Industries at the Intersection: FinTech & InsurTech

Nicholas Brathwaite: Hi everyone. This is the TechSurge Deep Tech Podcast presented by Celesta Capital. In each episode, we spotlight issues and voices at the intersection of emerging technologies, company building, and venture investment. I'm Nick Brathwaite, founder and manager partner at Celesta Capital.

This episode of the series, we look at an industry at the intersection. Most industries today are technology enabled, but some businesses have been late in adopting the level of technology necessary to enable transformation. With this series, we're looking at the future of major industries involved in technology driven transformation.

Today, we're going to examine technology's impact at the enterprise level within the financial services and insurance industries, and to help guide us through this topic. We joined by Christian Mitchell, Chief Digital and Information Officer for Northwestern Mutual. I'm sure that most listeners know Northwestern Mutual.

NM is a Fortune 100 company that provides life insurance and financial planning and investment services. They are a leader in the industry in almost every metric. In his role, Christian leads the company's technology and digital strategy with oversight on its AI capabilities, data engineering, technology infrastructure, and cybersecurity.

Hello, Christian, and thank you for being here. It's great to be here. Thanks for having me. All right, let's jump straight in. Many people still think of the insurance and financial planning world as one that is largely analog. Built on legacy tech stacks using, you know, what we in this tech world would call antiquated equipment and, you know, driven by manual work from agents and advisors, is this perception still real?

Christian Mitchell: Things have changed dramatically over the last few years and the perceptions that you just laid out, I might address some of them individually. First off, we do have a lot of legacy technology and we are addressing that legacy technology. The interesting aspect though, is. Often there are aspects of that legacy technology that work really well and are relatively cheap and durable.

And so as we transition those pieces of technology, as we refactor them for the cloud, we're being very thoughtful and making sure that we get the right ROI because a lot of the technical infrastructure, albeit somewhat aged works very well. That said, we have many instances and areas across the company where we're adopting the best patterns that you would see at any, uh, FinTech, any tech forward company.

Whether that's what we've done around the advisor experience, but notably around the client experience. So a few years ago, we built from the ground up a financial planning tool that really brings to bear not only the best thought behind the CFP process, but really invites the client into this in depth, Iterative discussion with, with the advisor where they can look at various scenarios and have a very clear conversation about something that can be very complex and opaque for the client.

And we were able to push that software to a point that in 2022, we actually won a FinTech breakthrough award, uh, for the best insure tech solution. So we're in interesting position where we do have legacy, but we have some shining examples of areas where we are really digitally transforming the enterprise.

Nicholas Brathwaite: So why don't you tell us some of the things that NM is doing, leveraging technology to transform this business? 

Christian Mitchell: That is a great question. There's so many different areas that I could highlight around how technology is transforming the business. I'll, I'll pick just a few out of many examples that come to mind.

Uh, perhaps I will, Uh, first address the word that's on everyone's lips these days, which is AI. And we can talk about generative AI a little bit later, but I'll start by talking about a more classical AI implementation. So as a life insurance company, is that that's a big part of our go to market. We have to underwrite individuals.

So assess their health, price their product, and then issue that business. Historically, that underwriting process could be pretty laborious. It could take weeks and weeks. We might even send someone to your house. To collect some fluids or put some, some sensors on your chest and even take an EKG. What we've been able to do over the last five years is.

Leverage AI ML and actually the data that we were already collecting to come up with a whole suite of models that allow us to do that underwriting much more readily. So something that may have taken weeks in the past, now we can do in a week or even two or three days, which enhances the client experience.

It saves money. And most importantly, we're getting Protection for our clients, uh, very, very quickly. So that's, that's an example that, that brings to bear more traditional aspects of, uh, of our business in terms of the digital transformation. Another area of digital transformation, which again, given some recent events is certainly on people's minds is cybersecurity.

In the past, we thought about cybersecurity as perhaps just being an expense item, something that just was hygiene, that there was some group in the company that was taking care of and senior management didn't really need to pay a lot of attention to it. That has changed substantially where we have senior management that is actively engaged in the cybersecurity process.

We're bringing in experts from the outside to make sure that we are, that we are, Protecting this incredible sacred stewardship that we have over our client data. We're doing regular tabletop exercises, scenario analysis to make sure that we are, uh, keeping a good eye on all of the incredibly valuable assets that we have.

So those are, those are two examples. Uh, but it's, uh, suffice it to say digital transformation is the rallying cry for the enterprise right now. 

Nicholas Brathwaite: This is great. And, you know, I know we're going to talk a little bit more about cybersecurity and AI and so later on, but I want to use this. As a segue to talk about startups in the space, because, you know, I remember reading not too long ago that there were somewhere between 25 and 30, 000 FinTech startups, um, around the world.

So it's become a very crowded space and a lot of money are being poured into the FinTech startup space, but there doesn't seem to be a lot of companies that have gained meaningful traction. From your perspective. What does this whole FinTech startup environment look like? 

Christian Mitchell: Yeah. So I might take us back to what I call FinTech 1.0. So 2013, 14, 15, where we saw that initial round of companies. So the betterments learn vests, personal capitals of the world emerge. And the thesis there is they were going to be able to build this incredible direct to consumer B2C business that you could essentially disintermediate the human. through an incredible client experience.

It sounded interesting. It attracted a lot of VC money. It ultimately ended up not working. Money is so personal. It's so intimate that people want to connect with other people. Now the technology can be an incredible Accelerant to that and put all of this incredible, you know, context around it, ease that, uh, communication, allow a client and advisor to visualize all kinds of aspects around someone's personal financial situation.

But there has to be a human in there somewhere. So that, that kind of fintech 1. 0, to the extent, to the extent those companies stayed on that direct to consumer bend. Largely didn't work out. There's still some of them out there trying to figure out what they're going to do, but that first batch really moved more towards a B to B stance.

So ethos in the life space. I think they've done a really nice job pivoting to more to a B to B advisor driven stance. We watched all of that. We, we participated in it. We were a investor in betterment and then investor and eventually acquired a learn best. So we could have a front row seat to what these fintechs were doing the innovation that they were embracing.

But through all of this, we really arrived at the conviction that we hold today that it's the human plus digital. The human is so important for that end of the conversation, but we can surround that human to human interaction with such a rich digital experience, whether it's web, mobile, visualization, scenario analysis, as we move forward, perhaps even the metaverse.

Uh, but we are, our deep conviction is that human to human connection is going to stay core to our business. 

Nicholas Brathwaite: As you mentioned, NL has participated in investment and an acquisition of some fintech companies. Can you share how NM is leveraging those startups as part of his business strategy today? 

Christian Mitchell: So, as I mentioned, with FinTech 1.0, we made some of these initial bootstrapped investments. Those initial investments turned into an organized, comprehensive, VC efforts. So NM Future Ventures, where we go out and we partner with the best teams, the best startups, the best VC firms to invest in FinTechs that we think are really interesting.

And we are targeting FinTechs where we think there could be some possibility of a strategic tie up with Northwestern Mutual at some point. Maybe it's a partnership, maybe it could be an acquisition at some point. But as we see FinTechs come up with new services, new ways of reaching a client, new ways of applying AI and ML in a FinTech setting, we're going out and investing in those companies. And so it allows us to, at the same time, run our core business, but see what's going on on in the outside and incorporate those learnings as we see fit. 

Nicholas Brathwaite: And I want to talk to you a little bit about deep tech and startups. So clearly the conversation we've been having so far around what people are traditional, you say FinTech 1.0, the traditional FinTech type startup, but we're deep tech investors. And when I think about I think of it more broadly in terms of, you know, it is, it is a technology response to some of the inadequacies in the financial services industry. But underlying these application level responses are technologies, core technologies that enable, um, what it is your industry is trying to do.

So can you, can you share a little bit about how a company like NM can leverage some of these deep tech type startups to help either accelerate business transformation or Whether or not you have interest in things such as how these companies can bend the mortality rate, which can have the auto mortality curve, I should say, which can have a significant impact on your business.

Christian Mitchell: So deep tech that could take us in so many different directions. As it, as it pertains to deep tech topics like cloud and infrastructure in general, we're going to be partnering with the large incumbent providers out there, just given the highly regulated nature of our business and the, and the relationships we have with our clients.

But as we go into, uh, AI in particular, this is a place where we're much more actively embracing some of the deep tech solutions that are, are out there. Sure. Um, more in the experimental phase. So we have a, uh, innovation lab that sits outside the four walls of Northwestern mutual, where we're talking to a lot of these startups playing around with some things and some safe sandboxes.

I'm certainly, uh, out presenting and rubbing shoulders at lots of different tech conferences. So we stay abreast of what's going on. Uh, so when the time is right, when we can bring those, those innovations within our environment and they've proven themselves to adhere to the cybersecurity data privacy regulations that are core to who we are, we're ready to.

Nicholas Brathwaite: So NM has been around for a long time. So you have this, this huge old, not in a, in a demeaning way, but this huge old company How does a company such as NM develop the scaffolding necessary to evaluate and implement new technologies? 

Christian Mitchell: So when I, when I, it's a great question. And when I think about that, where my mind goes is to why we exist, what is the actual purpose and goal and mission of Northwestern Mutual?

We've expressed it many different ways, uh, through the years, but at the end of the day, we exist to make people better off financially. Our clients are in legal form, actually owners of the company. And so as we think about these various technologies, what we want to adopt, where we want to place our bets, where we want to invest our money, it is with this in mind, are we going to be able to do things that make our clients better off financially?

And so that puts us, I think it puts us in a unique position, um, particularly As we think about AI and ML, um, we have this incredible treasure trove of client data, but we're going to use that data to make our clients better off financially. We're not going to try and monetize that. We're not going to try and sell that data off to anyone.

We can, we can use that in this closed loop system to enhance the value of the enterprise. 

Nicholas Brathwaite: So, you know, we, in the deep tech or in the technology industry in general, I used to things moving very quickly. And when companies are dealing, especially startups and others are dealing with a company like NM. You know, they're not usually accustomed to dealing with companies that are as thoughtful in the way they make decisions because you make decisions based on the long term ramifications to the business.

I am very impressed with NM in the sense that when NM and a client. Sign an agreement and then believe that this is a commitment that you've made to this client, that you will be there for them 40, 50, 60 years from now when they need you most. So when you come from an industry like ours, that is very transactional and everything is based on the next 12 to 18 months, this can sometimes be difficult.

But in addition to that, you have you know, regulatory and other issues that you have to deal with that leads to some of the conservative nature of the industry in general. So in this new role that you have as chief digital and information officer, what are some of the challenges associated with bringing a company like this into what some people would call the digital age, but at the same time, staying nimbly enough to continue to keep evolving?

Christian Mitchell: Yeah, it's a, it's a great question. I can take it in many directions. I might actually address the talent dimension of that question, because I think it's a really interesting aspect. So we, we can be a little bit slow moving at times. We do have to bear this regulation and scrutiny and regulators coming in and examining our systems.

And that can at times slow us down a little bit. So how do we go out and find the best talent? How do we really get those engineers that we need to work on the most cutting edge aspects of the company? The formula that I've landed on and has been really successful is to find the technical individuals that can also buy into the mission of it.

They want to hone their craft, but they want to hone their craft in a place where their work means something, that it actually is helping families. Retire and send their kids to college and all of that, you can go out and you can hire incredible people. I can, I can find people at Google or Facebook or acorns or, or wherever, if I can identify those people that our mission is going to resonate with and that, and at the end of the day, that's having the right people on the bus, having the right talent.

And the right structure, that's 90 percent of the battle. Once we get that taken care of, uh, then I have supreme confidence in our ability to face so many of the other technical challenges in the environment. 

Nicholas Brathwaite: So let's double click on this people conversation for a little bit in your role, as you take on this transformation.

But as the entire company transformation, but as, as you lead these efforts, you are now trying to hire people that a few years ago, and then would never be thinking of hiring and know you're competing against Alphabet and Apple and others like that for the same talent. So how do you, as a company make it attractive?

For these young, you know, data scientists and others to come to NM and tell us a little bit about the kind of successes you're having. 

Christian Mitchell: So, as I mentioned, we definitely sell them on the mission of the organization. We also sell them on the incredible financial stability of the company. We've been around for 170 years through ups and downs as, uh, markets gyrate.

This can be a really safe place for people to Build their careers and learn and grow. Whereas some of the people they went to school with may be sitting on the beach somewhere without a job. So we, we can definitely sell that aspect, but more and more, what I've been leaning into is meeting those talented individuals where they are.

So I make the circuit of all of the. The various tech conferences and presentations, not only as an attendee, but getting up on stage and telling our story and relating anecdotes about how we're using gen AI and how some of the, the greenfield build that we're doing really does adopt the latest patterns out there to get them interested.

We have our innovation lab. That's another place where we can at times attract really interesting talent where they can come in and work on these incredible problems. That the enterprise needs to solve, but they can do that in an environment where they're completely unfettered. They can try anything they want.

That's that can be another really powerful venue. And we talked earlier about our venture portfolio. That's another place as we're talking to startups, as we're in the startup community, where we get connected with really, really interesting technical people. And at times we can sell them on the mission and the ethos of Northwestern Mutual and get them to join the team.

Nicholas Brathwaite: Whatever you're doing seems to be working. So tell us a little bit more about that. The innovation lab and how, how that's managed. 

Christian Mitchell: So I set up the innovation lab a number of years ago, and the idea was that it would be strategically aligned, but operationally separate. So they don't have access to any NM data.

They don't use NM systems. We gave them a small budget and they. Can basically buy whatever they want, buy their own laptops, set up their own infrastructure. But the first part of what I said, that strategic alignment, that's really the most critical part is that we're engaging with that innovation lab on a regular basis.

We're bringing business leaders from the core business, and they're talking to these innovation leaders and saying, https: otter. ai These are the problems I need to solve. If you could do this, it would be amazing. If we could issue business in this way. If I could talk to my clients in this way, that would be absolutely incredible.

And then we just set the innovation lab folks free to go off and do that. And then they return and talk to. Those core business leaders, they write briefs, they show the results of various experiments, focus groups, maybe they stood up a dummy company or did some Facebook marketing or something. And we can take those insights that we gleaned very, very cheaply and in a safe way and incorporate them back into the core of the business.

Nicholas Brathwaite: Cool. So we started talking with AI and I want to Drilling into that a little bit more, you know, those of us in the technology space and those of us, especially in the VC space have been aware of AI for a long time and investing in AI for quite a while. In fact, our own company, soul and AI startup to one of the big semiconductor companies several years ago that became the foundation of their AI initiative.

But certainly AI is now on everybody's lips and adoption has started to explode and probably for most people is going much faster than, than they might've thought. And there are many, many potential applications for AI, even though today, most of the applications are around training, but that, that is going to change as inference is gonna later on become the big deal.

But in the insurance business or financial services business in general, there are lots of potential applications. Thanks. One of the biggest challenges I imagine for executives like yourself is trying to determine where to focus and where to prioritize. So can you give us a little bit about how you are going about making these decisions for NM as to where to focus, what to prioritize?

Should you put your focus more on improving customer experience or optimizing operations or all of those combined? And then walk us through how NM and maybe the industry in general. is thinking of, of leveraging AI and where you see the early adoption happening. 

Christian Mitchell: I, yeah, I think about our AI agenda across two horizons.

So the first horizon, and that's the horizon that we're in right now, it's really about enterprise efficiency and cost savings. So whether it's co pilots that all of my engineers are using or knowledge management, Using rags to make our service agents or advisors more powerful in front of clients.

That's really where we are right now. The tools are amazing. Are they fundamentally transforming the business right now? No, but they are very useful because in addition to driving bottom line savings, we are getting the enterprise more accustomed to these tools. They're using them. They can, they're starting to see the power.

What we want to use, do is use the momentum from horizon one to catapult us to horizon two. And this is where we start using generative AI as the means of fundamentally transforming the business, thinking about our value proposition in a, in a fundamentally different way. And a lot of this is happening more in the innovation lab at this point, but as we're prioritizing those things, we're really looking at strategic alignment.

We're looking at whether There has been something proven in the innovation lab, and one of the beauties of Gen AI is the models are so powerful and readily applicable that you can set up experiments really quickly and see if there's a there there. So we want, we want to see through the experimentation that there is some kind of there there.

The third thing that we is a big determinant of the prioritization, which may come as a surprise to folks is not technical. It's actually business sponsorship. So I have a lot of really smart technical people in my function that are going out there and cooking up really cool ideas. If we don't have a business partner who's going to take that and pound the table and get the funding for it, I don't want to put technical resources behind it.

And so we're really a big part of the prioritization process is making sure that we have that strong partnership and sponsorship from the business. 

Nicholas Brathwaite: So, you know, there are a lot of people who, who say today that You know, AI is more or less a cost center for most companies today, and there are not a lot of benefits being seen from it, but you have a bunch of engineers playing around and doing things.

And maybe like I mentioned earlier, people are focused more on, on training type opportunities versus, you know, revenue generating. But if you focus on business optimization, you would assume that you will see the benefits of AI very quickly. Have you already started to see concrete examples of how significant the operating benefits of AI can be?

Christian Mitchell: We do. We see some early indications of that. So, in the coding co pilots, we're seeing 5 10 percent productivity gains and really, really high levels of code commits, code acceptance. In the servicing organization, Where we, we have a servicing GPT as we call it, maybe a little bit of context on this one, because it's kind of interesting, you mentioned earlier, we have these decades long relationships with our clients.

So at any given time, someone might be calling into the call center to ask a question about a policy that we issued. 50 years ago. Now, our client service representatives are not going to have that information at their fingertips. They're going to have to go and look in some file folder or microfiche or something to actually provide an answer for the client.

What we've done is we've taken all of that knowledge base. Uh, and done a RAG implementation using GPT 4. 0, where instead of going to that file cabinet, now the service representative just actively chats with servicing GPT and gets an answer immediately. And the right answer, the client doesn't have to call back.

So we're seeing the power of that tool in terms of call duration, first call resolution, etc. It's really driving a lot of efficiency, but more importantly, a better experience for the folks that are calling in. 

Nicholas Brathwaite: One of the concerns people have around using AI, of course, has to do with, you know, privacy and bias, and there are a lot of issues around this.

What are some of the biggest challenges that you see in this industry that you have to be very cautious around in terms of making sure you have the right guidelines and guardrails around AI implementation?

Christian Mitchell: So there are two dimensions of this that come to mind immediately. So first off, as Technical people are presented with a really exciting new technology.

There's always the temptation to circumvent controls. Let's go fast. We need to do this. And so the first part of my answer to your question is we have to maintain discipline around the protocols that are already in place. And enforce that discipline. The second dimension, and this is what I think is more interesting is this technology is so new.

It's such undiscovered territory in many regards that we're not going to have the perfect regulation or the perfect guardrail or the perfect oversight mechanism for every use case. And so I'm firmly of the belief that the moment that we're in right now, what we need to drive is this really rich cross disciplinary dialogue.

Around these issues, data, privacy, the use of AI model, governance, et cetera. So internally at Northwestern mutual, we have an AI council. It represents business people, technical fields, legal, and we throwing these big media issues at this AI council for them to take a position on it. Now, some of those positions may end up and turning into regulation that or process that we put in place in the organization, but where we are right now, we really think it's the dialogue.

It's. Really, really smart people debating this out. That's where we need to be. And that's going to help us get to the right answer. 

Nicholas Brathwaite: So as an early adopter, which I believe NM is in terms of some of the applications that you've mentioned, how do you deal with the fact that, and you partly answered the question, but how do you deal with the issue of the fact that in all AI implementations, you have a human in the loop. And you have humans in the loop at the front end with the programming, and so you have to worry about bias. And then you have humans in the loop at the back end, which has to do with interpretation of recommendations and others. Are there specific things that you're doing to try to address that?

Um, in addition to the council. that you mentioned. 

Christian Mitchell: Yeah. So on the front end, as it pertains to, to bias and discrimination, we just relentlessly interrogate our datasets to stamp it out. Uh, we have internal teams to look at that. We use external service providers, law firms, et cetera. It's an issue that we just take so, so seriously.

And so the front end, I feel like we have that very, very well covered. The back end is more interesting where we are right now. We want a human in the loop. To act as a gut check on what the AI may, may spit out, uh, they're amazing that, you know, I, I use, uh, AI in my personal life all the time. And most of the time it's astounding.

Sometimes it's horrifying what comes out of the models. And so we want that human in the loop to gut check. But I, one of the things that we are so focused on going forward as we implement various, um, AI apps into production is the human change management aspect of it. I actually think that our ability.

To upskill our, our folks, help them actually understand what a good prompt is, how, how they actually understand and interpret the output of the AI. That's actually going to be our limiting factor in terms of innovation. It's not the power of the model itself. And so we are so focused on upskilling our, our folks, internal programs, external programs, because that, that human competency, the savviness of our employees around AI is so, so critical for us to really unlock the full power of the technology.

Nicholas Brathwaite: Yeah, I, my personal view on AI is that it is probably the most important technological tool, uh, certainly of our lifetime, maybe ever. And for industries like yours, where you sit on so much data and you're trying to make data driven decisions, the AI, which I think of more as augmented intelligence, the fact that, you know, have a tool where you can leverage all of this massive data that you have.

To give you greater insights so you could make better decisions about your clients and your businesses. And so has to be exciting for a company like NM. 

Christian Mitchell: It's so exciting. It's going to open up just incredible ways for us to interact with our clients. There's also some risk and threat in this as well, we are going to have to deal with.

And my personal feeling is Gen AI is going to put us in a position where we have to fundamentally reimagine certain aspects of our value proposition. 

Nicholas Brathwaite: So staying on the data side, but Changing from AI to other technological decisions that you have to make, things like how do you manage your data on prem, in the cloud, hybrid, what are your thoughts around cloud, on prem, hybrid?

How are you going around architecting your network and your services to take maximum advantage of the tools that are available to you? 

Christian Mitchell: Yeah, it's, it's a good question 'cause we're right in the midst of really grappling with this. So we have a very, very large cloud footprint. And so particularly for new applications that we've, we've built over the last few years, they're all built in the cloud, cloud native, uh, bene, we're reaping all of the benefits of those applications being in the cloud.

But as we mentioned, as I mentioned earlier in, in our conversation, we have a tremendous amount of legacy tax. In the data center mainframe and we are in the midst of having this really rich conversation around how do we take that and cost effectively move it to the cloud. We can't just like take it and dump it in the cloud.

It would be incredibly expensive. We wouldn't get the benefits of moving that to the cloud. So we're in the process of very, very carefully looking at everything in our environment. and determining when we should migrate, how we refactor before we migrate to get the most out of the cloud. And at the same time, you know, all roads lead back to AI.

Uh, there are some interesting developments out there, companies spinning up some programs that can't, AIs that can look at very, very old code. Code bases and assist in that translation, that transformation that's necessary to reap the benefits of the cloud. So we're on the journey. It's, it's, uh, it's going to take us a while, but we really want to be thoughtful about it, both from a cost perspective and again, cognizant of the fact that we hold our clients money.

This is really, really important stuff. And we've got to make these moves in a way where we always have an eye towards a stability, accessibility, cybersecurity, et cetera. 

Nicholas Brathwaite: And in this business. Where, of course, Privacy and security is, is a big issue, but in addition to that, there is a general approach by many companies where everybody wants to benefit from other people's data, but they don't want anybody to benefit from their own data.

What are your thoughts around your own private enterprise LLMs versus using the common LLMs that are available today? 

Christian Mitchell: Yeah, it's so. I actually think the way we think about data is going to lend us such an incredible competitive advantage going forward. We have so much data, but as I mentioned earlier, we're only going to use that data to benefit our clients.

We're not going to sell it. And so we believe that as, as consumers become more savvy around data, they are going to be more and more willing to do business with companies that. Take care of that data stewardship, uh, in a very, very responsible way. So we think that's, this is essentially our game to win as it pertains to the LLMs right now, my viewpoint is the foundational models are so powerful and they are so expensive to build it. Does not make sense for us to, uh, embark upon the creation of our own LLMs. That said, we're constantly playing around with things as the new open source models, uh, come out, we're downloading the weights and, and, uh, looking at those with this eye towards at some point. Perhaps we train our own LLM or perhaps we want to run more on prem as opposed to other implementations to safeguard the data, but it's again something that we're going to walk into thoughtfully through time.

Nicholas Brathwaite: So my last question is around cyber. How are you handling the risks associated with cyber actors?

Christian Mitchell: As you well know, cyber has been one of the most meaningful areas of emphasis for senior management over the years. The last three years now, not to suggest that that alone is going to close address all cyber security issues, but it's such an interesting barometer of a company's stance towards cyber security as I could speak for the CEO and my colleagues on the team.

We view cybersecurity as our collective responsibility. This is not something that we are just going to delegate to a CISO or a CIO and periodically get a report on. We are regularly engaging in topics as there are cybersecurity incidents across the industry. We're discussing them. We are doing tabletop exercises.

We're having deep conversations with the board. All in the effort to, uh, make sure that we are as well equipped as possible at all levels of the company from the board to the senior leadership team to business leaders all the way down to, uh, the fingers on keyboards that are creating our applications such that we have the best security stance as possible.

Nicholas Brathwaite: Well, thank you very much. Appreciate you taking the time to participate in our podcast here. And you've always been a friend of the firm and thank you very much. And we look forward to continuing to talk with you and work with you on a number of areas. 

Christian Mitchell: Likewise. Thank you for the rich dialogue. 

Nicholas Brathwaite: Thank you.

Well, thank you all for tuning in to Tech Search Podcast from Celesta Capital. If you enjoyed this episode, feel free to share it, subscribe, or leave us a review on your favorite podcast platform. We'll be back every two weeks with more insights and discussions of all things Deep Tech. Bye for now.