Ahikam Kaufman

And on top of that, we augment the results with rules that allow us to make sure that we understand that the results are on track.

And if something is not on track, because all the enterprise data is very structured, I can understand the outcome based on the various data points I'm looking at.

So I can understand, for example, that let's say I'm pulling a data point from a contract and I now compare it to your billing and compare it to the LP.

I can see in various places how that data was captured and whether that amount makes sense or not.

So whatever the AI is pulling out and decides whether there is like an issue that needs to be, I can check it with other sources of data in the enterprise.

So because the data is structured, then I

I can augment the results with rules and checkpoints to make sure that it resonates or it doesn't resonate.

Yeah, we crossed a million dollars of ARR.

We're about at like 1.5.

Again, we just started to sell last year.

Every engagement is kind of significant.

And yeah, we would like to go from here.

I think 2026 is going to be the day year for AI in the enterprise.

I think we can triple our business by the end of this year.

Right.

So we actually, this is like feedback at the time we heard from one of the largest VC funds on the planet where they said that

In order to allow AI to run successfully in the enterprise, you have to provide him with reliable data.

We have created unique technology, exactly like I explained, like creating the foundation of data.

So we created a unique, we built this unique graph technology, allowing us to pull all the data from the various sources and normalize the data in a way that allows AI to operate across all the data.

You can't just run AI on each and every system in the office of the CFO.