Resilience: Creating a new Category

On this episode of The Founder's Sandbox, our host, Brenda McCabe speaks with Rushabh Mehta- CEO and Founder of MatchbookAI. They speak about Resilience: creating a new category by filling unmet needs with a new Data category. Rushabh shares his point of view on the primary challenges faced today in large enterprises, including latency, high costs associated with how data is used across enterprises and the control of the data.  

You can find out more about MatchbookAI or contact Rushabh at:

[email protected]

https://campain.matchbookai.com

 

 

Transcript:

00:04
We're standing on the edge of something big. We're gonna make some changes. Welcome back to the Founders Sandbox. I am Brenda McCabe. I own and operate a consulting firm, NextAct Advisors, where I have a simple mission. I want to assist entrepreneurs

00:32
and entrepreneurs in building scalable, well-governed and resilient businesses. The Founders Sandbox, the podcast you're listening today, is an additional channel to feature founders, business owners, corporate directors, and professional service providers who like me, want to use the power of the private enterprise, small, medium, and large to create a change for a better world.

01:01
through storytelling with my guests that include topics around how they built their companies. We're gonna touch on topics like resilience, purpose-driven enterprises, and sustainable growth. And my goal with my guests is to provide a fun sandbox environment where we can equip one startup founder at a time to build a better world through great corporate governance. Today, I'm absolutely delighted. My guest is

01:29
CEO and founder of Matchbook, Rishabh Mehta. Thank you Rishabh for joining me today. Thank you, Brenda, for having me on this podcast today. I'm really excited to talk to you. We're gonna talk about data and truth, right? Yes. So Rishabh and I, we're gonna talk about resilience. The company that he founded back in, wow, 2018, and even before that out of his garage.

01:59
is truly creating a new category. And it's a great story, I think, to support the resilience theme that I often work with founders on. Roshabh is passionate about data quality and the power of data. So passionate that he started over five years ago, Matchbook AI, as a solo founder. Along the journey, and as I actually got to work with him, the pain of bad data

02:28
was actually a journey that he started over 20, 25 years ago and working at Raymond James, as well as with some government entities on really identifying the provenance of data, where it's coming from, and the truth and accuracy. So I wrote in a blog, some time ago, it's one of my evergreen posts around purpose-driven organizations and

02:57
John McKay, who at the time was whole field CEO, had written a piece on the trust-based organization. And he called out four types of business models that are today pretty prevalent as entities. He called out the models of the good business models, the true business models, the beautiful business models, the heroic and the true are

03:25
business models that are discovery and furthering human knowledge. In these sites, examples like Google, Intel, Genentech, and Wikipedia that express this higher aspiration as a trust-based organization. So they're true. So I think today, what we're going to talk about, Rishabh, is how matchbook AI and you bring truth to the space of data. So how did I begin?

03:55
So I have a long history with data as you well mentioned. I have been a passionate data guy since in the 90s and data has always interested me, attracted me and it's attracted me because of the potential that data has for organizations and through my journey over the last

04:24
25 plus years in working with data and in working with various organizations how I've seen how data can transform businesses and transform lives of others as well. Whether it's increasing efficiency in businesses, whether it's affording better decisions or whether it's lowering risk as well. So there is a lot of potential to data and that is what really attracted me to

04:54
to data and everything that I have been doing along this journey. So this to me, matchbook AI is really this natural progression of all the challenges that I have seen in that 25 year career with data and the challenges people have with data and how do we make that better? And so matchbook AI really started with the seed of we need to solve certain problems

05:23
in access to data, in access to the right data, the right information that can solve these business challenges and solve it more effectively and faster. So this is really the seed of Matchbook AI. It started as you mentioned in my garage. It was something that I was passionate about, wanted to learn more, wanted to see how we could solve and provide a solution to the space.

05:52
where there was nothing and solution was greatly lacking. And so that seed of providing access to data and the right data started back in your garage. The market you're addressing is actually quite larger than that of access. I think you're bringing me the

06:20
the data access to a different level. Can you speak to us about, you know, what is the market? And specifically, what are those pain points that you've identified on that journey, right? There was a seed, but then you've evolved it. Absolutely. So it's definitely any journey is about evolution. It's about looking at what is the future, right? And how do you impact the future? So it's not only looking at

06:48
what problems can I solve today that's in the marketplace but how do I solve a problem that's sustainable in the future that makes future life easier as well. So in terms of the problems we are solving, it's as I mentioned, the primary problem is access to data, access to the right information for decision making and this

07:16
just that problem comes with a number of challenges along the way. It's about the latency of access to data because latency can have a fairly adverse impact on business decisions on this data. So it's about solving for that latency. It's about solving for that very high cost that companies incur when it comes to bringing in the right set of information and getting it actionable.

07:45
and useful for business decisions. It's about taking decisions away from a few within the organization and into the hands of many. So these are all of the challenges that we are looking to solve with Matchbook AI. Wow. So you've talked about latency and the high cost of bringing in the data. And what I like the most is kind of the democratization, right?

08:15
Absolutely. It's about democratization and it's about giving people that have the ownership of this data, the power to make the decisions and the power to work with that data. And how you do that, how the platform at Matchbook.ai does that? Can you walk my listeners through instead of a top down, MDM traditional, bottom ups and the

08:44
different use cases across large enterprises. Absolutely. So if you think about a large enterprise, there are so many decisions at so many levels within an enterprise that need to happen on data. Each of these decisions have different inputs to them, have different data needs. So...

09:07
What I've seen in the industry today is when it comes to a lot of data being brought into the enterprise for decision making, there is almost this one size fits all model of here's the data and do what you may and as a result today what happens is when you need to make a certain level of decision you are going out and finding other ways to access data and you're really not only increasing the cost of data

09:35
within the organization, you're also increasing the cost of access to that data. And after that, a lot of this data is coming in and staying siloed within business decisions and not used across the enterprise. So if I, if I think about a simple use case of sales and marketing, and, and I just talk about the various types of data you may need at different points within that cycle of sales and marketing.

10:05
If you look at prospecting, you are wanting to bring in some basic information so you can figure out which accounts you want to go after. If you think about once you figure out which accounts you want to go after, you want to do market segmentation of those accounts so that you can either give it to the right salesperson within the enterprise to go after that account or you can classify it in a way.

10:33
that you have the right sales motion against that enterprise. Now those classifications could be based on the size of this account. It could be based on the revenues. It could be based on the industry that a particular account is in. And there's just this multitude of factors that go into that particular decision of market segmentation on who and how we are going to target that particular account.

11:00
Then as you go further down into the sales cycle, you start having to make other decisions like maybe we are ready to contract and sell to this organization. So now it becomes a question of, if you are an organization that needs to give certain credits, how much do you give? And now this takes a complete different set of inputs because this could be based on

11:28
simple thing like a credit score. It could be based on a multitude of things of not only the credit score but also having a deeper understanding of payment histories and other things about this organization that you're about to work with or the customer and being able to take that into account into that decision-making process of what can we sell them and how much credit can we give them and

11:58
And then as you get even further into your legal and contracting, legal and contracting may have its own set of needs for data. And these again are very different from everything else that you've brought in about these entities. They may need to know, for example, can we do business with this enterprise? Is this a verified company? And so they may need a completely different set of information that they need to bring in into this decision-making process. So if you think about just sales and marketing,

12:28
There's so many different decision points that happen within sales and marketing. And each of these decision points require different inputs and different data. So imagine today's world when all of this data is so completely siloed access to this data siloed, each division is taking its own unique path of deciding what data they need, where they source it from and how they get it. And the incremental cost of the human.

12:58
capital or the human cost of thinking in the right information so that they can make decisions. And then if you take into consideration latency, by the time you make these decisions, by the time you have all the data you need and you make these decisions, the data that you made that decision on could already have been outdated. So latency can play a major factor into that.

13:28
say give a certain credit limit to a company and you've used that credit score from two months back and suddenly the company started having financial stress or something happened, there's a bankruptcy warning or something like that on the company, you don't have that information to rectify your decision in real time and so there's a lot of cost to the organization and so this is just one example.

13:57
of where there's so much data need and there's so many inefficient processes today to get this data. And then lastly, even with all these processes, you still have to contend with the latency of the information you're using for your decision-making. And this is precisely what we're trying to solve. Yeah, so how is it that this new data category, what have you architected that addresses latency?

14:27
the high cost of ingesting data of multiple sources into an enterprise and decision-making across multiple organizations. How are you doing it differently and creating truly what you say is a new category? Right, absolutely. So, an interesting thing, I've been mulling over this for a while. Okay.

14:54
when we've been talking about the fact that we are creating a new category. Yep. To me, a new category is really about solving an age old problem and legacy processes in a completely different manner, rethinking something from the ground up in a way that it can not only impact the status quo, but it can, it can change how these things are done in the future.

15:24
So to me, that is what is creating a new category. It's, you rarely ever create a new category where you're doing something so completely different that it's never been done before. There are absolutely examples of that. If you look at Amazon and what they did to just the whole idea of being able to buy and sell products online. That is a completely new category.

15:52
where you've defined a completely new market. In our case, it's about taking age-old processes, it's taking legacy, it's looking at all the problems in that legacy pipeline, and then coming up with a solution on how we fix that. So to me, that's a new category. So the way we've looked at designing this is we've really looked at the problem head on. We've looked at the mechanics of the problem. We've looked at...

16:22
what is it underneath the scenes that is happening that is not only causing these issues with the latency and all that but it's also about what are the components that are required to solve this problem in a meaningful way and we actually started with looking at the people on the front lines. We are looking at the solutions, the business processes on the front lines to look at

16:52
how do we solve for that? And through that, how do we solve for this across the enterprise? And so by focusing on that, by focusing on not only looking at this sequentially, okay, these are the challenges that accessing your data, accessing the right data that you need for decision-making to looking at how do you then...

17:19
taking the next set of challenges within the enterprise and how do you solve for that and the next set of challenges and at the end of the day, it's all about how do you make this information more actionable. So we've gone through these stages even within our own platform to look at the most fundamental challenges, solve for that and then start solving for what are the next steps that an enterprise needs to take.

17:48
in order to get to that utopian point, which is an informed decision. And that's essentially how we architected Matchbook from the ground up to then solve for these problems. So we've really looked closely at what our customers do, what are not only their initial pain points, but what are the next steps in their journey and the next steps in their journey and keep addressing those.

18:18
till we get to that point of actionable data, actionable decision, and how do we solve all of that? And then of course, looking at, you know, if it was an ideal world, if the customer had everything they needed to make those informed decisions, what else would they need? What else would they be asking for? And really looking into that future, trying to look at that hourglass and say,

18:47
Okay, if you had everything you needed to make an informed decision today, you obviously need to then be able to have predictive capabilities of these decisions. You need to have good insights into the decisions you made and make sure that you have that constant refinement on how you can keep making better decisions into the future. And this is really what drives us and what drives Matchbook as a solution to solve for these.

19:17
very cold needs of enterprises. You know, it's a great, first of all, thank you. The predictive capabilities in the era we live now with Shadjibiti and the AI and matchbook AI. It's very exciting what we can do with data. The segue, I think you said it very well, by addressing an age old legacy album with the new solution from ground up, but you went to

19:47
the frontline workers when building this. You've also, who are your customers? You're very customer-centered company. Can you talk about some of the biggest lessons that you have learned from current customers that actually surprised you and maybe informed the future architecture of Matchbook AI? Can you tell us a story?

20:15
Let's see, that's an interesting question. So.

20:21
I remember the first customer we went to. We had essentially built the code plumbing of solving this basic challenge that we really thought because at that point, that was the challenge we had looked at, at the fundamental level of connecting for the system and access to external data. And we had built that out. And when we went to the customer,

20:51
They said that this is great. This is very useful for what we need and what we are doing today. But here's some other things we need to be able to do. And the one thing that stood out to me through all that was we don't just have one system integrating with third party data. We actually have multiple systems that need multiple data sets. And so when I initially started this,

21:20
I hadn't given it as much thought because I was really at that most fundamental level, that most fundamental building block of this platform. And so that really opened my eyes to the fact that we need to account for the fact that an enterprise is very unique. And even within the enterprise, there are so many colors. It is not

21:48
simple, it is not the same. Every system, every access point within the enterprise has different needs, has different types of data, has different data quality. And that really opened my eyes to the fact that a good solution cannot just address a single channel of integration, it needs to address an enterprise need.

22:17
which is very different, which can have many colors to it. And then how do we make this accessible? How do we make it easy for them? So that was a big learning for me. It was a big eye-opener for me to realize this is really what enterprises was all about and working with enterprises. And so that allowed me to go back, look at the solution.

22:47
look at how do we make something that we had initially built with a purpose in mind and build it for that higher purpose, how do we morph that? Some of the other learnings we've had from customers is again just within the same vein, there is so many differing needs within the enterprise that you cannot just assume that it's a one size fits all. So you just cannot

23:17
go into a platform or go into an enterprise with a one size fits all solution, that does not work. Also importantly, when you think about trust in data, and at the end of the day, this is what we are trying to do is we are trying to achieve trust in data. And trust also means transparency and it means control. So the fact that

23:46
people want to know where the data is coming from, how that data is coming in, and what sort of controls they have in place on that data was very important. I have seen other solutions fail because of lack of transparency, because they're simply black box solutions. And businesses, even though they use it because it solves a need, they're always uncomfortable because it does not allow them to have that trust.

24:16
And so for us, building trust into our solution was just as important as building a solution that met enterprise needs. Fantastic.

24:30
You're now into the fifth, sixth year of a company and you scaled from what five employees up to how many today? We have between I think full-time, part-time contractors and everything we have close to 82. 82. Now. Always been a distributed company. You've survived through the pandemic. You've been growing at a hundred percent.

25:00
year. Can you speak to the resiliency and bootstrapping this company as you've built? You know, 82 person strong, 32 customers, fortune 1000 customers. So how has bootstrapping and the resiliency? What is in your toolkit? So resiliency is interesting. Resiliency is

25:30
Going through a hundred no's before you get to a yes. Resiliency is about the trust that other people put in you and upholding that trust, whether that's a customer that's come along this journey with us, whether it's employees that are willing to take the risk of working with a startup.

25:59
whether it's our investors that are willing to back us up and believe in me, believe in my vision and that truly is humbling to me. So resiliency is about making sure that we can uphold that trust that these people have put in us and into the organization and as I said, it's going

26:28
It's not an easy path by any means but it is definitely made easier by the trust that people put in you. Thank you.

26:41
And can you share with my listeners what it is you look for when you're discovering talent and you're building and scaling the company's culture? You're at that really, really critical point where you're going down if you're C-suite and bench strength. So what's critical when you're seeking talent to maintain the company culture?

27:10
Absolutely. So when I seek talent, I am truly looking for aptitude. It's less about the experience, it's more about the aptitude to learn because I have built teams before and I know that the most successful people

27:37
are not necessarily the ones with the most experience in a particular area, but the ones with the aptitude and the drive to want to succeed. So I place that above all when I'm looking for people to hire. When I'm looking to hire leaders within the organization, I'm also looking at how well they can build teams

28:06
they can lead and lead by example. I think that's very important to be able to build teams and build leadership that can lead by example as well. Thank you. I always like to allow time for my guests to share with my audience how to reach out to you and or your company. Can you speak a bit how to get in contact with Matchbook AI? Absolutely.

28:36
So anyone can go to our website, which is matchbookai.com. They can reach me at rmeta at matchbookai.com. Would love to talk to you, hear your thoughts about data, your challenges with data, or even just have a conversation around when you think about trusted data, what do you think about?

29:06
I love it.

29:09
So I have the honor to work with my guests on something that's very near and dear to the work I do with my clients and next act advisors. I work on themes or topics of resiliency, on purpose-driven organizations and sustainable growth. So I always like to finish the podcast asking my guests, what's the meeting to resilience? I think you've already shared

29:38
your definition, but what would be your, what does purpose-driven enterprise mean to you? To me, a purpose-driven enterprise is about changing the lives of others for the better, whether it's changing the enterprise for the better, whether it's changing the lives of people working within there for the better with better decisions.

30:08
It's about doing something that can have a positive impact into the future. Thank you. And from a CEO that's bootstrapped the company, what's sustainable growth mean to you? It's interesting. If you think about sustainable growth, that it could mean making sure that you're not spending more than you make.

30:38
That's not always the case with startups as we well know it. To me sustainable growth is about taking those measured steps and then really it's about building a company that has an impact way into the future and not just an impact into the now. To me that's a sustainable growth. Excellent. Last question.

31:08
Did you have fun in the sandbox today, Ruchav? Absolutely. Absolute fun. This was a nice way to spend a Monday morning, especially a Memorial Day. That's right. That's what we do. That's how we rock and roll in the startup world. Well, I wanna thank you for making this podcast possible because in full disclosure, I have been with you along the journey

31:38
for many years now and like to think that some of my good craft is at work at Matchbook AI. It truly is an honor working for you and really seeing how you bring resilience, creating a new category to the marketplace. So thank you again, Ruchat. And for my listeners, thank you for...

32:06
downloading the podcast, the Founder Sandbox, drops monthly and look forward to talking to you next month. Thank you. Thank you, Vlenta. Thank you so much.