SaaS Interviews with CEOs, Startups, Founders
Selling Check for $400M, Now Building a $1.5M ARR AI Startup
06 May 2026
Transcript generated automatically by AI and may contain errors.
Chapter 1: What led to the $400 million acquisition of Check by Intuit?
I think that was a $360 million acquisition by Intuit in 2019. Is my timeline right?
Actually, it was close to $400 million.
How many millionaires did you make? At least 10. Are you comfortable sharing? What's the largest company pay you? Do you have any million dollar per year accounts?
I would say our largest engagement is around $300,000 right now. In Q2 of 2014, when we sold the company to Intuit, we were like the largest M&A deal, according to the Wall Street Journal, by coincidence.
Are you comfortable sharing what you personally took home when you exited into it?
Very large audience. I would prefer not to share that if that's okay.
That's totally okay. How many paying customers are you working with now today at SafeBooks?
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Chapter 2: How did SafeBooks AI achieve $1.5 million in ARR?
So we have about 15 paying customers. You have to understand we are selling powerful data and automation platform for the office of the CFO.
You got 3 million bucks of extra rooming around. I want to invest. Hey, folks. My guest today is Ahikam Kaufman. He is a veteran fintech executive who previously co-founded Check, which was acquired by Intuit and served as an executive at HP and Mercury Interactive.
Today, he's building SafeBooks AI, which uses agentic revenue integrity to autonomously ensure financial data accuracy for enterprises. Ahikam, you ready to take us to the top?
Yeah, hopefully.
All right. That is a mouthful, but it's important work. Give it to me like a kindergartner. What are you selling today?
What we are basically selling is the ability to understand your financial data across system, validate it, check it, and replace significant manual work, which today is being done by accountants.
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Chapter 3: What strategies did SafeBooks use to secure their first $300,000 engagement?
One of the challenges in the office of the CFO is there is a gap between what an accountant is trained to do and what he needs to do, which is basically check his data across multiple systems in real time, across structured and unstructured data. And we now, using the benefits and power of AI, we can fully automate that work.
Let's play a game here. I'm going to do old, and then you're going to tell me the new way to do it using your technology. Okay, old way. I do invoices via bill.com. I have a fractional CFO. I pay $3,000 a month. They go into my QuickBooks at the end of each month. They close everything out. They integrate bill. They integrate my bank accounts via Plaid or SoftEdge or Teller. They close the books.
That's the old way. The new way is what?
Chapter 4: Why is building a proprietary graph database crucial for preventing AI hallucinations?
I'm sorry. So that's like a great example, but doesn't fit our ICP. We're catering to large enterprises, companies, public companies who need to execute governance across their data. For the most part, they're not using the platform you mentioned, and they're using a flow of other systems.
And the way they do business and conduct the business where we currently focus, which is order to cash and revenue and billing integrity, is very, very cumbersome, manual, and process through multiple systems like CRM, billing, coding, ELP, and so on.
So I did it on purpose, right? Can you give me an example just like I did? So instead of using CRM or CPQ, actually tell the story of the actual, like an actual example of what you're replacing.
Right. So think about a company issuing a quote for a product or service, right, which then translates into a contract. The contract has many terms. It's complex. The contract is not fully accurately captured by the CRM, which may lead into billing discrepancies or errors.
Chapter 5: How does SafeBooks automate the quote-to-cash process for enterprises?
And then there's a human in the loop in finance that manually checks the data across all these systems and across the document, right? That happens when you close the deal. Then maybe you have to charge the customer extra for all kinds of extra usage. So he goes back to the document, he changes stuff. Then maybe the sales organization, they change some of the terms with the customer.
So again, the data is wrong. And today, all of that work has to be done by people where they are logging into the disparate systems, checking the data, checking the documents, which is always the source of truth. And we now can replace it with agents who see the data end-to-end, and it has to be end-to-end, and that's part of our secret sauce.
And when they see the data end-to-end and they understand it, they know exactly what the discrepancies are and how to remediate them.
So this sounds to me more like it is, you know, there's an industry that's well-known called quote-to-cash. You're effectively making that system way more efficient using artificial intelligence and agents.
Yes. We're focusing on billing and revenue integrity, but almost the same use cases exist in procure-to-pay and in payroll. These are like the main three engines that leads most of the company's money flow or cash flow.
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Chapter 6: What are the challenges of managing founder dilution in a venture-backed company?
So, yeah.
Are these companies that need at least, you know, $100 million of revenue before they feel this problem and pay you? or is it bigger, 500 million revenue or more?
Totally. I'd like to think that companies that start to exceed $200 million, $300 million in revenues would significantly feel the pain because they would have to cope with multiple offerings, multiple products, Data structure, which is very different between one system to another and a lot of transactional volume in their day-to-day business.
So on a monthly, quarterly, actually on a daily, weekly, monthly, quarterly basis, they have to check, verify their revenue data. The reason they need to do it is for three or four very critical reasons. It's customer facing data, right? If you're wrong in your billing, it's not a pleasant experience with your customer. You need to check it for compliance purposes. Then you have to deploy people.
Now the industry is facing an accountant shortage. It's not happening in real time. People can make mistakes.
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Chapter 7: How did SafeBooks secure $15 million in seed funding?
Even if you offshore this work, we see significant amount of mistakes in each company we're working with. And it's all natural in you.
I think the product suite now is super clear. Thanks for that. When a customer does sign up and use you, on average, what are they paying you per month or per year to use the technology?
I'd like to think that the initial use case we start with, it's around the way we kind of sell it. It's the cost of a single resource. finance resource. So I would say it's around like $100,000, $125,000 for the initial starter use case. And then each use case has its own ROI so you can justify that and charge more.
But what we are now doing, which is pretty amazing, we are now releasing a capability which allows the customer using AI to prompt and configure his own use case. So the more work the customer can do, IT can do, on the platform serving their customers, the more they can benefit from the system without adding additional cost.
Because we don't talk about data. My gut tells me because you're adding so much extra product value that you have upsold customers way above the $125,000 ACV without naming, obviously, the customer name for confidentiality reasons.
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Chapter 8: What are Ahikam's predictions for the future of AI in enterprise finance?
Are you comfortable sharing what's the largest company pay you? Do you have any million dollar per year accounts?
You know, we just started to go to market like about less than a year ago. So, but we do have, you know, I would say our largest engagement is around $300,000 right now. Okay. But we do see the potential because they continue, again, companies continue to suffer.
We are now seeing a significant reduction in stock prices, which will force companies to be more efficient and try to remove the human in the loop as much as possible. So I think we're going to see more.
To be clear on the growth story, it sounds like 2025 was your first paying customer. When did you write the first line of code for the platform? What year?
So we actually started first line of code probably around June, July of 2023. But the thing is, is that in order to do what we are doing today, we actually started from the foundation and the foundation was to build a sophisticated proprietary graph database that for the first time connects and links automatically using AI, all the various data sources in the office of the CFO.
That was never done before. And that allows us to easily automate because we provide the agents the end to end view on how the transaction behaves across systems and linking it to the source of tools, which is always a document.
How did you fund the business between 2023 and 2025? I know you had obviously a big exit in the past. Did you self-fund it or did you raise capital?
We raised a $15 million seed in like two chunks from like six, seven funds, early, very early stage fund. But we raised like $15 million. Got it. That makes a lot of sense.
Guys, remember, I am not just a YouTuber. I'm investing in my third fund. We've deployed $250 million into 550 software companies so far. Again, at FounderPath.com. If you're interested in capital, I would love to cut you a check because I know you're investing in your education. You watch my show. So sign up at FounderPath.com. And when you get the onboarding email, I reply and I see all those.
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