Pablo Palafox

We talked about these use cases with one of our largest supply chain companies and customers where we need to call customers to recover duties on parcels.

And today we're running campaigns of 20,000 to 50,000 daily outreach to customers, collecting duties on parcels that otherwise they would not get if they don't pay the duty on the parcel.

So that's sort of...

surprises, if you will, we've gotten from customers.

Like, yeah, I also need to recruit drivers.

Can you do that?

And we obviously can build an agent that not only just recruits drivers, actually connects to the operation so that now they know they can service a truck with a customer earlier because now they have a driver to move that.

So there's all sorts of interesting connections between the functions.

Maybe I'll give you another example.

we built an agent to reach out to maintenance shops to see where a truck or when a truck was ready.

You could just leave that agent in a silo and just have an agent that is practically reaching out to those repair shops to see when the truck is ready.

Well, it turns out that the sooner you know when the truck is ready, the sooner you can put it in the market to sell it as capacity for your customers to actually move things.

So that was a very interesting realization of how

sales in this case, and maintenance were tightly connected.

So that is the context that we talk about.

There has to be an underlying context sharing across the different functions in a business so that the whole business optimizes for a global maxima, if you will, or a global minima, depending on what optimization problem you're trying to run, versus just minimizing the

probably in one function, if that makes sense.

Maybe let's start in the beginning.

I was the first forward-deployed engineer without knowing it, I guess.

Which is pretty much what any founder would do.