Latest / Code Story | Startup Podcast for CTOs, CEOs and Technical Founders / S12 E25: From Processing $500B at Intuit to Building the Fraud-Proof Ledger: Eradicating Financial Misstatement with Ahikam Kaufman, Co-Founder & CEO of Safebooks AI

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S12 E25: From Processing $500B at Intuit to Building the Fraud-Proof Ledger: Eradicating Financial Misstatement with Ahikam Kaufman, Co-Founder & CEO of Safebooks AI · Code Story | Startup Podcast for CTOs, CEOs and Technical Founders

• 0:01
  [SPEAKER_01]: One of the things that stroke me, if you think about it, when I walked in finance in a few public companies I had to just walk in four public companies, minus always have the notion of clothes.
• 0:12
  [SPEAKER_01]: But so several weeks, every quarter, everyone is heads down, closing the books.
• 0:18
  [SPEAKER_01]: You can take vacations, it's almost like you'll nail to your chair, so to speak, and see on the processes that basically requires you to validate the data, document what you're doing.
• 0:29
  [SPEAKER_01]: create a set of analytics, all kind of repetitive work, which AI can immensely help to solve, and I just thought it could be different.
• 0:39
  [SPEAKER_01]: My name is Aikam Kastman, I'm the co-founder and CEO of Saved Mix AI.
• 0:47
  [SPEAKER_03]: This is Code Story.
• 0:49
  [SPEAKER_03]: a podcast bringing you interviews with tech visionaries.
• 0:52
  [SPEAKER_03]: Six, six months moonlighting goes.
• 0:53
  [SPEAKER_03]: It's the last and all of the backgrounds who share what it takes to change an industry.
• 0:58
  [SPEAKER_00]: I don't exactly know what to do.
• 1:00
  [SPEAKER_03]: It doesn't go as to get right.
• 1:02
  [SPEAKER_02]: who built the teams that have their bad company is its team's help each other, which is proud of our team.
• 1:09
  [SPEAKER_03]: Keeping scalability top of mind, all that infrastructure was up there.
• 1:13
  [SPEAKER_03]: Yes, we've been fighting it as we grew up.
• 1:14
  [SPEAKER_03]: Total waste of time.
• 1:15
  [SPEAKER_03]: The stories you don't read in the headlines.
• 1:18
  [SPEAKER_02]: It's not an easy thing to achieve.
• 1:19
  [SPEAKER_03]: To get yourself a deficit of off, try to begin to ride the ups and downs of the start-up line.
• 1:26
  [SPEAKER_02]: Need to really want it.
• 1:27
  [SPEAKER_03]: Not just about technology.
• 1:29
  [SPEAKER_03]: All this and more on code story.
• 1:32
  [SPEAKER_03]: I'm your host, snow lab partner.
• 1:34
  [SPEAKER_03]: In today, I hate calm Kaufman, as built the agintic platform for finance operations, turning your manual processes into agintic automation.
• 1:49
  [SPEAKER_03]: Ajikam Kaufman spent most of his career in the Bay Area.
• 1:53
  [SPEAKER_03]: After becoming a CPA, he started his career as CFO to a startup company.
• 1:57
  [SPEAKER_03]: Over time, he's been given multiple opportunities to not only serve finance, but serve business roles as well, which prepared him for his own entrepreneurial path.
• 2:06
  [SPEAKER_03]: IE starting three companies and exiting one to intuit.
• 2:10
  [SPEAKER_03]: Outside of tech, he enjoys traveling the world spending time with his family and hiking, but he notes that the demands of being a business owner limits the amount of time he spends in these things.
• 2:21
  [SPEAKER_03]: I he come started to think about how automation can positively impact financial operations, specifically around managing data in the Office of the CFO.
• 2:31
  [SPEAKER_03]: After the first AI models were released, he got excited, realizing that these models would continue to get better and better, alongside operating with agency.
• 2:43
  [SPEAKER_03]: This is the creation story of safe books.
• 2:51
  [SPEAKER_01]: As a co-founder of Czech, we built a mobile payment system which was sold to it.
• 2:55
  [SPEAKER_01]: And then when I got into it, I started working very closely also with the finance team of into it.
• 3:02
  [SPEAKER_01]: I got more and more exposed to some of the challenges, finance people have, when they have to deal with a lot of data.
• 3:10
  [SPEAKER_01]: Let alone filter companies,
• 3:15
  [SPEAKER_01]: and learn how do you get your arms around the compliance and regulatory requirement while you help to run a business which is very complex from a Danish standpoint.
• 3:24
  [SPEAKER_01]: And I started to think about like, automation can help in that obviously, during that time, we used to talk about AI, but it was more like machine learning.
• 3:34
  [SPEAKER_01]: And after the first model came out towards the end of 2022, I thought that it's going to get even better.
• 3:42
  [SPEAKER_01]: I always say at least in my humble opinions that where we are now is like we're looking at the fold model T and we're really excited about the car but cars gonna get much better in a couple of wheels down the road so we we think it's like super innovative but I think AI is gonna look even better every few months that goes by so we think we're excited but we're gonna be even more excited as things move along.
• 4:08
  [SPEAKER_01]: And when the first model came out, I realized that we have now a way and we have the technology to be able to solve some of the complex problems around how do you manage data in the office of the CFO.
• 4:21
  [SPEAKER_01]: One of the things that stroke me, if you think about it, when I walked in finance in a few public companies I had to just to walk in full public companies, minus always have the notion of clothes.
• 4:32
  [SPEAKER_01]: But so several weeks, every quarter,
• 4:37
  [SPEAKER_01]: It can take the case and it's almost like you'll nail to your chair, so to speak, and filmed in processes that basically requires you to validate the data, document what you're doing, create a set of analytics, all kinds of repetitive work, which AI can immensely help to solve, and I just thought it could be different.
• 5:00
  [SPEAKER_01]: What we've done or we save folks we developed an agenda platform that can totally emulate most of the work done by your human being, replacing repetitive, specific work around the structure and structure data.
• 5:16
  [SPEAKER_01]: Well, it comes more complex than I think I even create an advantage is when you need to look at that data across systems, we finance every transaction could be processed by multiple systems.
• 5:27
  [SPEAKER_01]: For example, a revenue transaction, then you have a coding system, you have a CRM, you have supporting documentation whether it's like a contract or the form.
• 5:36
  [SPEAKER_01]: You have the billing system, you have a new peer of banks, in a peer, put it to page the same, and then you have a peer on your RHS and payroll and so on.
• 5:45
  [SPEAKER_01]: The ability to reconcile the data, look at the data, and make sure everything is okay across systems, that really becomes herian complex.
• 5:55
  [SPEAKER_01]: and their agents can do a much better, fast or job than even human beings.
• 6:00
  [SPEAKER_01]: Why, and I think most human beings, I know, would appreciate the fact that they can focus on what they do really best, making decisions, making accounting decisions, as opposed to counting the beings so to speak and checking the data.
• 6:16
  [SPEAKER_01]: I think we just got to a place, or I think we got to a place where the technology can actually execute on that with quality because the other concern, the top concern, trying as people would have, is okay, I get that it can do the world, but what's the level of accuracy, what's the level of integrity, how much confidence can I, or trust I can have in the system.
• 6:37
  [SPEAKER_01]: three years after being into this AI journey, all of us the entire world, or the most advanced models, I think we are the place where the technology meets or exceeds some capabilities of the human being to be able to do that work and I think it's exciting because specifically for the office of the CFO, most specifically for finance,
• 7:01
  [SPEAKER_01]: With all those who are like following up on that or interested, there is like a massive exodus accident from the profession.
• 7:08
  [SPEAKER_01]: Much less people are going to learn finance today.
• 7:11
  [SPEAKER_01]: So, you have less supply of people, while I think the world is basing tremendous business opportunity and expansion, and finance is a critical function.
• 7:22
  [SPEAKER_01]: though how do you do more with less?
• 7:24
  [SPEAKER_01]: I think you do it using technology.
• 7:26
  [SPEAKER_01]: So I thought it's a tremendous opportunity to augment manual work with automation while the technology allowed that automation to be sufficient for the requirements in the office of the CFO industry some of these capabilities in terms of accuracy
• 7:43
  [SPEAKER_01]: and quality are only, I've only been available over the last, I would say even six to eight months, so we got to a level where it's sufficient, then I do have to admit I do see in 2022, I see a different level of openness and acceptance towards AI and the office of the civil, so that's all story.
• 8:07
  [SPEAKER_03]: I'd like to dive into the MVP for safe books for the first version of the product you built and got out there how long to take the build and what sort of tools we're using to bring it to life.
• 8:18
  [SPEAKER_01]: When we started a company, one of the decisions we've made is that we really care about the quality of the data we ingest.
• 8:26
  [SPEAKER_01]: So we spent a lot of time almost two years building the first what we call is financial data context graph.
• 8:34
  [SPEAKER_01]: which means, because of the unique needs and unique set of walks that Finals is doing, walking with AI, we have to provide AI the right infrastructure and foundation to make sure it doesn't elucinate and it doesn't make any errors.
• 8:50
  [SPEAKER_01]: And in order to do that, we use proprietary graph technology that basically links on the data across systems.
• 8:58
  [SPEAKER_01]: As if it was a single system.
• 9:00
  [SPEAKER_01]: So our message to our prospects and customers that in order to enjoy the benefit of AI don't need to replace a new field systems, any of you will text that.
• 9:09
  [SPEAKER_01]: We come on top, but
• 9:11
  [SPEAKER_01]: When we come and talk, we'll not only augmenting what you have today, we actually ingest the data in a way that links the data across the systems by that providing AI with the full context around the data.
• 9:24
  [SPEAKER_01]: So if you think about that, we take the transaction from your, we take your code, we link it to the data in the CRM, we link it to the data that comes from the auto form of the contract in your document manager system.
• 9:37
  [SPEAKER_01]: We link it to the relevant billing transactions in your billing system and we link it to the relevant journal entries in your ERP.
• 9:44
  [SPEAKER_01]: And by doing that, when the AI then looks on the data, it can't go wrong, it can't elucinate.
• 9:51
  [SPEAKER_01]: We spent a lot of time building that infrastructure as opposed to just build a product.
• 9:56
  [SPEAKER_01]: And once we build that infrastructure robust infrastructure, the AI came to a point where all the user experience
• 10:03
  [SPEAKER_01]: I would say foundation and capabilities would be prompted.
• 10:08
  [SPEAKER_01]: So you don't need to build like a very structured kind of one-trick pony product.
• 10:14
  [SPEAKER_01]: Once we create this data foundation in this unique data infrastructure, then when we come to create the use cases on top of the platform, we can actually prompt them, we can prompt this board, we can prompt world flows.
• 10:26
  [SPEAKER_01]: As opposed to people starting with the product experience, which is then how to code it, and it's a one-trick pony, and when you want to be flexible, and serve other use cases, you have to build each of them separately.
• 10:37
  [SPEAKER_01]: So, we actually focused on the data foundation, data quality, which is, I see, something that even the lab companies, which, of course, they're doing an amazing job, but that specific needs that finance have.
• 10:50
  [SPEAKER_01]: to be able to look at on the data cross systems in order to generate the right integrity and accuracy is something that we have as a differentiator.
• 11:00
  [SPEAKER_01]: And that gives us the unique mode to be able to meet the expectations of finance people around accuracy and integrity.
• 11:09
  [SPEAKER_03]: So you've got that, that MVP where you're focusing on the data, you're paying attention to how you're building the foundation for what you're building.
• 11:16
  [SPEAKER_03]: How did you progress and mature it from that point?
• 11:19
  [SPEAKER_03]: And I think to wrap that in a box a little bit, what I'm looking for is, how did you go about building your roadmap?
• 11:24
  [SPEAKER_03]: How do you decide that, okay, this is the next most important thing to build, or to address with saybooks?
• 11:31
  [SPEAKER_01]: First of all, from the get-go, we started to work with customers and walk on real data.
• 11:36
  [SPEAKER_01]: In order to be able, so you can build that product in a lab, you have to build it with real data.
• 11:41
  [SPEAKER_01]: Once we did that, and we started to create point solutions for customer.
• 11:46
  [SPEAKER_01]: then you start to hear the same tune over and over again.
• 11:50
  [SPEAKER_01]: And a very prominent part of that tune was our closed process is taking too long, taking too much time, why it's taking too much time because we have these 200 world papers we need to generate.
• 12:03
  [SPEAKER_01]: We have all these processes that we need to complete.
• 12:07
  [SPEAKER_01]: And you start to hear that over and over again.
• 12:09
  [SPEAKER_01]: And then you start to realize that we can actually focus the solution on you start to see consistency across companies.
• 12:17
  [SPEAKER_01]: We started from two blocks so you could do different things.
• 12:20
  [SPEAKER_01]: But then we realized that being able to focus on close acceleration and automation,
• 12:26
  [SPEAKER_01]: would be some things that would help to that could be easily replicated and could help many companies.
• 12:34
  [SPEAKER_01]: And then I say close automation.
• 12:35
  [SPEAKER_01]: I don't mean on how you manage the clothes.
• 12:37
  [SPEAKER_01]: It's actually how you execute the clothes.
• 12:40
  [SPEAKER_01]: You are a few very good.
• 12:41
  [SPEAKER_01]: close management solutions today, which are like basically platforms that help you run the process, but in order to actually execute the war, then you'd still meet people and that's what we can mean.
• 12:53
  [SPEAKER_01]: But when we started to hear the same tune over and over again and then you go to the other projects you ask him, hey, do you have this problem in the respond positive news and you know what to focus?
• 13:05
  [SPEAKER_03]: I'm curious about team.
• 13:07
  [SPEAKER_03]: How do you go about building your team?
• 13:08
  [SPEAKER_03]: How did you go about building your team?
• 13:09
  [SPEAKER_03]: And what do you look for in those people to indicate that they are the winning horses to join you?
• 13:15
  [SPEAKER_01]: When I started first of all, I was fortunate enough to walk with some engineers that I was walking in the past, so I was fortunate enough to walk with people that I walked with before and that's always great.
• 13:28
  [SPEAKER_01]: While I was looking for our people who are really experts in data, so because from the get go we realise.
• 13:35
  [SPEAKER_01]: It's not about accounting when you try to build a company like that we'll try to solve that problems the technology is so you need data people.
• 13:42
  [SPEAKER_01]: Obviously data people don't know much about accounting or data engineers, but that's like the federal expertise you need to be able to build a foundation of that.
• 13:51
  [SPEAKER_01]: On top of that, I think mostly over the last year you'll starting to look for additional people or sometimes you have to replace.
• 14:00
  [SPEAKER_01]: and look for people who are like an AI first people.
• 14:04
  [SPEAKER_01]: The people who appreciate the advantage that AI brings to the table you want to be like an AI company AI first company.
• 14:11
  [SPEAKER_01]: You can just talk about AI without.
• 14:14
  [SPEAKER_01]: without actually implementing it so you want to have people who can manage agents and be open to that and obviously that's something that we'd started to implement over the last year but would say initially data people and then for we go to market teams and use people from the industry who thought to the office of the cheerful that's always helped they know the language, they know the terminology that's always easy and that's it.
• 14:45
  [SPEAKER_03]: I'm curious about scalability.
• 14:46
  [SPEAKER_03]: I'm curious about how you look at scalability from the beginning.
• 14:50
  [SPEAKER_03]: And also, I'm interested if there's been interesting areas where you've had to fight scalabilities you've grown.
• 14:57
  [SPEAKER_01]: When I thought about folding most felt statement, what kind of organization, what size of organization can we serve?
• 15:04
  [SPEAKER_01]: From a data volume standpoint and processing power.
• 15:09
  [SPEAKER_01]: In that respect, I think we're able to build a robust foundation infrastructure that allowed us to handle scale of data.
• 15:19
  [SPEAKER_01]: And that's actually, that was a friction point because a lot of companies where they say, can you handle escape?
• 15:25
  [SPEAKER_01]: Obviously, we do have like our area of focus in terms of the ideal customer profile.
• 15:31
  [SPEAKER_01]: But within that profile, I think, first and foremost, I wanted to make sure we can deal with the data scale.
• 15:38
  [SPEAKER_01]: And I'd like to think that the other thing was how to scale the operations, the delivery to the customer and all of that.
• 15:46
  [SPEAKER_01]: And I think over the last year, a gave us a lot of capabilities in how we can deploy things faster and also...
• 15:54
  [SPEAKER_01]: scaled the business from a delivery and customer's success standpoint.
• 15:58
  [SPEAKER_01]: A lot of the things that we used to do manually and would take a week to configure a use case are now taking, I don't know, minutes or hours, depends on the thing.
• 16:08
  [SPEAKER_01]: So AI being an AI first company's and using your own dope food actually help us scale and solve the operations in the delivery.
• 16:18
  [SPEAKER_03]: Okay, as you step out on the balcony, and you look across all that you've built, thus far with safe books, what do you most proud of?
• 16:26
  [SPEAKER_01]: At the end of the day, it's all about the people.
• 16:29
  [SPEAKER_01]: I think even today, when we interact with a poster door customer, we want to first inform us getting the trust and confidence.
• 16:36
  [SPEAKER_01]: We want to gain the trust and confidence at the not wasting the time, which is the most I think valuable resource even before money.
• 16:44
  [SPEAKER_01]: And we want to make the game the confidence that we can deliver because one of the things people have to realize and is sent to an enterprise is that.
• 16:54
  [SPEAKER_01]: When you talk to your prospect, eventually what happens, he has to go swing and turn a processor.
• 16:59
  [SPEAKER_01]: He sells the same message to his colleagues and peers, which have to sign up for like, their time commitment, whether it's like a D, whether like he sees managers, right he's going to tell them hey, in three months or in two months.
• 17:15
  [SPEAKER_01]: We're going to be able to do this so that, stored in the closed time, increased the accuracy, avoid leakage, so on solfels.
• 17:23
  [SPEAKER_01]: So being able to gain that trust is first and foremost, driven by the quality of the team you're at.
• 17:31
  [SPEAKER_01]: that deliver these messages and deliver on the message.
• 17:36
  [SPEAKER_01]: To me, that's the big thing, it's the team.
• 17:38
  [SPEAKER_01]: It's all about people.
• 17:40
  [SPEAKER_01]: With some of the things we're doing, especially in the office of the CFO, you can build a company which is like, has to co-founders or whatever.
• 17:47
  [SPEAKER_01]: You have to build a team, you have to build a customer success.
• 17:51
  [SPEAKER_01]: Customers service, you have to be available sometimes.
• 17:55
  [SPEAKER_01]: The functions we serve have extreme time-sensitivities around clothes, audio, the diligence, and stuff like that.
• 18:02
  [SPEAKER_01]: You have to be there for them.
• 18:03
  [SPEAKER_01]: And it's all about the team.
• 18:07
  [SPEAKER_03]: Let's flip the script a little bit, tell me about a mistake you made and how you and your team responded to it.
• 18:13
  [SPEAKER_01]: One of the examples we'll have been in, let's say, again, because the AI had a lot of influence around that, but sometimes the delivery times will not.
• 18:25
  [SPEAKER_01]: The times we communicated promised, let's say, a year ago.
• 18:29
  [SPEAKER_01]: And that scene is very important customer of being able to deliver on your time commitment.
• 18:35
  [SPEAKER_01]: Now with AI, because every scene, technology is so powerful, we can actually exceed these timelines.
• 18:42
  [SPEAKER_01]: In the past, we used to be aggressive.
• 18:45
  [SPEAKER_01]: We thought we could solve the problem one way and then you have to pivot or linger both sometimes the customer has constraints.
• 18:52
  [SPEAKER_01]: But at the end of the day, you don't need the time now.
• 18:54
  [SPEAKER_01]: So I think that's one thing that we became super sensitive about.
• 18:59
  [SPEAKER_01]: Because I think the time commitments
• 19:02
  [SPEAKER_01]: for how much effort involved when you're going to deliver in my humble opinion are more important to people than even money.
• 19:09
  [SPEAKER_01]: It's because that's the main resource.
• 19:13
  [SPEAKER_01]: It's like it's then, it's the commitment, it's the sacrifice, it's the balance between the daily job and bringing on a new system.
• 19:25
  [SPEAKER_03]: Let's move into the future.
• 19:27
  [SPEAKER_03]: I'm curious about what the future holds for SafeBooks for, you know, for the product, the company, where the industry is going on with the things.
• 19:35
  [SPEAKER_01]: Look, I think entering into the office of the CFO is in its infancy right now.
• 19:41
  [SPEAKER_01]: I think we'll just get started.
• 19:43
  [SPEAKER_01]: Unlike maybe other areas of the organization, like cyber security DevOps saves marketing and so on, social engineering, of course.
• 19:52
  [SPEAKER_01]: I think it's really its infancy.
• 19:55
  [SPEAKER_01]: I do think that a lot of the things that finance people are consumed with today would be totally automated over the next three years.
• 20:04
  [SPEAKER_01]: From the other hand, I think the real work that needs to be done around governance, processes, process improvement, counting decisions, things like that, would remain with people.
• 20:15
  [SPEAKER_01]: So I think the finance corporate function is going to go to determine this transformation and transition into much more automation.
• 20:24
  [SPEAKER_01]: I think we're starting late in the process just because, again, engineering came first and saves a marketing, but I think finance turn is coming right now.
• 20:35
  [SPEAKER_01]: So I think that's that's that's like I see the future.
• 20:37
  [SPEAKER_01]: So I think it's gonna, things are gonna be done.
• 20:41
  [SPEAKER_01]: I think the system of workloads are gonna stay, but I think every system of workloads would require to
• 20:54
  [SPEAKER_01]: The office of the CFO is going to be completely different in three years.
• 20:59
  [SPEAKER_01]: Think about the public companies that has to write their QOK.
• 21:03
  [SPEAKER_01]: The poor-fading the validations, it's all going to be totally different and automated.
• 21:09
  [SPEAKER_01]: They're in a good way, I think.
• 21:10
  [SPEAKER_01]: Think the audit profession is going to change dramatically.
• 21:14
  [SPEAKER_01]: I think one of the things we allow is we don't sell to auditals.
• 21:17
  [SPEAKER_01]: We sell to companies, however, we do make the audit process much easier because we can enable the company to enable its auditals.
• 21:28
  [SPEAKER_01]: to see the full audit rate for each transaction for the first time.
• 21:31
  [SPEAKER_01]: So audittals can actually interact with the system the same way they interact with cloud or whatever.
• 21:37
  [SPEAKER_01]: So I think the audit process, which is a pain point for many companies, many finance people, it's not fun, you have to do your day job and you have to serve the auditor.
• 21:49
  [SPEAKER_01]: That's going to be totally different.
• 21:50
  [SPEAKER_01]: So I would give it three years to least talk to the way office of seafood does business
• 21:57
  [SPEAKER_01]: In the relevant function, right?
• 21:58
  [SPEAKER_01]: Obviously investor relations, it's investor relations, and that A.P.A.R., both with controller, S.E.C.
• 22:07
  [SPEAKER_01]: reporting, and F.P.A.
• 22:09
  [SPEAKER_01]: are going to be totally different.
• 22:12
  [SPEAKER_03]: Okay, I think I'm going to switch to you.
• 22:14
  [SPEAKER_03]: Who influences the way that you work?
• 22:16
  [SPEAKER_03]: Name a person or many persons are something you look up to and why.
• 22:21
  [SPEAKER_01]: To be honest, I've still spending many years in Silicon Valley.
• 22:24
  [SPEAKER_01]: All the Silicon Valley heroes influenced me all the way from... And again, in a remote way of course, reading about them or watching them.
• 22:32
  [SPEAKER_01]: I remember a lot of the things the latest Steve Jobs used to say on how you've been to team.
• 22:39
  [SPEAKER_01]: how you build a product, a superman, and to me the influence of figures like on the figures we all know from Silicon Valley.
• 22:48
  [SPEAKER_01]: And I have to also admit and say that I'm trying not to decide to learn from many people while younger than me and have been successful because I think it's always like
• 23:04
  [SPEAKER_01]: and skills, and you have to be able to constantly learn as an interpreter.
• 23:09
  [SPEAKER_01]: So, I'm being influenced by, and even if it's not directly relevant, you will definitely inspired by the determination, the consistency, the kind of like the problem-solving approach there is aliens you need to have, the culture, how you build the culture, and so on.
• 23:26
  [SPEAKER_01]: For example, building safe books, it was obvious to me we call it like security first.
• 23:33
  [SPEAKER_01]: Whatever we do, top of mind for engineering is the security of the data, right?
• 23:37
  [SPEAKER_01]: We process very sensitive data, the security always comes first.
• 23:41
  [SPEAKER_01]: And you have to have that mindset when you develop with the engineer.
• 23:46
  [SPEAKER_01]: It does store us some other times in releasing new features because we want to think about, okay, if we enable this thing,
• 23:55
  [SPEAKER_01]: How can we make sure it's also fully secured?
• 23:58
  [SPEAKER_01]: Because a lot of time changes in a platform can create vulnerabilities.
• 24:03
  [SPEAKER_01]: So being a security first company was like one of the cultures we were trying to install with the team.
• 24:12
  [SPEAKER_01]: But I was inspired by it and again, I don't think he would need to be inspired by someone who did exactly what you're doing.
• 24:19
  [SPEAKER_01]: It's their approach to how you build a team, how you build a culture, how you build a business, listen, learn from, let's say if you're building a business, what to do, what's not to do.
• 24:29
  [SPEAKER_01]: So, and you also learn from your team, so because when you hire a diverse team, I think that investigation brings a lot of value when people together in each country's own discipline and learn names and so on, take aways.
• 24:45
  [SPEAKER_03]: I come last question so you're getting on a plane and you're sitting next to a young entrepreneur who's built the next big thing.
• 24:51
  [SPEAKER_03]: They're jazzed about it, they can't wait to show it off to the world and can we show it off to you right there in the plane?
• 24:56
  [SPEAKER_03]: What advice do you give that person having on down this road a bit with safe books and multiple other places?
• 25:02
  [SPEAKER_01]: The cookie cutter advices around respect, your investors keep them informed, because let's see a lot of times where people understand they need to take money from investors, but they don't understand it's like a two-way relationship and you have to keep them engaged in informed.
• 25:21
  [SPEAKER_01]: So treat your investors as your partners, treat your team as your partners, try to build the culture and try to hire according to that culture, whatever that country is, and despite everyone saying the same thing, still, every organization has its own culture, try to hire the team based on the culture, not just necessarily based on schemes, and then you always have to move fast, especially today's world.
• 25:45
  [SPEAKER_01]: And I would find another advice, I think for the most part, if you
• 25:50
  [SPEAKER_01]: If you're building a business company, think about the business.
• 25:54
  [SPEAKER_01]: Don't just beat the technology and then look for a solution.
• 25:59
  [SPEAKER_01]: Even if you can sell a service first, sell a paid for a customer and then try to automate it.
• 26:04
  [SPEAKER_01]: That's a much better way than just build something and then look for.
• 26:09
  [SPEAKER_01]: So always make sure you see the business in everything you do because that's what really it's really important.
• 26:15
  [SPEAKER_01]: Make sure like when I say see this, make sure the customer has a pain, the customer has a budget, the customer wants to buy from you and you can solve that problem.
• 26:26
  [SPEAKER_01]: With whatever you provide, it's a service or problem.
• 26:29
  [SPEAKER_01]: I think that's fantastic advice.
• 26:31
  [SPEAKER_03]: Well, I come thank you for being on the show today and thank you
• 26:41
  [SPEAKER_03]: and this concludes another chapter of Code Story.
• 26:51
  [SPEAKER_03]: Code Story is hosted and produced by Noah Labhardt.
• 26:54
  [SPEAKER_03]: Be sure to subscribe on Apple Podcasts, Spotify, or the podcasting app at your choice.
• 27:00
  [SPEAKER_03]: And when you get a chance, leave us a review.
• 27:02
  [SPEAKER_03]: Both things help us out tremendously.
• 27:07
  [SPEAKER_03]: And thanks again for listening.