theCUBE + NYSE Wired: C-Suite Business Transformation | Ahikam Kaufman, Safebooks

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Ahikam Kaufman

Co-Founder & CEO

Safebooks AI

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Ahikam Kaufman, Safebooks

Ahikam Kaufman

Co-Founder & CEO Safebooks AI

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(INTRO)

John Furrier

>> I'm John Furrier, host of theCUBE here in our New York Stock Exchange theCUBE studios, of course, with our Palo Alto studio connecting Silicon Valley to Wall Street. This is our Transformation Edge series where we talk to leaders who are building the technology, operating the technology, investing in the technology to bring in this next era of AI. When injected into a company, intelligence, AI intelligence, transforms the business, the business model, and all of the operations. As a result, the entire business changes. That's the topic we're exploring. Ahikam Kaufman's here, co -founder and CEO of Safebooks AI, entrepreneur, taking a large scale approach, a systems approach to AI in the financial area, but of course has implications everywhere. Good to see you, thanks for coming on theCUBE.

John Furrier

>> Thank you, John, for having me. I'm excited for today's conversation.

John Furrier

>> So, finance has been this cottage industry. they had applications, a lot of vendors would sell stuff to finance departments, but now with the AI and post -cloud era, now we're in the AI era, which builds on top of cloud, large scale finance capabilities can be done very easily and with agentic AI. Injecting intelligence is certainly going to impact the overall company's numbers, and finance actually likes to roll those up, So they're going to be involved. They have their own department needs. So you see finance in the middle of all the action. In fact, just this week, Jensen Huang was on TV, and I wrote a post in April. He always says, energy bounds AI. Well, I wrote a post in April that said finance is a bounding function. They announced NVIDIA with Goldman, KKR, Brookfield, all the top capital, $500 billion for AI capital. Finance and energy are the bounding functions of AI. So the word finance is in there. You're going after this market.

Ahikam Kaufman

>> Right. So we're going after corporate finance, and I'd like to think three, four years, three and a half years after the models came out, now finance teams are being asked, what are you doing about AI? The challenge or the problem was always that finance, unlike other parts of the organization, they require data integrity and accuracy like no one else because they're subject to compliance and you just need to rely on data which is accurate so we created technology that uniquely provides AI with the right context and the right guardrails to make sure that every response would be accurate and agents would be able to operate as well as, if not better than, a human being.

John Furrier

>> So you had a previous venture you sold to QuickBooks. I'm sorry, Intuit.

John Furrier

>> Right.

John Furrier

>> QuickBooks is the product. Safebooks, QuickBooks, any? You get the green light to go with Safebooks on this one?

Ahikam Kaufman

>> I wanted to correspond with the QuickBooks brand, but I wanted also to pick a name where it kind of reflects that when customers buy a product I think that you need to kind of sell a sentiment you need to sell an experience and making people feel safe about the data because every large company there's an explosion of data and you can't check everything all the time we're creating that cyber like experience for finance because with the automation comes also the level of accuracy and integrity that human beings cannot do with that level of data. So creating that sense of safety and security for financial data and for the financial leaders is what our name kind of...

John Furrier

>> Yeah, and you mentioned accuracy because of compliance, but also they're in the numbers game. They want to be, they have to be right. Right. Because if you get a number wrong, think about the consequences on audits. You're going to unwind it. You got to throw even more forensic work at it. So I think that's a key point. But I want to ask you about your idea around this venture because in hearing you talk, and I'd love for you to expand on this because I think you'd illuminate it. It feels like a platform because you're talking about not just a point solution.

Ahikam Kaufman

>> No.

John Furrier

>> You're talking about a platform.

John Furrier

>> Yeah.

John Furrier

>> Take me through your thinking around this and what it means to the company, not just the finance department. Right.

Ahikam Kaufman

>> So from the get-go, we understood we needed to do two things which are different. First, to develop the technologies that can provide AI with the right context and again the guardrails to be able to have a deterministic way to take the actions. The other thing that we wanted to do is we wanted to make sure that we don't just generate a single point solution, but eventually with AI, because you can do so many things, I don't want the office of the CFO to deal with multiple or they don't want to deal with multiple vendors each for a point solution. Once we've got the data right from all the systems, using our agentic layer, we can create that point solution on top of the platform, allowing finance to go and expand their use using the same platform. But instead of using the Claudes of the world, which are great, but cannot provide that level of data accuracy and integrity, they're using Safebooks and using our Agentic Layer. You can configure any agent to do any work, whether it's across cash, revenues, expenses, and so on.

John Furrier

>> A data layer in finance is very compelling to me. I like this idea. So explain more because there's technology involved. We're hearing more and more words like ontology, graph databases, Palantir, they popularized the term. That's been around since the 80s. I did that as a kid. I was playing with ontology. There was not a lot of infrastructure to support the scale, but now we have a database world where you can actually connect data. You can put a context graph together. And there's a lot of benefits that are compatible with computer science of AI. Recursion, things like that are like you start to see a formation of a brain.

John Furrier

>> Right.

John Furrier

>> Metaphor. Why wouldn't a company want to have a financial brain And connect it to all aspects of the business?

Ahikam Kaufman

>> I think again, the technology did not exist. Even a portion of our technology did not exist even a year ago. I think 2026 is the year where finance is starting to embrace AI And embarking on that journey. I'd like to think that today 100 % of the companies, meaning finance organization companies, are being asked, what are you doing on AI? In 2024, 2025, it was more like engineering, sales, marketing. Now it's finance because we do have the technology to be able to provide accurate results which are also auditable. We recently acquired our SOC 1 certification, which allows our customers And their auditors to rely on the outcome. But the foundation is the ability or the pain point originally was the fact that in corporate finance, every transaction is processed by multiple systems. And the ability to connect the dots between these systems was manual work. And now it's fully automated using our graph.

John Furrier

>> Yeah, And you have connective tissue with APIs And MCP servers. But you brought up a good point because compliance, finance has all the elements of success built into it for AI to be injected. It's got a lot of stuff that's in manuals, process rules. It's got data, probably good data, maybe not perfect, clean data. It's got process. and if you connect to other systems in a distributed computing fashion, you now have a mechanism to cross boundaries outside the department, hence other business units.

Ahikam Kaufman

>> Right. I'll tell you even more. I think an example maybe Elon Musk used was that 100 years ago, people used to sit in a bank and calculate the balance for each account on a monthly basis. That's work that I don't think anyone wants to go back and do. I think we're going to witness that same transformation in corporate finance. Imagine people are doing the same work every day across the data. You can't do it across all the data because we're all human. And again, there's a lot of details. So the ability to use a machine that can emulate or replace some of that manual work, allowing people to focus on more strategic work is actually essential. But technology did not exist. I think a year from now it's going to look totally different.

John Furrier

>> Well, the thing about that metaphor of counting money, but take the modern version of that pre-AI. Labor is essentially a manufacturing operations concept. It's an assembly line of people who expect to do this repetitive task. The next person does the same, so you have uniform, deterministic tasks. That's human. That's easy to understand.

Ahikam Kaufman

>> Just to give you an example, imagine today in large companies, let's say they process thousands of orders for their customers every single month. Now finance needs to sit down, look at the documentation, look at the policies that they have, look at the billing and make sure everything is correct and approve the deal. And it takes them time until they approve the deal. The machine can do all of that, which means less mistakes, less customer-facing mistakes, less leakage or missing billing or whatever. Better relationship with your sales organization because you move things faster. And the anomalies and discrepancies are still there, so you manage the exception. You don't even need to manage the standard.

John Furrier

>> You know, Ahikam, that's a really good point. You brought up something that made me think, and I want to bring this back up. You mentioned sales and finance. In every company that I've worked in, in every company my friends worked in, every company that I've observed, they hate each other, basically. And you've got to put the finance department in one building and you put sales. Because sales are like they're very agile. They're front office. They think differently. The cultural intersection, what you're getting at, is actually a liberating opportunity from a change management perspective. Because finance people want things buttoned up and sales say, just get the quote out there and then put it in that bucket. So there's a lot of inbound logistics. But if you have a problem, I want to know about it fast.

Ahikam Kaufman

>> Yes. Don't drag it for a week and then. So speed, velocity here is an essential component in the relationship.

John Furrier

>> How do you see finance with AI, the way you're thinking about it, impacting the velocity, accuracy, efficacy of sales and marketing? Specifically sales go to market because product market fits change. But like you said, they're just on the front lines communicating the value proposition of a company. And then finance has to, inside out, align with that group, their customer focus. There's a lot of potential error opportunities there. How does this fix that?

Ahikam Kaufman

>> So I'd like to think that, I think eventually everyone needs to be a corporate citizen. And everyone wants to get the deal done for the benefit of the customer, for the benefit of the vendor or the company. and to get the commission check, right? And in order to do that, they expect their finance peers to be able to look at stuff in time so they can react to the customer and be able to identify all the issues in detail so if there is an issue, they don't miss it and then something happens down the road. So the ability to accurately scan everything 100 % in real time, So the velocity and quality of deal review is one of the things, one of the many things, Safebooks solves.And again...

John Furrier

>> That's a huge value proposition. Because that's where miscommunication, misinterpretation, lack of understanding the other party, that's where this could be very acrimonious.

Ahikam Kaufman

>> Right. Right.So, and again, allowing people to focus on managing exceptions as opposed to managing everything.

John Furrier

>> Everything. Yeah. We've all seen deal reviews. Okay, yes. And then send an email, asynchronous work. It's just that work goes away. All right, talk about the company. Where are you guys now? Take us through the plan, the strategy, where you are on momentum. I love the platform play you have here.

Ahikam Kaufman

>> Yeah, so we started the company three years ago. We've built a very deep technology. And also when the recent models came out around maybe nine months ago, we were able to then amplify it with the agentic layer. And we started to go to market. Now we serve about, I think, 15 large enterprises, corporates, and we will be starting to think about our Series A soon. And that's it.

John Furrier

>> How many customers do you have now working with you?

Ahikam Kaufman

>> So 15. 15.

John Furrier

>> Are they design partners, early customers?

Ahikam Kaufman

>> No, no, they're paying customers.

John Furrier

>> Paying customers, okay.

Ahikam Kaufman

>> Paying customers. We provide value. Actually, today, the way the relationship goes, we prefer to do like, to start the relationship kick off with a proof of value. And we turn this around, we turn the use case around in a couple of weeks. And then the interaction is different because we show you stuff on your data.

John Furrier

>> It's not like. It's like the best forward deploy engineer motion. You go in, look at their data and come in with not just a slick demo. Right. You actually have actionable... Right.

Ahikam Kaufman

>> And then we want to become your trusted advisor because we know your data as well as you do. And with our platform, we actually would like to continue and deepen the relationship byexpanding...

John Furrier

>> Okay. You got me to a yes. You got me to a yes. I'm a customer now. I want to work with you. I want to sign on the line that's dotted. Tell me what's next. How do I work with you? What do I need to do on my end?How do you add value?

Ahikam Kaufman

>> Typically, our initial goal is to automate your close processes. So we, again, we have a team of forward deployed engineers as part of our delivery team. They would go and map your close processes. When you try to nail it down, it could be anywhere between 150 to 800 tasks. Depends on the size and complexity of the business. And then we go ahead and configure them on the system. It could be a process that would take, depending on the size of the company, anywhere between 8 to 10 weeks to maybe even 6 months. But every single week or month, your life gets better and something else goes away. And when we create these agents, they can do three things. They automate the work, they create the documentation, the work papers and all the specific documents. The proof of work, that's key. Right, the proof of work, which is essential. And you can interact with them. You can ask them questions like a staff member, like an employee. You can ask them questions.

John Furrier

>> Like what kind of questions?

Ahikam Kaufman

>> Those questions could be like, hey, why didn't you do this there?Or imagine if we –

John Furrier

>> Can you get down to the level of why did you book it there in that category versus that?

Ahikam Kaufman

>> Right.The same way because, look, in finance it's critical, especially in public companies, that there will be a preparer and a reviewer. We replace the preparer. The reviewer is still the human in the loop. The same way the human in the loop would ask his team members.

John Furrier

>> What percentage of work would you say goes into preparation without AI?

Ahikam Kaufman

>> So I'd like to think it's 90%.

John Furrier

>> Like a heavy lift.

Ahikam Kaufman

>> Let's say, yeah, probably 80 -20, but I think it's really 90 -10.

John Furrier

>> I've heard similar. Yeah. That's consistent with what I've been hearing. Right.

Ahikam Kaufman

>> That's a lot. Right. That's a lot.

John Furrier

>> That's a lot.

Ahikam Kaufman

>> It's much easier to review, and also you think more strategically. When you need to prepare every line, it's way more difficult. and you get lost in the woods as opposed to...

Ahikam Kaufman

>> Yeah, it's the bark of the tree.

Ahikam Kaufman

>> And you don't see the big picture. Right, right. So being able to do that, and then you interact with the agents just like an employee. You can ask him questions. You can challenge him. You can alter the documentation.

John Furrier

>> All right, what's next for you? Tell us what's on your agenda. Obviously, you've got 15 customers you're building out. You're getting more data, proof points from customers. I'm assuming to continue to get more customers. What else you got going on?

Ahikam Kaufman

>> So we're looking to raise our series A by early next year. And that's really where we want to go and continue from there. I think our technology is unique. I think 100 % of finance teams around the world, around the country are looking at AI. And I think it's the right moment to expand.

John Furrier

>> All right, what would you say if I say, well there's a bubble going on. How would you answer that question? Everyone's saying there's a bubble in AI. Oh, supply is constrained. There's demand for intellect. There's intelligence.

Ahikam Kaufman

>> I think like anything else in the world, the cost of using AI will drop, will continue to drop down as the infrastructure will be more efficient. And I'd like to think AI would be even more available. one of the analogies I'm using, I think we're looking at the Model T now in 1920 and we're switching, we're moving from the horse to the Model T, but I think the Lexus and the Mercedes -Benz and the Tesla are like three, four years down the road. So I think we're not, it's just getting started.

John Furrier

>> There's a discovery journey for enterprises right now. They're learning, and once they get unit economics nailed down, once they see that intelligence, that's the product they're buying. And the world is going to significantly change in ways we can't even imagine. Yeah, I think the demand curve is good.Now on the funding, have you had a seed round?

Ahikam Kaufman

>> So we raised a relatively big seed of $50 million. Seed round.

John Furrier

>> Seed round. It used to be the old Series A, but...

Ahikam Kaufman

>> No, no, $50 million of seed, four or five different funds. One of the leaders is Propel Ventures from San Francisco, and Insight Partners and some others, and 10D. and that was really meant to help us build the foundation, the technology, and to support growth.

John Furrier

>> So you're going to have a good A round because you have some nice investors, world -class investors in there, Insight, very strong. Probably don't need any help from me, but if you do raise from theCUBE, you get a 20 % discount on the equity. There you go. He's like, yeah, approved by the board. No, I'm only kidding. Good luck on the financing. Love the venture. Again, I love the systems approach. It's very consistent with what we're seeing on AI infrastructure. Kind of the same game, but everything's denser, close to the compute. You're seeing the architecture's systematic. It's a systems game. Congratulations.

Ahikam Kaufman

>> I'd like to think the world is changing, but we're also solving pains that people are experiencing on a daily basis. And once we solve that, I don't think anyone would like to go back.

John Furrier

>> prep, it's just grind. Take that toil, that undifferentiated heavy lifting, and move it into review and whatever professional service they could do. That's a winning hand if you can get it done. So congratulations, thanks for coming on.

Ahikam Kaufman

>> Thank you, John.

John Furrier

>> All right, this is the kind of conversation at theCUBE we love. It's deep tech meets reality. That is the transformation edge, the competitive edge for the companies going forward will be how they can leverage AI, not just leveraging AI for the sake of leveraging AI, it's injecting intelligence into their organization. It's not an IT project. The payback is there and there's money involved. It is a transformation time and the biggest edge will go to the winners. Thanks for watching.

Ahikam Kaufman, Safebooks

search

(INTRO)

John Furrier

>> I'm John Furrier, host of theCUBE here in our New York Stock Exchange theCUBE studios, of course, with our Palo Alto studio connecting Silicon Valley to Wall Street. This is our Transformation Edge series where we talk to leaders who are building the technology, operating the technology, investing in the technology to bring in this next era of AI. When injected into a company, intelligence, AI intelligence, transforms the business, the business model, and all of the operations. As a result, the entire business changes. That's the topic we're exploring. Ahikam Kaufman's here, co -founder and CEO of Safebooks AI, entrepreneur, taking a large scale approach, a systems approach to AI in the financial area, but of course has implications everywhere. Good to see you, thanks for coming on theCUBE.

John Furrier

>> Thank you, John, for having me. I'm excited for today's conversation.

John Furrier

>> So, finance has been this cottage industry. they had applications, a lot of vendors would sell stuff to finance departments, but now with the AI and post -cloud era, now we're in the AI era, which builds on top of cloud, large scale finance capabilities can be done very easily and with agentic AI. Injecting intelligence is certainly going to impact the overall company's numbers, and finance actually likes to roll those up, So they're going to be involved. They have their own department needs. So you see finance in the middle of all the action. In fact, just this week, Jensen Huang was on TV, and I wrote a post in April. He always says, energy bounds AI. Well, I wrote a post in April that said finance is a bounding function. They announced NVIDIA with Goldman, KKR, Brookfield, all the top capital, $500 billion for AI capital. Finance and energy are the bounding functions of AI. So the word finance is in there. You're going after this market.

Ahikam Kaufman

>> Right. So we're going after corporate finance, and I'd like to think three, four years, three and a half years after the models came out, now finance teams are being asked, what are you doing about AI? The challenge or the problem was always that finance, unlike other parts of the organization, they require data integrity and accuracy like no one else because they're subject to compliance and you just need to rely on data which is accurate so we created technology that uniquely provides AI with the right context and the right guardrails to make sure that every response would be accurate and agents would be able to operate as well as, if not better than, a human being.

John Furrier

>> So you had a previous venture you sold to QuickBooks. I'm sorry, Intuit.

John Furrier

>> Right.

John Furrier

>> QuickBooks is the product. Safebooks, QuickBooks, any? You get the green light to go with Safebooks on this one?

Ahikam Kaufman

>> I wanted to correspond with the QuickBooks brand, but I wanted also to pick a name where it kind of reflects that when customers buy a product I think that you need to kind of sell a sentiment you need to sell an experience and making people feel safe about the data because every large company there's an explosion of data and you can't check everything all the time we're creating that cyber like experience for finance because with the automation comes also the level of accuracy and integrity that human beings cannot do with that level of data. So creating that sense of safety and security for financial data and for the financial leaders is what our name kind of...

John Furrier

>> Yeah, and you mentioned accuracy because of compliance, but also they're in the numbers game. They want to be, they have to be right. Right. Because if you get a number wrong, think about the consequences on audits. You're going to unwind it. You got to throw even more forensic work at it. So I think that's a key point. But I want to ask you about your idea around this venture because in hearing you talk, and I'd love for you to expand on this because I think you'd illuminate it. It feels like a platform because you're talking about not just a point solution.

Ahikam Kaufman

>> No.

John Furrier

>> You're talking about a platform.

John Furrier

>> Yeah.

John Furrier

>> Take me through your thinking around this and what it means to the company, not just the finance department. Right.

Ahikam Kaufman

>> So from the get-go, we understood we needed to do two things which are different. First, to develop the technologies that can provide AI with the right context and again the guardrails to be able to have a deterministic way to take the actions. The other thing that we wanted to do is we wanted to make sure that we don't just generate a single point solution, but eventually with AI, because you can do so many things, I don't want the office of the CFO to deal with multiple or they don't want to deal with multiple vendors each for a point solution. Once we've got the data right from all the systems, using our agentic layer, we can create that point solution on top of the platform, allowing finance to go and expand their use using the same platform. But instead of using the Claudes of the world, which are great, but cannot provide that level of data accuracy and integrity, they're using Safebooks and using our Agentic Layer. You can configure any agent to do any work, whether it's across cash, revenues, expenses, and so on.

John Furrier

>> A data layer in finance is very compelling to me. I like this idea. So explain more because there's technology involved. We're hearing more and more words like ontology, graph databases, Palantir, they popularized the term. That's been around since the 80s. I did that as a kid. I was playing with ontology. There was not a lot of infrastructure to support the scale, but now we have a database world where you can actually connect data. You can put a context graph together. And there's a lot of benefits that are compatible with computer science of AI. Recursion, things like that are like you start to see a formation of a brain.

John Furrier

>> Right.

John Furrier

>> Metaphor. Why wouldn't a company want to have a financial brain And connect it to all aspects of the business?

Ahikam Kaufman

>> I think again, the technology did not exist. Even a portion of our technology did not exist even a year ago. I think 2026 is the year where finance is starting to embrace AI And embarking on that journey. I'd like to think that today 100 % of the companies, meaning finance organization companies, are being asked, what are you doing on AI? In 2024, 2025, it was more like engineering, sales, marketing. Now it's finance because we do have the technology to be able to provide accurate results which are also auditable. We recently acquired our SOC 1 certification, which allows our customers And their auditors to rely on the outcome. But the foundation is the ability or the pain point originally was the fact that in corporate finance, every transaction is processed by multiple systems. And the ability to connect the dots between these systems was manual work. And now it's fully automated using our graph.

John Furrier

>> Yeah, And you have connective tissue with APIs And MCP servers. But you brought up a good point because compliance, finance has all the elements of success built into it for AI to be injected. It's got a lot of stuff that's in manuals, process rules. It's got data, probably good data, maybe not perfect, clean data. It's got process. and if you connect to other systems in a distributed computing fashion, you now have a mechanism to cross boundaries outside the department, hence other business units.

Ahikam Kaufman

>> Right. I'll tell you even more. I think an example maybe Elon Musk used was that 100 years ago, people used to sit in a bank and calculate the balance for each account on a monthly basis. That's work that I don't think anyone wants to go back and do. I think we're going to witness that same transformation in corporate finance. Imagine people are doing the same work every day across the data. You can't do it across all the data because we're all human. And again, there's a lot of details. So the ability to use a machine that can emulate or replace some of that manual work, allowing people to focus on more strategic work is actually essential. But technology did not exist. I think a year from now it's going to look totally different.

John Furrier

>> Well, the thing about that metaphor of counting money, but take the modern version of that pre-AI. Labor is essentially a manufacturing operations concept. It's an assembly line of people who expect to do this repetitive task. The next person does the same, so you have uniform, deterministic tasks. That's human. That's easy to understand.

Ahikam Kaufman

>> Just to give you an example, imagine today in large companies, let's say they process thousands of orders for their customers every single month. Now finance needs to sit down, look at the documentation, look at the policies that they have, look at the billing and make sure everything is correct and approve the deal. And it takes them time until they approve the deal. The machine can do all of that, which means less mistakes, less customer-facing mistakes, less leakage or missing billing or whatever. Better relationship with your sales organization because you move things faster. And the anomalies and discrepancies are still there, so you manage the exception. You don't even need to manage the standard.

John Furrier

>> You know, Ahikam, that's a really good point. You brought up something that made me think, and I want to bring this back up. You mentioned sales and finance. In every company that I've worked in, in every company my friends worked in, every company that I've observed, they hate each other, basically. And you've got to put the finance department in one building and you put sales. Because sales are like they're very agile. They're front office. They think differently. The cultural intersection, what you're getting at, is actually a liberating opportunity from a change management perspective. Because finance people want things buttoned up and sales say, just get the quote out there and then put it in that bucket. So there's a lot of inbound logistics. But if you have a problem, I want to know about it fast.

Ahikam Kaufman

>> Yes. Don't drag it for a week and then. So speed, velocity here is an essential component in the relationship.

John Furrier

>> How do you see finance with AI, the way you're thinking about it, impacting the velocity, accuracy, efficacy of sales and marketing? Specifically sales go to market because product market fits change. But like you said, they're just on the front lines communicating the value proposition of a company. And then finance has to, inside out, align with that group, their customer focus. There's a lot of potential error opportunities there. How does this fix that?

Ahikam Kaufman

>> So I'd like to think that, I think eventually everyone needs to be a corporate citizen. And everyone wants to get the deal done for the benefit of the customer, for the benefit of the vendor or the company. and to get the commission check, right? And in order to do that, they expect their finance peers to be able to look at stuff in time so they can react to the customer and be able to identify all the issues in detail so if there is an issue, they don't miss it and then something happens down the road. So the ability to accurately scan everything 100 % in real time, So the velocity and quality of deal review is one of the things, one of the many things, Safebooks solves.And again...

John Furrier

>> That's a huge value proposition. Because that's where miscommunication, misinterpretation, lack of understanding the other party, that's where this could be very acrimonious.

Ahikam Kaufman

>> Right. Right.So, and again, allowing people to focus on managing exceptions as opposed to managing everything.

John Furrier

>> Everything. Yeah. We've all seen deal reviews. Okay, yes. And then send an email, asynchronous work. It's just that work goes away. All right, talk about the company. Where are you guys now? Take us through the plan, the strategy, where you are on momentum. I love the platform play you have here.

Ahikam Kaufman

>> Yeah, so we started the company three years ago. We've built a very deep technology. And also when the recent models came out around maybe nine months ago, we were able to then amplify it with the agentic layer. And we started to go to market. Now we serve about, I think, 15 large enterprises, corporates, and we will be starting to think about our Series A soon. And that's it.

John Furrier

>> How many customers do you have now working with you?

Ahikam Kaufman

>> So 15. 15.

John Furrier

>> Are they design partners, early customers?

Ahikam Kaufman

>> No, no, they're paying customers.

John Furrier

>> Paying customers, okay.

Ahikam Kaufman

>> Paying customers. We provide value. Actually, today, the way the relationship goes, we prefer to do like, to start the relationship kick off with a proof of value. And we turn this around, we turn the use case around in a couple of weeks. And then the interaction is different because we show you stuff on your data.

John Furrier

>> It's not like. It's like the best forward deploy engineer motion. You go in, look at their data and come in with not just a slick demo. Right. You actually have actionable... Right.

Ahikam Kaufman

>> And then we want to become your trusted advisor because we know your data as well as you do. And with our platform, we actually would like to continue and deepen the relationship byexpanding...

John Furrier

>> Okay. You got me to a yes. You got me to a yes. I'm a customer now. I want to work with you. I want to sign on the line that's dotted. Tell me what's next. How do I work with you? What do I need to do on my end?How do you add value?

Ahikam Kaufman

>> Typically, our initial goal is to automate your close processes. So we, again, we have a team of forward deployed engineers as part of our delivery team. They would go and map your close processes. When you try to nail it down, it could be anywhere between 150 to 800 tasks. Depends on the size and complexity of the business. And then we go ahead and configure them on the system. It could be a process that would take, depending on the size of the company, anywhere between 8 to 10 weeks to maybe even 6 months. But every single week or month, your life gets better and something else goes away. And when we create these agents, they can do three things. They automate the work, they create the documentation, the work papers and all the specific documents. The proof of work, that's key. Right, the proof of work, which is essential. And you can interact with them. You can ask them questions like a staff member, like an employee. You can ask them questions.

John Furrier

>> Like what kind of questions?

Ahikam Kaufman

>> Those questions could be like, hey, why didn't you do this there?Or imagine if we –

John Furrier

>> Can you get down to the level of why did you book it there in that category versus that?

Ahikam Kaufman

>> Right.The same way because, look, in finance it's critical, especially in public companies, that there will be a preparer and a reviewer. We replace the preparer. The reviewer is still the human in the loop. The same way the human in the loop would ask his team members.

John Furrier

>> What percentage of work would you say goes into preparation without AI?

Ahikam Kaufman

>> So I'd like to think it's 90%.

John Furrier

>> Like a heavy lift.

Ahikam Kaufman

>> Let's say, yeah, probably 80 -20, but I think it's really 90 -10.

John Furrier

>> I've heard similar. Yeah. That's consistent with what I've been hearing. Right.

Ahikam Kaufman

>> That's a lot. Right. That's a lot.

John Furrier

>> That's a lot.

Ahikam Kaufman

>> It's much easier to review, and also you think more strategically. When you need to prepare every line, it's way more difficult. and you get lost in the woods as opposed to...

Ahikam Kaufman

>> Yeah, it's the bark of the tree.

Ahikam Kaufman

>> And you don't see the big picture. Right, right. So being able to do that, and then you interact with the agents just like an employee. You can ask him questions. You can challenge him. You can alter the documentation.

John Furrier

>> All right, what's next for you? Tell us what's on your agenda. Obviously, you've got 15 customers you're building out. You're getting more data, proof points from customers. I'm assuming to continue to get more customers. What else you got going on?

Ahikam Kaufman

>> So we're looking to raise our series A by early next year. And that's really where we want to go and continue from there. I think our technology is unique. I think 100 % of finance teams around the world, around the country are looking at AI. And I think it's the right moment to expand.

John Furrier

>> All right, what would you say if I say, well there's a bubble going on. How would you answer that question? Everyone's saying there's a bubble in AI. Oh, supply is constrained. There's demand for intellect. There's intelligence.

Ahikam Kaufman

>> I think like anything else in the world, the cost of using AI will drop, will continue to drop down as the infrastructure will be more efficient. And I'd like to think AI would be even more available. one of the analogies I'm using, I think we're looking at the Model T now in 1920 and we're switching, we're moving from the horse to the Model T, but I think the Lexus and the Mercedes -Benz and the Tesla are like three, four years down the road. So I think we're not, it's just getting started.

John Furrier

>> There's a discovery journey for enterprises right now. They're learning, and once they get unit economics nailed down, once they see that intelligence, that's the product they're buying. And the world is going to significantly change in ways we can't even imagine. Yeah, I think the demand curve is good.Now on the funding, have you had a seed round?

Ahikam Kaufman

>> So we raised a relatively big seed of $50 million. Seed round.

John Furrier

>> Seed round. It used to be the old Series A, but...

Ahikam Kaufman

>> No, no, $50 million of seed, four or five different funds. One of the leaders is Propel Ventures from San Francisco, and Insight Partners and some others, and 10D. and that was really meant to help us build the foundation, the technology, and to support growth.

John Furrier

>> So you're going to have a good A round because you have some nice investors, world -class investors in there, Insight, very strong. Probably don't need any help from me, but if you do raise from theCUBE, you get a 20 % discount on the equity. There you go. He's like, yeah, approved by the board. No, I'm only kidding. Good luck on the financing. Love the venture. Again, I love the systems approach. It's very consistent with what we're seeing on AI infrastructure. Kind of the same game, but everything's denser, close to the compute. You're seeing the architecture's systematic. It's a systems game. Congratulations.

Ahikam Kaufman

>> I'd like to think the world is changing, but we're also solving pains that people are experiencing on a daily basis. And once we solve that, I don't think anyone would like to go back.

John Furrier

>> prep, it's just grind. Take that toil, that undifferentiated heavy lifting, and move it into review and whatever professional service they could do. That's a winning hand if you can get it done. So congratulations, thanks for coming on.

Ahikam Kaufman

>> Thank you, John.

John Furrier

>> All right, this is the kind of conversation at theCUBE we love. It's deep tech meets reality. That is the transformation edge, the competitive edge for the companies going forward will be how they can leverage AI, not just leveraging AI for the sake of leveraging AI, it's injecting intelligence into their organization. It's not an IT project. The payback is there and there's money involved. It is a transformation time and the biggest edge will go to the winners. Thanks for watching.