Ahikam Kaufman

speaker

208 appearances 1 recordings 1 series first heard Jun 2026 last heard 30 Jun

Ahikam Kaufman’s voice in public audio — every appearance, attributed to the second.

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Recordings per month over the last 12 months — 1 in all, peaking in Jun 2026 with 1.

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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 · 30 Jun 2026 podcast

And by doing that, when the AI then looks on the data, it can't go wrong.

It can't hallucinate.

We spent a lot of time building that infrastructure as opposed to just build a product.

And once we built that infrastructure, robust infrastructure, the AI came to a point where all the user experience, I would say, foundation and capabilities could be prompted.

So you don't need to build like a very structured kind of one-trick pony product.

Once we created this data foundation and 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 dashboard, we can prompt workflows, as opposed to people starting with the product experience, which is then hard-coded 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.

So we actually focused on the data foundation, data quality, which is, I think, something that even the lab companies, which of course they're doing an amazing job, but that specific needs that finance have to be able to look at all the data across systems in order to generate the right integrity and accuracy is something that we have as a differentiator.

And that gives us the unique mode to be able to meet the expectations of finance people around accuracy and integrity.

First of all, from the get-go, we started to work with customers and work on real data in order to be able... So you can't build that product in a lab.

You have to build it with real data.

Once we did that and we started to create point solutions for customers,

then you start to hear the same tune over and over again.

And a very prominent part of that tune was our close process is taking too long.

It's taking too much time.

Why it's taking too much time?

Because we have these 200 work papers we need to generate.

We have all these processes that we need to complete.

And you start to hear that over and over again.

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