Brynn Gibbs

I mean, we'll, I think we're going to hit just under 4 million.

And so.

I know.

I just, I don't, I don't like to boast.

I don't know.

I also don't like people knowing all of our business.

We'll be right under that.

We're projecting that.

Yeah.

2.9.

So this is incredible.

Thanks.

So it's the AI tool that I mentioned.

So we have years worth of data.

So every time we would work on disputing and getting a negative review removed, we logged the directory it was from, the reason why it was successfully removed.

And after eight years, we finally had enough data compiled to where we could develop an AI tool, did heavy machine learning.

So we can plug in the content of a review through our back end, like when we pull in...

our clients' new reviews, it'll automatically go through there and it will put out a rating of the percentage of the review likelihood that it's able to get removed.

Anything above 50% has a good chance at being removed.

So now this will quickly identify which reviews our team needs to target versus ones that would be more of a waste of time.