Yes.

Yeah.

And again, I think if you're having this conversation about trajectory of the business and the overall expectations, this should kind of come naturally at the end of the day.

One other point to make, there's other structures out there just to make you aware of that I've seen.

And to me, it kind of comes down to the maturity of the business.

If you're in a more mature business, so a lot of larger firms, I think use this structure is there's some firms that will walk a management team through an investment case before the deal closes and actually get them to sign up to performance hurdles for that business.

So saying we expect X percent growth, this amount of EBITDA each year, and then kind of set parameters over those years.

don't have that story, that's like, well,

I'd love to hear like how you've approached that.

Yeah, so the biggest problem with top-down AI requirement or top-down AI push is that reps will be enabled on it but then don't know how to fit it into their daily schedule.

And I think it's easy for C-suite to wax poetic about everybody needs to be building in these tools and everyone needs to be adopting them all the time.

when you have like a three hour block on a Tuesday afternoon to play around with this stuff.

But if you're a rep on my team, you have like four or five demos a day, you're running POCs, then you have to send your next step emails and you're just like your day is full.

Like if you look at my reps calendars, their day is full.

And so the way you get ai to be adopted within the team is you find very specific pain points that they're facing and you build agents that solve those specific pain points so the way we've done it with dust is you have central um kind of hierarchical agents so a go-to-market agent a marketing agent a account management agent And those agents then have sub-agents.

And how has that changed over with AI and kind of, because I feel like you were at the forefront of rethinking the structure of your team.

I'd love if you could expand on that a little bit before we kind of dive into the details of what you've been doing.

Yeah, absolutely.

So, you know, one of the prevalent notions is that a rep spends 25 to 30% of her time selling.

And our notion was how do we double that and spend more time on the phone? And so the way in which we approach the problem has been at every step in the funnel in which a rep is spending a lot of human hours doing something that's not uniquely human, like updating CRM, sending multi-threading emails, updating next steps, building business cases.

There's a long list of these things, building QBRs on behalf of customer relationships.

how can we leverage AI to solve very discrete problems on behalf of the seller? And how do we get the seller bought into the power of AI at solving these problems? And so the way in which we did it, we partnered with a couple of different tools on the CRM side, using momentum to automatically update next steps and a lot of other fields required to sell our product.

You know, with our product, it's, There's a lot of questions to ask.

So first, what do they do and what was the problem that you guys were trying to solve with Dust?

When do you hire a banker?

Yeah.

Th- if you have time and you wanna get ready.

If you're, it's, it's, um, if you're, if, if the actual buyer is not obvious, why not take the time and get some help? Uh, there's, those processes take...

If you're gonna contact multiple buyers, there's just a ton of...

It's comes down to preparation, organization, and project management at the end of the day, up to a point.

Um, so in those situations, you hire a banker, you get somewhat...

Uh, can't you just use the one you did five years ago and, and, and put that in the data room?

We are doing one right now.

Yeah.

We're usually [laughs] we're usually, um, getting an Excel file that they've taken a shot at, but then contract dates and all these things don't line up, so we're, like, doing a bunch of work on it.

But, um, reading between the lines on the LOI, you know, little phrases like, "Subject to the information we've seen to date," or, you know, things, things like that.

Um, as the seller, you know what information they have, right? There's...

It gets into the, like, the lemons prob- Like, you know everything.

They don't know everything, or they know what you've given them and that type of thing.

Is that fair?

We thought we could take advantage of our data to make kind of more informed real-time decisions.

Makes sense.

So, yeah, like you said earlier, I don't want the integration there to seem or feel straightforward or easy either, but a little smaller, less customers than kind of the overall business we had done before.

So now we've made our sandbox bigger.

Now the world that we can go sell after has expanded significantly.

But then really what I would say, once we got that acquisition kind of done and on its way, we did take a step back and take a look at the products that are kind of in our bag today.

And really another company that we had identified early in the investment as one that would be a perfect marriage for this, you know, kind of overall open brand group was a company called TrackLine.

So there's just lots of real tangible benefit we're getting out of these LLMs.