed. That's a portability problem, right?
So data has to live in multiple places at the same time. And right now I think about the experience of having to manually make data portable. So it's like in your head you've got to put it in this place and then okay, later on you've got to put it here, or you've got to translate the fact that your ticket in your wallet is different than the thing that's on your phone. And you've got to think about it like it takes these cognition cycles. We're used to that.
And unfortunately that makes it a lot harder to change it. If people were frustrated by that experience and thinking about it the way you're thinking about it, it's like, what? Why is this? Can this not be this way? Then I think innovation would actually go a little bit quicker.
Richard Walker: 05:45
So in wealth management, you may have a financial advisor asking clients to fill out an intake form to gather their data. They'll put that into a financial planning system. A proposal system. A hypothetical system. Later, they want to get that into a form.
They want to populate their CRM. And we don't know everything together. Right? It's not in harmony. So is that a problem you're solving?
Mark Ovaska: 06:09
Yeah I yeah that's a problem. But it is more so than just just Precept. It's a problem that has to be solved if you want to grow, standardization becomes more important. And I mean that in kind of the broadest sense. Right.
Like, like we can't every single car that's being manufactured can't be a different width outside of some constraint, right? Like, like the things that we make, every pipe that we get from the plumbing store can't be a different gauge, you know, just wildly different. Like consistency is essential for scale. And so that's not just true, I think fundamentally, but it's also sort of the starting point of how I started to think about these things is like, if you have standardization, then there's this other whole world of opportunity comes to you when it comes to portability and efficiency of like creating things that are more portable. And then you can get this scary part.
Then you can get a little bit higher and say, okay, now the objects that are portable, whether it's a form or whatever, if you've standardized it, then you can make the standardization portable, and that's when it gets kind of woo woo.
Richard Walker: 07:18
Okay, so this is why I'm a data company. First because I looked at the form and I said, hey, it's just displaying data, right? Your first name and your last name and your address etc. and all the forms ask for the same data: first name, last name, address, social, blah, blah, blah. So why don't we define all the forms with the same data fields? And so we did and we built the Quik! field definition, which today is over 1.2 million fields of defined capable repetitive fields.
Yeah. But you know and I know this problem really, really well, Mark, because at one point I had an integration to 35 different data sources to fill out forms. What we did is we essentially normalized how the data flows to the form. We had to build 35 different interfaces to those systems to get the data, which meant we had to do the work 35 times, and it took years and a ton of cost. This is where your product is really amazing.
So tell me how you solve that problem if you're trying to normalize the data into some other system? How are you solving it? I know, yeah.
Mark Ovaska: 08:22
No, I was super . I was blown away. I think we talked and you showed me that list a couple months ago. And I was just like, wow, he's done it. But the amount of work I immediately appreciated, the amount of work that went into that, you know, like just understanding it. And I want to make a comment kind of more broadly about technology being in closer proximity to the people who are experts about the data.
But to get back to your point. right away when you have all of these different standardizations across how many, how many of them do you have?
Richard Walker: 09:03
Well, we had 34 at the time. We don't support that many anymore. It's too much work.
Mark Ovaska: 09:07
It's not sustainable at scale.
Richard Walker: 09:10
Well, some of those companies no longer exist as well.
Mark Ovaska: 09:13
Yeah, yeah. Well that's it. So maintenance is a whole other topic. We could probably spend, spend an hour on. But no. So standardization once you've actually there's, there's two I think, I think there's two inflection points. To answer your question, I think the first is that AI makes things a lot easier.
AI is really pretty good at mapping things, right. And it has its fringes. There's points where you have to take something that they don't, they don't quite fit. Right. But by and large, AI is pretty good at saying, you know, this is first name and the other ones, you know, name underscore first or whatever.
It's good that way. And so I think the technological advantages that way have helped solve that. And we certainly lean on those. But I do think the standardization is a big piece of it. If, for example, you build those 35 integrations and this is like the other level of portability that I was talking about.
If you solve those 35 integrations, and then the result of you solving that can be plucked out of your platform and plugged into any other platform, then those people don't have to reinvent that wheel. And so I think I try to communicate it as, please don't reinvent the API wheel. You might have to reinvent. You might have to invent it once, but you should never have to invent it again.
Richard Walker: 10:28
Yeah. And that's actually what we did way back when we would take. So I had to throw away the database connectivity. It was data sources. But to us it was databases.
We call it the quick database connector, the Qdc. And if you know anybody's technical skills, they will remember ODBC and Microsoft Object or. Yeah object database connection. Anyway, we stole that. I'm really getting geeky here.
Sorry. We actually deployed it as a product, and one of our customers, fidelity, could take it, plug it into one of their applications, and now they could talk to act and goldmine and Excel and outlook and text files and CSV just without doing any extra work, because whatever data source it was, it transformed all the data to the same outcome. Therefore, they just map to that outcome, which is our field definition, of course. But what I don't want you to gloss over the impact AI is having here, because what you guys are doing is building interfaces to different APIs in like seconds, not months of work, but like minutes of work. How does that change the landscape in what you're doing and how you're helping people?
Mark Ovaska: 11:38
Then it makes new integrations a real time proposition. And so one of the things that I get asked, probably more than anything else, is how many integrations do you have or how many integrations can you make? And my response is always, you know, the number. But I try not to say the number because I think it's actually the wrong question. That's an old question that's like an old world question of how many integrations have you built?
Because it took a long time to build them, and it was like a mark of pride, of like, you know, I've got 100 integrations. It's taken me four years to get it, you know, or whatever it is like today. Integrations can be done in real time. And so whether or not you have the integration isn't the right question, the question that you ought to be asking, not you, but whoever the question that I wish I would get is, do you have the documents for this and are you ready to go? Because that's all you need.
You need the data model or the documentation or some artifact to feed into the AI engine and hit process.
Richard Walker: 12:38
So I have a bad analogy to this, but you just made me think of somebody saying, hey, how many pairs of shoes do you own when they should be asking, can you run right now? Yeah.
Mark Ovaska: 12:47
Yeah. Exactly right. Are you ready to go to the ball or train or whatever it is? Exactly.
Richard Walker: 12:52
I don't need to know exactly how many shoes you have, because you only wear three. Three out of the 30 pairs you own. Are you just ready to go? Yeah. You know, Mark, this is a really, really interesting time from my standpoint.
And I know from you and your partner's standpoint because you and I are both doing things. Your firm and my firm are both doing things with AI that I don't know very much about. Yeah, honestly, I don't know anybody else who is. And that is you've created the ability to build software so fast that it becomes negligible cost-wise and time-wise to do it. And that's what Precept is doing in my mind.
Like, you are building interfaces so fast that it's like, why does this have to be a roadblock anymore to how you want to do business? You just get to just turn it on, just use it. Maybe there's a little bit of configuration that goes into it, but just you're done. I don't think that's a fair assessment of what's going on.
Mark Ovaska: 13:45
No, it is a fair assessment. I was talking to shout out to Tony Leal. I was talking to Tony earlier this week and he and I were just kind of comparing notes on this exact thing, and you and I talked about it a little while ago. It changes the way you approach business. But it's not, you know, there's a human moment, right?
Like, it's not natural for me. Right? Like I'm 45. I've spent a majority of my career working in tech the old way. Right.
And so like, I'm, I still and this is the reason why we think about AI like, as agents. Because we have to, like, put it in a box that's kind of like a human. So we can conceptualize how powerful it is. And I think that's an intermediary stage. Like I think we're in a transition stage, and we have to do those things because we can't otherwise fully appreciate what's happening.
We don't know how to talk about it. Right. Still. And so yeah, we can do these things in real time. And we've, we've, you know, I don't know if we're at the bleeding edge, but we're certainly ahead.
And the way I think about it is starting to change because the economics around software is fundamentally changing. And to bring it back to the podcast and, you know, like the topic at hand is like, how do you bundle those things up to make a customer experience that's so delightful that people can't differentiate between, like, magic? It should be. It should come back to like magic, like when I was a kid and I was using software and I was like, this is magical. The first time I logged into the internet, you know, 1998 or whatever, it's like, this is magical.
We need to get back to that. I think we can.
Richard Walker: 15:29
So I'm going to give a real-world example that I have for myself, because I was just at this. I was at the Beacon Roundtable conference, and time zone-wise, I was up at 4: 30 in the morning. So I had some time and there was a project that I wanted to run, which was to take a website that had been designed by AI and turn it into a functional website built from scratch in different tech, a different tech stack. So it had like to call it the golden image of what I wanted built design-wise and what I wanted to actually make functional. And so I just ran this autonomous programming system.
Mark Ovaska: 16:03
Just for fun.
Richard Walker: 16:05
This is what I'm doing. Like, this is my side project because I'm interested in trying to figure out things. It's like tinkering on cars on the weekends. This is what I do, right?
Mark Ovaska: 16:13
I love it.
Richard Walker: 16:14
So I ran it. And, you know, later in the day, I'm sitting in this conference and my CTO is like, I wonder how long it actually took to run because it finished like it finished it all before I had to go to the conference two hours later. So I asked it to look at all the timestamps in the logs and tell me how long it ran. And it came back and said, hey, this project is estimated to take humans anywhere from 40 to 40 5 to 70 hours of work to do. The time frame in which it finished was 75 minutes.
Wow. That's 44.5. Times like 44.5 x multiplier.
Mark Ovaska: 16:49
Faster rate.
Richard Walker: 16:52
Right now, I'm not saying it was perfect. Bug free. Like done 100%. But this is what we're talking about.
Mark Ovaska: 16:59
You can do it 45 times to get it right and still be on par. It's crazy.
Richard Walker: 17:06
The game changer aspect of what's going on right now is companies like yours are finding ways to enable other companies to go ten times, 100 times faster than what they had to do so that they can deliver that better experience. And so, I mean, I just wanted to give that example as real world, because I've seen you guys doing this too.
Mark Ovaska: 17:25
Yeah. Yeah. It's good to have the conversation because I've gone from the same journey that a lot of people have, which is AI is not a thing or we talk about it and it was really just machine learning, right? Circa two years ago. You know, back when dinosaurs were alive.
And then AI hits the ground and you're like, okay. And I was sort of like a late adopter. It took me a couple months to even check it out after all the breakthroughs. And then it took me another year to rejoin my business partner Nate. And Warren Seymour.
And we started really looking at this and just blew away. And so it's like a roller coaster, you know, like you're like, oh well, now we can do this. But now what? And you've got to kind of climb back up the hill. And it's not a technological hill.
It's like a strategy hill. Right. Because you're like, well, what do you do with a strategy when the economics of building software are completely different? And if you want to build something, you can try it out right away. And do you build lots of things and then test them.
These are like these weird questions to be asking.
Richard Walker: 18:38
Yeah, I just spoke with a firm a few weeks ago, their CEO. I won't name the firm because I need to have him on the show. I'll let him tell the story, but he just spent the last two years using AI to rebuild an entire tech stack for his broker dealer and Ria firm. And he said, Rich, I'm ditching all vendors. Not us.
He likes us. He can't replace forms with AI. He's like, But I'm ditching all the vendors. I built a bespoke system that does exactly what I want. I didn't build it to sell to anybody else, he said.
It was just so cost effective to build it myself and own it, and it's so cost-effective to change it and maintain it now. Yeah. So it's a really fascinating time in software because there's opportunity for companies like ours. You're building tool sets and capabilities that make people go faster and what they want to accomplish for their overall goals. But it's also a challenge to the traditional software model, like where does this leave you if you build your own billing system, why do you need QuickBooks or whatever?
Mark Ovaska: 19:39
I think there's going to be a lot of disruption. I think this is me predicting the future. So I'm really bad at this, but here I go.
Richard Walker: 19:51
I think about the future.
Mark Ovaska: 19:54
We all talk about predicting the future. It's true. I think that's like a universally true statement. I think that software is going to be hyper-personalized. I think it's going to be the stuff that you consume.
It's not like everyone's going to go out and buy TurboTax to do their taxes. They're going to have Marco Vazquez, you know, specific tax software, and it's going to be for you. I was working on a fun story. I was working for a really large family office in New York, and I didn't know this. I was young and I hadn't really been exposed to this sort of thing.
And they had this story about how customized the view for this, for this wealthy person was across all of these different touch points. And then I had another friend who worked for a billionaire as an audio engineer. Long story. But he seconded that. We kind of compared notes and he's like, yeah, there's all the experience for this person at this level has been so shaped and crafted that it becomes like a bubble.
You know, it's like being the president. Don't you forget that there's stop signs, you know what I mean? Yeah. Like, yeah. You're just like in this, in this weird bubble.
And I wonder whether software being super cheap and super focused on individual people, that we'll get delightful customer experience and it'll go so far that we'll actually kind of have this more existential like issue of all of these tools being so personalized.
Richard Walker: 21:32
So I do think that personalization is going to be greatly enhanced. I actually my own prediction is that we're going to go to a website that's marketing something to us. It's going to recognize us in some manner. It's going to change its color scheme to my preferred color scheme. It's going to change the language to my preferred language and speaking tone and whatever.
And it's going to know my preferences so that it can market to me better than it does today. I think stuff like that's absolutely going to happen. But, you know, there's a realism side to this.
Mark Ovaska: 22:02
Sure.
Richard Walker: 22:03
It's really, really hard to accomplish this stuff. And it's like there's a lot of frameworks that have to be built. There's a lot of intermediary steps that have to be built, I believe, to make all of this really easy to do at scale, because you talk about like a billion or having their glass pane to look at their world. Yeah, it's affordable to them to have a highly customized system just for them. Right?
Sure.
Mark Ovaska: 22:26
Sure.
Richard Walker: 22:27
But somebody who's only got 100 grand and they're just signing up with an investment for the first time or whatever, how customized can it be for them? So to get there we have to have data. We have to have insights. We have to have intelligence, and we have to have systems that can adapt and morph to those types of capabilities. Sure.
And I don't think we're there yet. I think there's a lot of foundational layers that are going to get built to get us there first.
Mark Ovaska: 22:52
I agree with you. I, I think I, I think it's going to be faster than we think. I think it's going to be really fast. I think it's going to be surprisingly fast. We're so early on these things that you kind of have to have an engineering mind or computer science background or whatever to really kind of make these tools work.
I think that's about 12 months. This podcast will live for longer than that. So we'll go back and we can make fun of me. But like, you know, that'll be the fun part. But the tools aren't going to be geeky for very long because you'll be able to.
You'll essentially be able to create the tools to create the tools. And it's going to become more and more consumer. And the more and more consumers it gets, the cheaper it's going to be for people to say, I want a bespoke TurboTax just for me. And you get that kind of billionaire experience. I think it'll happen quickly.
I want to.
Richard Walker: 23:54
I want to offer something that I don't know if everybody gets here. The reason you can say that, Mark, is because you're building a fabric layer for others to leverage. You're building an interface layer that people can leverage and therefore go faster. Like you have that view that that is possible. So let me give you a different example.
Most people have seen Star Wars and most people have seen R2D2 stick out a probe, turn it into some key thing on some wall at the Death Star or some ship or whatever. Yeah, yeah yeah. Nobody asks how in the world can he do that? Right.
Mark Ovaska: 24:29
Yeah.
Richard Walker: 24:30
Nobody has assessed. Do they all run the same software? Do they all run the same hardware? Do they all run the same systems? Is R2d2 actually a part of the Death Star?
Like nobody knows this stuff. Yeah. So we're now with AI and what you're talking about with interfaces and AI helping you build interfaces really fast, we're starting to actually see clearly how R2d2 can do that. Like he's got some AI in there that's saying, let me read the system, understand it, and let me figure out how to talk to it. Right.
Mark Ovaska: 24:57
Yeah yeah. Yeah. So my answer is sort of like it's strongly informed by the fact that I spend every day thinking about how those other tools are going to be solving that problem. Right. But they all have to kind of come together.
Yeah. I see what you're saying.
Richard Walker: 25:13
It's true because it's a challenging thing. This is where I struggle. The wealth management industry has had decades to all come out with their own APIs to share data, to open accounts, etc. none of them have agreed with each other on how to do that. None of that.
Mark Ovaska: 25:30
It's crazy.
Richard Walker: 25:31
This is why. Quik! exist. We have built the de facto standard of data, of data standards across forms and therefore account opening systems. Right?
Mark Ovaska: 25:40
Yeah, yeah.
Richard Walker: 25:41
Think about banking in general. They've all agreed on what an ATM should do and how it should communicate. That's why you can go to Wells Fargo with your Bank of America account and make it work. Yeah, wealth management hasn't done that. And why?
Because it's exceptionally hard to get everybody to agree on the standard. So when we talk about AI building all this tech, I think the advancement AI is giving us is in some ways not to worry about the standards, because you can call 28 different APIs and standardize it and abstract from those APIs, and then standardize it on the back end or the output end. I mean, that's really the power of what you're doing. And I think what I'm doing is we're taking all the different data needs and putting them into one standard as we go, so that people can actually build other systems on top of them and go faster.
Mark Ovaska: 26:30
I have a slightly different take on that. I agree with you, but maybe this is too cynical. You can tell me, but my take on that is philosophical, and it's that when something is really difficult to achieve or deeply personal, humans really like to think that it's of differentiation, right? Like, if I make my own shirt by hand, I'm going to wear that shirt. Or at least I'll be very strongly inclined to walk around and be like, this makes me unique, right?
Yeah. But it doesn't. It's a shirt just like everybody else. And so I think when it comes to integration and stuff that you're talking about, people spend a lot of time on these things and they're hard to do. And so they label it with a differentiator.
I've got to differentiate because I've connected these two platforms or whatever else it is. When you're forced to recalibrate that because of the power of the technology, you have to come back and ask a really crucial and hard to answer question, which is what is your differentiator if those things aren't? And then people are. So my all of that to say, yes, there are a thousand different ways in wealthtech to see things and so on and so forth. People are grasping at those differences and resisting standardization.
Not always, of course, but resisting standardization because they see it as their unique value proposition. And what I'm really suggesting with Precept, at a kind of a philosophical level is that if you standardize and you embrace standardization, it will release you from the the obligation of thinking that just because it's hard, it makes you special and you can go find something that really, truly is special, that will attract your customers and delight them.
Richard Walker: 28:38
Yeah, I really like that concept. It goes along with me thinking that I should work 12 hour days, 80 hour weeks for 12 years. And that's what was going to build my company because it wasn't that wasn't exactly it was hard. They must be successful. No I wasn't.
Mark Ovaska: 28:55
Yeah yeah yeah, yeah. I don't remember who it was. Somebody somebody famous Bezos or somebody was. You don't get bonus points for working too hard. Like.
Like if you can do something really simple, you know, set up the coffee shop across the street from Starbucks because, you know, it's a good location. Like, just do that. Like.
Richard Walker: 29:15
Yeah. I had a question about this that I wanted to really ask you. And it, you know, it's along the lines of how you make money with this. And so as I was kind of contemplating like, what? How do people pay for what you're doing, etc., I think it always has to come back to what is the value they're getting.
And the value that they're getting is speed, efficiency, flexibility, real time flexibility, etc.. So the real question I was driving at in my head as I was kind of streaming through this, was everybody in this industry is so fascinated with agentic workflows for their advisors. Save time for the advisor, do work for the advisor, think for the advisor, etc., right?
Mark Ovaska: 29:59
Yeah.
Richard Walker: 30:00
But isn't the data the footprint? Isn't that the underpinnings to make this possible? So are you guys fueling or making possible agentic workflows? I do see that as what you're possibly enabling.
Mark Ovaska: 30:14
Yes, the short answer is yes. And the longer answer is AI is built on data, and data has to have that connectivity and portability that we've already talked about. The standardization is important for the future, but the ability to make new integrations in real time using AI is bridging the gap between what exists today. You know, you go to I'm going to pick on Pershing, which is really unfair, but you go to Pershing or some of these large organizations that have a hard time, you know, keeping up with things, and they don't have the latest API. They're not going to be able to, you know, swivel on a dime and give you what you need.
And so you've got to meet them where they are, right? They have value just right where they are. And so. The technology is going to have this. I'm really into intermediary states.
But transition phases. Are you going to have this transition phase where you've where the tech exists? But the primary purpose of the tech actually isn't the new thing. The primary purpose of the tech is to bridge the gap between what people are doing and don't understand what the new thing is. And some lighter version of the new thing.
Yeah, and that's a transitional phase.
Richard Walker: 31:33
So okay, I'm actually excited to say this because a lot of people don't understand. They think business, the exciting business, are the ones at the user edge. They're changing the experience. And I love that stuff I do, right. Like the workflow concept is so cool, the note takers are so cool, etc..
Mark Ovaska: 31:51
Yeah.
Richard Walker: 31:52
People don't realize the value, and I think the elegance of a utility system that can make things actually work under the, under the, under the seems like to me forms and data flowing through a form into a new account opening system. And that ability to process is the same thing as the plumbing in the concrete of the foundation of your house.
Mark Ovaska: 32:13
Yeah, you don't worry about it unless it leaks. Yeah, yeah, I know right I know.
Richard Walker: 32:19
And that's why I like your company. Because you're doing something really, really similar with building foundations like plumbing that people can rely upon and use and go faster with it.
Mark Ovaska: 32:30
It's a good observation. I'm not sure. It's definitely not the easiest thing in the world to do, but it's born out of that attempt, right. Like, like the precursor to Precept and all the the founders came out of AI labs, which was the same sort of mission in terms of single pane of glass for advisors across, you know, any number of different platforms to solve the swivel tail problem. Right?
You've got 23 platforms that you have to deal with, all the stats everybody is familiar with. But that's really good because you can say, here's the single pane of glass and here's what it looks like. That's actually pretty easy and sexy to sell. You start talking about the data pipelines underneath and it gets a lot harder to sell. But foundationally, what you know, because of your business, those things are really critical.
And so coming from that experience and saying is our ability to focus and move faster on these foundational infrastructure problems, a net gain over being distracted with building the whole stack just so it's sexier to sell. That was one of the early questions that I had just in terms of product strategy, right? Obviously, the answer that I concluded was there's a lot of work to do just to do the standardization and AI mapping and all of that. And so we really ought to focus on that and find partners who understand that they're the pane of glass, not us, and then help them.
Richard Walker: 34:00
Yeah. And this is why a lot of utility systems or companies are hard to find. It's a hard sell. It's hard to find the right person who understands it, wants it, implements it, uses it, sees all the value for it. I mean, look, that's the nature of having a cool idea and starting a business, but Man.
We have to switch gears because I'm running out of time and I want to get to my last question. But before I get there, what is the best way for people to find and connect with you?
Mark Ovaska: 34:29
Yeah, the best way is to reach out its mark. My first name Mark@precept.sh, find me on LinkedIn, and so on. The website is the same domain and the contact form. I got a copy of it, so I'll get back to you right away.
Richard Walker: 34:48
Awesome. All right, switching gears, I love this question. Who has had the biggest impact on your leadership style and how you approach your role today?
Mark Ovaska: 34:59
I'm glad you gave me this as a prep, because I wanted to think about this and be thoughtful. The first day I walked into AI labs, I met Lori Hardwick and she's stuck with me through the years. And so, you know, shout out to Lori Lori's. Lori's a huge influence on me, the way I thought about things. And I worked with her very closely for a few months and yeah, yeah, a huge impact.
So glad to be able to bring her up.
Richard Walker: 35:25
She's had a lot of great impacts on our industry with people. I've heard her story many times. It's awesome. Yeah.
Mark Ovaska: 35:31
Yeah, yeah, I'm one of many I'm sure.
Richard Walker: 35:34
But it's great. I mean, we need that, right? We need people who care, who want to see success and want to see new things come to light. And as you know, I mean, it's hard to navigate that. It's dude, I did everything wrong in my business for decades.
It's.
Mark Ovaska: 35:50
Yeah. Well, I think anybody who's been through this a few times, right? Like like you just you win because you lose like, like winning is just taking the loss and moving on, learning the hard way, you know?
Richard Walker: 36:06
Yeah. Look, I think failure is spelled quit quit. Yeah I don't think so.
Mark Ovaska: 36:12
Yeah, exactly.
Richard Walker: 36:13
And keeping going is actually failure. I think that's just learning. Yeah. It's just getting back up and trying again. All right.
That's the difference.
Mark Ovaska: 36:20
Between. Sorry. I was just gonna say that's the difference between people who are successful and people who aren't. Because you're absolutely right. That's exactly it.
Yeah. Yeah.
Richard Walker: 36:27
No. Totally. Totally. All right. Cool.
So I want to give a huge thank you to Mark Ovaska, CEO and co-founder of Precept, for being on this episode of The Customer Wins. Go check out Mark's website at precept.sh. It'll be in the show notes. And don't forget to check out Quik! at quickforms.com, where we make processing forms easier. I hope you enjoyed this discussion.
We'll click the like button, share this with someone and subscribe to our channels for future episodes of The Customer Wins. Mark, thank you so much for joining me today.
Mark Ovaska: 36:55
Thank you Rich. So happy to be here. Talk to you soon.
Outro: 36:59
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