Building AI Employees for Hospitality: How AITropos Takes Orders Where Customers Already Are

Welcome to Just Now Possible with Teresa

Taus.

>> Hi, my name is Santi Maruri. I am the

CEO at Itropos and I'm an engineer MBA.

I've been doing product software product

for the past 10 years and I'm an AI

fanatic. Absolute fanatic.

>> Love it.

>> I'm Juan Ao. I'm CTO at ITropos. I'm a

software developer. I've been doing

software development since the age of

around seven or eight. I'm 42 now. I got

my first computer when I was a little

kid and never stopped getting into it.

And with that in mind, I've always been

working with cutting edge technology,

always looking for the latest things. Of

course, AI excited me as soon as it

showed up. I have a degree in data

science and yeah I have a lot of

experience more than 15 years of

experience in hospitality software for

hotels and restaurants been working on

that. Yeah that's how we got where we

are now.

>> Amazing. One of my questions I want to

dig into is how why hospitality and how

you found this space. But before we get

there tell me a little bit about what

does atropose do?

Yes. So, we're actually building AI

employees for the hospitality industry

and we're trying to generate real

operational impact, right? So, it's not

just a bot, not just a chatbot, but it

has a lot of tools and we have many

integrations so that we can get done

real operational work. That's what we're

doing. We're addressing restaurants and

hotels mainly, but we also have bakeries

as customers. We're looking into bars

and every hospitality business can use

our AI employees basically.

Yeah, I love that. Okay. So, I we've had

actually quite a few companies on the

podcast where they're basically creating

AI employees in one way or another,

whether that's like customer service

agents or one of our recent episodes was

with a company that's like creating

agents to help with managing clinical

trials. I think this is a very hot space

right now because AI is capable of so

much, but it's also a little bit

>> of a tricky space, right? There's a lot

of fear around our jobs going away. Um,

and I think especially in the

hospitality industry, I could see there

being these are the types of businesses

where it's probably hard to find good

employees, especially if we're talking

about bakeries and restaurants, the

owners are probably tapped out and need

help. How do you think about this

balance of what's good for AI, what's to

do, what's good for humans to do. I

think especially in hospitality,

I think of hospitality and I im I

immediately think service. Like when I

show up to a hotel, I like having a

human welcome me.

>> Do you want to just do you want to just

tackle some of this? Like how do you

think of these hard challenges?

>> Yeah. Know and that's an amazing point.

And of course the AI employees are not

for every type of hospitality business,

but there are for there are super useful

for a lot of services. For example, we

are one one of our niches is QSR, quick

service restaurants, right? Because in

those cases, you don't go for the human

attention. You just go for food.

>> Yeah.

>> And the same thing applies for for

hotels. There's some hotels that of

course you have to have the human

attention, the human touch and there's

other hotels that you just want to get

some room service or that you just want

to schedule the taxi for to to the

airport. So that's where we believe

those are our targets. They are super

distinguished and the customer, the

restaurants and the business owners

really understand when these type of

services are useful for customers and

when they might uh need the human touch.

>> Yeah. If I'm sitting by the pool and I

need a margarita, I'm okay with ordering

from an AI. to your point of where it

impacts on places where this this

technology can be used. The places we're

looking to do it is not replacing where

a human should be but where the

technology is in the middle in between

humans. So basically instead of having a

human to human conversation through a

platform we know that we can replace

that interaction with human to agent or

human to AI through that same platform.

Right? So even more so some other places

where we think we can be very

competitive is where technology even

doesn't have a person on the other side.

So for example when you're using an app

we are basically are focusing on the

conversational interface uh rather than

the app interface.

>> Yeah.

>> Yeah. And maybe just to finish wrapping

up the idea our product of course we

market our product as AI employees. Erh,

we're even thinking about changing that

because AI is not our product. Our

product is delivering a high quality uh

service for guests and restaurant

customers and we actually do that of

course with a lot of AI but sometimes we

need humans to jump in, right? So our

main focus is to deliver an amazing

experience and one of our goals is to

pass the train test. The idea is to make

customers feel like they are speaking or

interacting with a human and we are

achieving that in many cases. Many cases

people the final consumers thank us and

even send us pictures food pictures to

thank us and they they think that they

explicitly think about how the attention

was super close.

>> You like this framing? It's interesting.

Let me back up. I can see clearly like

in hotels there's a lot of rules where I

could see your service being really

helpful. You already mentioned room

service. I joked about the margarita by

the pool, but anybody who's been to a

resort that's busy has had this

experience of you just can't even find a

human. Your example of a walk up

restaurant makes a ton of sense to me.

I'm curious about the restaurant

category in particular. You mentioned a

bakery. These are more experiences where

I feel like we're used to talking to a

human. And I know like here in the US

during COVID, a lot of our restaurants

move to QR code menus to reduce human

contact. But we're seeing at least where

I live, we're seeing all of that go

away. Like people want to connect with

humans. And but I also know restaurant

owners that can't find good employees.

they are strug they're like working two

full-time jobs and still struggling to

run the business. Tell me a little bit

about where do you see this playing a

role in restaurants just so I can get a

clearer picture of what types of roles

your product is filling.

>> Yeah. So let's jump right into a

specific example.

>> Yeah.

>> Let's say McDonald's.

you today you have to either order

through the kiosk or wait in line to be

served by a human.

>> Imagine if you were able to just get

into the McDonald's, take a seat and

order through a voice message on your

phone. And of course, the employee will

tell you when your food is done so that

you can pick it up at the counter or if

the restaurant can have a runners, they

will take you the food to your place to

your to the table where you're seated.

That's one of the main use cases that

we're aiming for.

>> Yeah, I could see this thing really

powerful. Like what im immediately came

to mind is my town has a big concert

venue in outdoor amphitheater and I

would love to be able to just order a

beer

>> there

>> and have it come to me rather than

walking to the tent and standing in

line. Yeah. Amazing. Okay.

>> Line there's a huge potential for AI

employees to taking care of their

orders.

>> Yeah, that's a great way to think about

it is why do we stand in line?

>> Correct.

All right, Juan, I want to go back to

something you said in your intro. You

said you've been in the hospitality

industry for 15 years. Is this how the

two of you landed in this space? Tell me

a little bit about how you found this as

the area you wanted to work in.

>> Yeah, actually we met with Santi, a

previous company we were working on that

was mostly related to marketing but did

marketing for hospitality basically for

hotels. Um but yeah, I got into that

company because of my experience also

working with hospitality systems. So

basically I work with the company here

in Argentina for around 15 years.

Actually it's more I always say 15

because I'm used to using telling that

but it's five years ago was 15. So we

could say that it was and I'm still

sometimes working with them like I do

consulting for them. So that that kind

of still there. But this company

basically builds one of the PMS

softwares, property management software

system, which is what hotels use for for

their operations. It's one of the one of

the system that has the biggest market

share in Argentina. And they also have

of course a POS system which is for

restaurants. So I've been working with

those systems for a lot of time for all

of these 15 years that I mentioned and

and that got me into kind of

understanding how the business or that

market works. So after meeting with

Santi and working on all of this of

course it was mostly

it went on its own decanting on that

specific market because of my

experience. Santi I know he can tell but

he's also been working a lot on that on

the industry as well. Previously he's

been working on another companies. So we

both know not had we both had the

knowledge uh maybe from different areas

but totally related both super nerdy

about the cutting edge technology super

nerdy about AI and so it was a

no-brainer to get started with this. Of

course, there were just because of the

contacts we had or what we've been

working on for these a lot of years. We

had a lot of easier entrance into the

market than if we had thought about I

don't know going into I don't know

automotive automobile market which I

know nothing about cars.

>> Yeah. So it sounds like you both had a

lot of domain expertise in this area.

you were geeking out on the technology

and just excited to play in this space.

One thing that's interesting to me is

you have a very broad problem space. So,

hotels have a lot of employees. They

have a lot of use cases. Restaurants

have a lot of employees, a lot of use

cases. Tell me a little bit about how

did you decide what to do first?

That's definitely a great question and

we did decide to work in the hospitality

industry but actually before that with

Juan we spent two years meeting with

industry experts and analyzing hundreds

of different ideas and we finally met

someone with a lot of domain expertise

in the restaurants area in the

restaurants industry and that's when we

realized Ed that there is one specific

use case, one specific feature that can

unlock a an immense potential which is

order taking. There's lots of companies,

lots of people helping with chat bots,

providing information etc etc. But

there's this use this specific use case

which is order taking orders which is

super hard and super valuable. So

actually we do have different employees

but our main feature is being able to

take orders and that's how we actually

decided about it. We actually started

doing a an assistant for waiters. That

was the first thing that we started

building. We literally spent six months

working on a device on a physical device

that would waiters would use and while

working in that we realized that taking

orders was the hardest part. So we said

okay let's focus on this and then when

we started offering this solution to

different restaurants to different

potential customers they started asking

for this service to be uh deployed in

for customers directly and that's how we

found out that there was huge potential

for a solution like this for customers

so we pivoted and we started focusing

specifically on that

>> and were you using AI already at that

time.

>> I can't remember a time when we didn't

use AI.

>> Yeah. Okay.

>> Anymore. I don't even want to think

about that. Gives me the chills.

>> Yeah. I think if you're familiar with

the product market fit question of how

disappointed would you be if this

product went away? I feel like AI for

people that have embraced it, like

disappointed is the wrong word. Like how

devastating would it be if this

technology went away? I think I wouldn't

be able to breathe.

>> Yeah, I know. Sometimes I wake up and

Anthropic has downtime and I'm like, how

do I do my job today?

>> Tony, we had a few episodes.

>> That happens to us. Well, we when we run

out of credits, for example, we use AI a

lot to code, as you might imagine, that

helps us move faster. And as soon as we

run out of credits, it's oh, what do I

do now? Do I have to code manually? No.

No.

>> Yeah. That's usually when I eat a meal.

I'm like, just step away from the

computer, go have a meal, go outside.

>> Yeah. Yeah. And it's fun because we

literally are the first ones to find out

when some of the LLM is not working. We

jump to X and there's nothing there.

Like silence, five minutes later, a

thousand tweets.

>> Yeah. Okay. So, you know what I really

like about your story is you clearly had

domain expertise. You still took a lot

of time to figure out the right problems

to solve. You found an area that was a

little bit com that that looked

promising. You started to build in that

space. Your customers, it sounds like,

pulled you even further and forget

waiters, do this for our customers.

>> Santi, you said you spent two years

looking for problems to solve. Was that

full-time? Were you both working

somewhere else? Tell me a little bit

about that exploration space. Yeah, and

two years is an understatement to be

quite honest. I spent the last 20 years

thinking about startup ideas, right? But

the two years was specifically related

to Huanu and myself, both of us

together. When as soon as we met,

>> we started enjoying very much our

conversations and started thinking about

so many ideas that we could build.

Actually just a very quick uh we we love

both of us love astronomy and at some

point we fantasize with building a

company that is called TAS which was

telescopes as a service.

>> Nice.

>> We were trying our idea was to use

SpaceX to h put in orbit a telescope and

lease the time of the telescope. So

that's how we literally spent a lot of

time together thinking about ideas. We

were working full-time. So, yeah, we did

it on our spare time.

>> Yeah. I hope you someday also make that

telescope company. I think that would be

fun.

>> Yeah, absolutely.

>> Okay, so let's get into this a little

bit. You got pulled into you were first

making software for the waiter to make

it easier to take orders. This got

pushed into can we just give it to the

customer. The first thing I love is that

this is a very specific use case. You're

not looking at a hotel and saying,

"Let's do all their jobs for them."

You're saying, "Let's take orders." The

other thing I love about this is Santi,

you mentioned this was a hard problem

and I can imagine it's not you're not

just spinning up a knowledge base and

being an answer bot. You've got to

integrate with point of service system,

point of sales systems. I'm imagining

you're interfacing somehow with a

kitchen that maybe is making food or

something real in the physical world

where that order is turning into

something real. So give me a sense of

what does it take to solve a problem

like this? What's the big picture?

>> Yeah. So the connections, the

integrations, all of that, although they

are quite complicated, those are not the

hardest part.

The hardest part is to being able to

translate the not deterministic world of

human conversations and LLM's into a

structured information so that you can

feed that to systems. That is one of the

hardest parts, right? So that's what

took us a long time. It's you can do a

prototype in a day. Anybody that uses AI

can do a prototype in a in a day, but

making sure that this is consistent and

that works every time takes a lot of

time. That's a one of the things that we

we realized when we started working. And

how did we solve that?

Putting a lot of hours. putting a lot of

hours understanding and making and

making a an architecture that is super

advanced. I'm gonna let Huanu speak more

about all the architecture that these

agents are using. They are not a simple

h a simple prototype that you can build

in a day.

>> Let me make sure I understand where you

said the challenge was. So there's first

you mentioned the non-deterministic

human, which I love this because we all

talk about the non-deterministic LLM,

but it turns out humans are also

non-deterministic. Yeah.

>> And there's this like first layer of I'm

assuming this is a chat interface. I can

say anything.

>> Yeah.

>> Yeah. You connect it to WhatsApp.

>> Okay. So that's the first challenge of

the user can enter anything.

>> Yeah.

>> Yeah. The platform is actually channel

agnostic. So basically we started with

WhatsApp here in Latin America is like

the OS the operative system of Latin

America that's what we use but we could

easily connect to iMessage SMS any other

channels and it's part of our road map

as well and it's the easiest thing to

connect for us. We're just using

WhatsApp right now because of that. But

one thing I wanted to mention regarding

what Santi said about the challenges

was all of the things that Santi said,

not only they had to be correctly done,

but as you might imagine, since you're

doing realtime order taking, the agent

has to be fast and responsive and

respond correctly while in a time

fashion where it doesn't get the

customer waiting. As you might imagine,

agents as agents today are mostly used

for longunning tasks, right? And that

takes a lot of time to process

information. So, one of the biggest

challenges and I had Salty like here on

my ear constantly. We need to look I

remember I I don't know if you saw the

playlist series based on the sp how

Spotify was built. So,

>> okay. So the playlist is a series where

it shows how Spotify was built from the

different perspective of the builders.

And one of the things you could see is

how the person had the idea, I can't

remember his name, constantly had the

technical guy saying faster. I need

songs to load faster. No, the time

between a song and another song has to

be faster, faster. That's how I felt

with Santi on my side. But that paid off

because we're actually right now at a

place where like Santi said, people are

not noticing uh they're chatting with an

agent. Not only because of the response,

the way that the agent responds, but

also because it responds not too fast,

but not too slow either. And that's kind

of part of the challenges that we were

trying to solve. I love that you

mentioned not too fast as part of the

challenge because I know like when I

send a support email and I get a really

detailed response one second later I'm

like yeah that was an AI.

Okay. So it seems like there's this

first layer of the human gets to enter

whatever they want. So you got to deal

with the messy of the messiness of this.

You have an agent that is trying to

understand that message. I imagine your

agent is doing the heavy lifting of

interacting, integrating, like

structuring that input in a way that

works with your now deterministic

systems, point of sale, whatever. So,

give me a sense of I want to go back to

like day one. What was your first

prototype? How did this start? How did

you even evaluate if AI could do any of

this?

Yeah, I that's you can start but one

thing I want to say is that the core

piece was always the same one which was

basically had an integration with this

external system that was something that

remained along these different

iterations but I let Santi talk about

the first product we built that we

iterated actually we had uh two I think

we're on the third iteration right now

right Santi we have first the hardware

then we have the custom app for waiters.

And now we're actually at the uh the

end.

>> Yep. Yeah. I can't remember how many

iterations we've done on this product to

be honest.

But yes, so we always we were super

optimistic about this, Teresa. We were

super optimistic. We saw the potential

of AI and we actually never thought that

this couldn't be done. I think it was

our our determination to make this work

which made us push very hard and again

the first time you one of the best

things about AI is that it gives you

some very quick dopamine hits because

making a prototype it's awesome. It's

awesome. But that is good and bad at the

same time because you have no freaking

idea what you are what you're starting

to do, what you are getting into. You

have no idea before you start, but it

gives you this dopamine heat and it's

like you feel a superhum and you're

convinced that you can do it. That was

where we were. But I honestly had some

doubts along the progress.

We spent a few months working on it and

we still had a an unacceptable error

rate because we wanted to make this

perfect and that's when we started yeah

testing different ideas playing around

with so many tools so many different

agentic architectures

we have five different types of ag rack

with different It's it got complicated

at some point. We started thinking about

the physics and how it should evolve etc

etc but yeah it was super super hard to

to be able to really to understand every

time what the customer is ordering

especially in especially in different

restaurants that probably have products

that are quite similar. So if you fit

that to a prototype you're done. That's

when you say, "Okay, this is might be

harder than it looks." But yeah, but it

was a hard process, but we're right now

it's working so good that we are very

proud of what we built.

>> Yeah. One one thing to mention is, and

this is anecdotal, but like Santi said,

sometimes you could be like overwhelmed

at things not working and like you start

having questions. like something said

like we we never thought that this

couldn't be done. We just were thinking

whether we were at the right time and I

had this memory I have this like

snapshot of a chat I was having with

Santi chatting with him where we were

doing this uh second iteration. So the

first iteration was a hardware that was

the idea was that kind of like having a

headset for waiters where they would

just talk to an agent, the agent would

help them etc. That was super hard not

only because of the model but also

mostly because of the hardware build.

Second one was something similar but on

a on an app where it was mostly like a

chat app and then the waiter would just

make the order take the order from the

customer make the order on the app and

then generate the order and send it to

the POS. Like I said all iterations had

the core idea of integrating with the

system. Now this third one is with the

chat. So when we were at the second

iteration, we were trying to get our

agent or agents to build an order right

with uh orders are super complex objects

in data terms because you have the

product the product can have a variation

in the recipe. The product can have a

something called a modifier which is

like large small. The product can have

extra products linked to it. So you

could have like a promotion. If you buy

two products separately, they have a one

price, but if you buy them together,

they have another price. And depending

on which POS you have, all POS have uh

different data structure. And that's

actually a regular problem of POS

system. Like

I tend to think that POSOS systems are

still an unsolved problem because they

all have different ways. Each restaurant

or each venue has a lot of each of their

own different ways of doing things each.

So they all have in the end to ask for

specific custom implementations to the

so to the software that the company that

developed the software. So there's a lot

of as you might imagine there's a lot of

variance and working on implementation

on integration with all of them for us

it's super hard but that wasn't the

hardest part. So when we were doing this

second iteration and we couldn't get the

agent to correctly build this system, I

was like Santi remember just a message

from Santi Hanu I'm not sure can we do

this it's not fully working as expected

and then I was like trust Santi we're

going to make it uh and in the end it

turns out that it's about not giving up

because the only limitation is the

technology I remember having like I was

saying is most of the times is basically

trusting your product and understanding

whether the problem is that can it be

done or not and if you think it can be

done what's keeping you from it and it's

most of the times is if it's the

technology that's limiting you is

whether do you think the technology will

be there at some point or is it like a

physical limitation I think even Ilan

Musk works with this idea or whether if

it's not allowed by physics then it's

can't it can't be done but if physics

allow it that you can do it we're super

far away from that. But I was I remember

having Santi sending me this message

saying Juano I don't know if we can do

this you it's not working as expected

we're taking a lot of time and I was

like let me see Santi trust trust we can

do it and just that exact same date one

of these companies that we use for model

released a news model smarter and it was

just a matter of let me try with this

and I just switched the model and it

started working like without even

changing anything from our side. Of

course, there were a lot of nuances that

we then fixed and improved and we

constantly keep doing that. But it was

just a matter of waiting for the

improvement on the technology, the base

technology that we were using to have

our product fully up and running. And

that's when we said, "Yes, this can be

done." And the fact that we actually got

to the point where the model that we

needed showed up for us to get moving

faster or actually get moving made it,

okay, man, we're on the right track and

we are early.

>> Yeah, this is I love this because I

think I think it was Andrew Karpathy

said it might it was either Andrew

Karpathy or it might have been Boris

Churnney from Anthropic said to build

your product for the model that's coming

out six months from now. And what's fun

about this space is that like you build

something and then time passes and even

if you do nothing, your product gets

better. Like the brain behind your

product gets better. And it's so cool to

see what this unlocks. Like I had this

experience myself with the product that

I'm building. I was like prototyping in

clawed code and Opus 4.6 six was doing

the heavy lifting and Opus was great but

Opus is very expensive and you cannot

put that in a production product and so

I like switched to Sonnet I started to

play with Haiku it was okay and I was

like that's okay time will pass we will

get opus level brain at ha coup prices

keep going and I feel like this is a

brand new part of building products like

I can't really think of anything else

where this has been true where like you

really can start to see the future and

build right on the edge of that future

which is really fun.

>> Yeah, for sure. The only caveat I would

say is that you got to work Yes, you got

to work on the thing that is coming. But

you have to make sure that you build it

correctly because if you only expect

your product to get better just because

of the model, that's fine. But that's

just adding steroids to something that

if your product is not as performant or

as good, maybe in the end it will fail

because maybe not because it's not

working but maybe because of costs as

you just said like intelligent smart

models are expensive. So if you only

wait for super smart models to come out

and you just depend on that it's going

to super expensive. You still have to

make a lot of engineering and

architectural decisions within your

product to make sure that your product

should and it does the right thing

internally to avoid these other problems

that might have.

>> Absolutely. And of course there's the

hard challenge of how do you know what

the model's going to get good at and

what to delay for later versus what to

work on for now. Okay, let's get a

little bit into give me the high level.

You've mentioned agents a few times.

walk me through. I'm a customer. I'm

trying to place an order. What happens

next?

>> So, basically, you just open up. In our

case, we're just going to talk with what

we currently have. You just go to your

WhatsApp, which is basically something

you have installed. That's one of the

great things about this approach that

we're taking is that no one has to

install anything at all. They just have

to use whatever they use. Uh, and then

you basically go to the contact that

represents the restaurant and you start,

hey, I want to make an order and the

agent starts talking to you and also

guides you on the way on what you want.

It even does recommendations for you. It

if you have some sort of idea of what

you want to eat, it recommends based on

your ideas. It tries to also match

products that you like with other things

that could go well with that. Of course,

there's a lot of marketing from the

venue side that they, hey, I want you to

offer this when they ask for this other

stuff. All of that kind of things that a

restaurant or a venue would actually

train an employee to do. Our agent can

do it also. The good thing is that the

agent doesn't get tired, doesn't get

stressed, they're never depressed, etc.

All those benefits. But from there

basically the or the as the user and the

agent chats basically the agent the

steps that it takes it starts building

an order with the products that are

being add that are being requested sets

like we mentioned the modifiers comments

variations on the recipes

there's a lot of internal processing on

because we have many features internally

while that happens which are like when

you add a product make sure that it has

stopped stock. Uh when you add the

product, make sure that it has stock,

but also you need to check whether the

order is for today or is programming

scheduling for another date. So if

you're scheduling for another day, you

have to make sure that stock it will be

available for that day. And so there's a

lot of these rules that I like I was

mentioning have to happen at the same

time in parallel most of the times to

make sure that we can reply in a

fashion. Once you do that, you build the

order. The customer is happy with their

order. Then the agent basically asks

whether it's for takeaway or for

delivery. And it's for takeaway, they

just ask for the name. If it's for

delivery, they ask for an address. And

we check the availability region for

delivery. So the agent can also know

whether they can deliver to that address

or not. And this cannot happen actually

in the middle of the conversation. It's

not something switched to when the order

is finalized. It's like when you talk to

a person, you just, hey, do you guys do

deliver to this area? Yes, we do. No, we

don't. So maybe even if the order is not

set, it can answer that question. Uh

sometimes even our agent is used for

asking questions about venue, not really

ordering, which also works. So that's

standard use for agents. And so once you

have the order and you have the delivery

method whether it's pickup, takeaway or

delivery, the agent basically closes the

order and generates a payment link which

is also sent through WhatsApp. And this

payment link basically the person just

clicks on it, goes to whatever payment

platform the customer use, they make the

payment and then on WhatsApp they get a

message, hey we receive your payment,

your order is being processed. So after

that the c the venue start preparing the

order which also triggers a notification

to the customer on their messaging

saying hey your order is being start

started working it will be around ready

in around 25 to 50 minutes whatever that

time is and if it's for delivery you

will tell them hey your order is ready

and it's out for delivery to your place

or your order is ready you can come and

pick it up and so basically as you can

see this is what currently delivery apps

are doing, but in a way that the person

doesn't have to leave their their happy

their safe place, right? Which is their

messaging app. They don't have to go to

external apps. Here in Argentina, we

have these two apps that are for order

delivering. I know in the US, you have

Door Dash and even some some venues have

their own custom app for order ordering,

which is a mess. As you may imagine, you

have to install external apps just for a

single venue. So, what we're trying to

do is like Santi said is just improve

the experience of the person when taking

orders by not leaving their comfort zone

so to speak for on their phone. And

that's basically it. We have the I think

the only time they leave the app is when

they actually just click the payment

link which takes them to whatever

payment platform it is and then that's

it. the remaining of the experience is

fully integrated into their chat

experience. It even tells you when your

delivery guy is at your door.

>> Help me understand this in the context

of let's say I'm ordering like food for

takeaway.

Am I going to the restaurant website to

see the menu, but then I'm ordering

through WhatsApp? Like I can imagine if

I'm at a hotel, I have a room service

menu in my room. I'm looking at that.

I'm using WhatsApp to order. But is that

the idea? like instead of I'm still

looking at the restaurant menu online

and then using WhatsApp to order.

>> That depends a lot on the user. The user

can either go to the website to see

previously what there is for to then go

and order or they can ask the agent to

either recommend them, tell them what's

in stock, or the agent can even send. So

our agent actually can send attachments.

So they can send you a PDF with the

menu. They can send you pictures of the

food of course as long as they are set

up on the system. But so the full

experience can h experience can happen

on the app

>> or they could just go to the website and

see the menu. That's up to the customer.

We don't force anything on that end

>> where the customer gets the what the

number to speak with our agent depends

very much on the business. There are

some businesses you just mentioned that

you have you can have the QR code on the

on on the room in a hotel

>> or you can have the QR code printed out

on a table in McDonald's for example or

you can get the number from the website

or even you if you're a recurring

customer you have it you already have

the connection so you just have to go to

whatever messaging platform you're using

and and search the name of that

restaurant. I can imagine too for like

delivery, people have their go-to spots.

Like I know exactly what I want to order

from a specific restaurant, so I don't

need to look at a menu. And I can see

that being a really powerful use case as

well.

>> Correct.

>> And I love that it's through WhatsApp

because I don't

>> I definitely don't want to call a

restaurant ever. I'm not a millennial,

but I feel like I have that millennial

trait. I just don't want to call

somebody. And I also I want to see I

want feedback that my order is correct.

I hate placing an order on the phone. I

don't really trust that they got my

order correct. And I really think the

world should just operate over text. So

I think this is amazing.

>> It's basically text, right? We just have

a technology that basically converts the

audio into text. But the idea is giving

the full conversational experience of

DMing through WhatsApp you have with

your friends. So whether you want to

chat or just send an audio message,

which is right.

>> Yeah. Okay. So

>> you you would clearly be one a good

customer, a customer that uses this

tool.

>> I would be I wish all things could be

ordered via text. Okay. Let's get under

the hood a little bit. It sounds like

there's a lot you're orchestrating

behind the scenes, whether it's checking

delivery zones, checking stock, making

sure you have all the right data for the

point of sale system. How does this what

does this look like under the hood?

>> Good question. I don't know if you want

to tell your secrets. No, just kidding.

No. Okay. These are different. So,

basically what's going on under the hood

is just to give a quick sample is

basically we have our system just

receives a web hook or a call from

whatever API we use to message for the

messaging channels. In this case,

WhatsApp receives a message and from

there starts a full pipeline that does a

lot of things.

The main challenge was so we actually

the first thing we had was that the

pipeline was straightforward like one

thing after the other right just to make

sure that things worked but on upon

iterations on that and we started saying

okay we need to shrink down the times I

think the biggest challenge was actually

figuring out which parts for example

could be parallelized right so

>> you got do we talk directly with the

POSOS as we build the order or do we

build an external system that takes the

order, which is much faster. Of course,

we the first time we just went through

the integration part. Next iteration was

a no-brainer. Let's just do everything

inside our app and then send to POSOS or

the integration. The next one was how

many things can we do in parallel that

can be done in parallel that doesn't

need a sequential sequential processing.

Right? So for example, if you're

ordering for if you're searching or

ordering for multiple products, the

agent can basically search for all those

products at the same time and then build

a response based on all the results,

right? So instead of searching one by

one, you do a lot of multiple searches

at the same time. Then the other thing

is like database the database the

basically the database infrastructure

how powerful is the database or the

database choice that you use so that it

actually has quick results and ordering.

Uh but all of that is basically

architecturing different ways of

treating the data and parallelization

caching database infrastructure database

engine of course yeah for different

things. So all of those things had to be

considered at the same time which is I

think the hardest part but in the end

what happens is that the agent in our

scenario we're using tools and we

decided to use agent tools rather than

MCP or rag because it's the fastest most

efficient way for the agent to interact

with MCP it would have to basically go

through the MCP understand what's going

on make a request to an endpoint to

which could potentially be fast But

still it's one extra step with tools.

It's basically okay call this function

and the tools are already on the prompt

of the agent. Now we do some rag some

initial rag retrie augmented generation

for knowledge bases or if we want to

preload information sometimes.

So we have these hacks that we've been

implementing where for example our

system prompt is built based on the last

message and the previous messages. So we

got two system prompts. One is like the

main system prompt and then we have

something called like updated system

prompt which is like an immediate system

prompt that the agent gets to know more

information about what's going on but

from the system role and that basically

builds the prompt based on okay so

you're looking for this product so let's

quickly build into that small system

prompt information about that product

before the agent can respond so that it

doesn't have to figure out a tool to use

and search for it. We just fitted that

information right away because we have

to figure that out. Right? So all of

this all of the like I was saying it's

mostly architecturing engineering but

just coming up with good ideas on how

can you resolve these problems. Hey,

it's taking too much time on a database

call. Okay, can we do this faster?

Figure it out on our own

programmatically instead of having the

agent figure it out and figure out which

tool to call. So

>> yeah.

>> Okay. So it sounds like you have you

started with the pipeline which I can

imagine first of all a lot of people on

this podcast talk about they start with

the pipeline. You have confidence it's

going to work. You control more of the

process. It's a little more

deterministic. I can imagine in this use

case though for the customer it feels

like they're on rails. It's not a like

open conversation where anything goes.

It's like the agent is guiding them

through their order taking. Whereas one

benefit I could see you getting from

shifting to a agent plus tools

architecture is the customer can drive

the conversation a little bit more. Is

that what you found?

>> Yes. So

we started using tools in general like

at the first moment. So we consider MCP

and workflows such as I don't know other

workflows with pipeline but tools was

the no-brainer for me and to get started

because we did the previous analysis and

then we saw that it was the fastest now

I think that's more what you're

mentioning is more of an emergence an

emergent property of the fact that we

decided tools this was already happening

right the agent has access to all these

tools at some point I did we did think

about using state just giving state to

the agent so that it knows okay now

right now it's just receiving the a the

or greeting the user now it's building

the order okay now the order is built so

the problem with that is that it would

happen what you just said they would you

would be having the customer on rails

and not giving them that freedom of

picking hey wait so actually there's

actually something a workflow that I can

mention that gives a good example so

what you can chat with the agent, start

building the order, you're done with the

order, and the agent sends you the

payment link. Before you even make that

payment, you can ask the agent to make a

modification to the order. So, they can

go back, update the order, and they will

send you a new payment link. You're not

on rails. You're free to to talk as you

would with a person on a call center,

for example, to take your order. So, I

think that's we were lucky enough to

make the right call at the beginning

>> early on

>> for tools. Yeah.

>> Okay. So, it sounds like so your agent

I'm imagining some of the tools that you

I'm not going to guess. Tell me some of

the tools that your agent has access to.

>> It's just built-in tools that we built.

So, basically the tools are for example

add products. You have a tool that is

basically create an order, add products

to the order. This tool with add

products to the order basically has all

the logic for adding modifiers,

comments, variations, etc.

You got tech product availability,

right? So you got search product, search

products. You got search knowledge base.

Of course, we have a knowledge base that

helps a lot about extra information. We

got geoloccation tools that helps hey

given this address figure out whether

you can use it. We got generate payment

link tool which basically takes care and

internally on all of these we have like

multiple providers. So depending on the

venue's configuration, if they have one

payment provider or the other, the agent

is agnostic to that. It just goes

through it and then the tool takes care

of that. That's the way Google thing

about tools, right? The same happens for

the geoloccation. If you most of the

times we use Google places API, but you

could use other one if you wanted. It's

kind of it we don't we try this is a

constant back and forth we have with

Santi most of the times. There's a lot

of things that go into the prompt, but

there's also a lot of things that should

be systematically happening, right? So

when the agent tries to take an action,

the tool should tell it whether that

action is successful or not and why so

that the agent can understand what's

going on. And this is like I was saying

the back and forth with Santi because

Santi takes a lot of time and working on

super amazing prompts that make the

agent talk like a real person. But then

we have the okay does the agent do this?

That's when the systematic

implementation has to come in. So that's

part of where we are mixing with

something where okay it's prompting but

the prompting should be related to what

the venues configuration is all about.

So we do we have a prompt composer

framework that we implemented which

inject fragments and depending on the

configuration it's just one fragment or

the other then so we have we do it

constant reviews about whether the

current prompt for how the agent should

reply con contains any logic that should

actually be implemented on the system

side or it's okay to implement it on the

more on the human side or how it should

reply. So yeah, a lot of pieces in

there.

>> Yeah. And many times we do MVPs for

different features through the prompt

and then we we build the features once

we realize that it's that it's helpful

and really needed by customers. We

assume the technical debt h to to test

if that's something that the customers

really need it.

>> Yeah.

>> Yeah. I love this. This is something

that the company every has written

about. Do you guys familiar with the

company every? No,

>> they're not. Oh,

>> okay. They are they're they build

themselves as the company of the future.

So, they have eight different products.

Each product's built by one person and

they're relying on AI. And they

introduce this idea of like agent first

development. And I don't mean coding

agents writing code first development,

but like you let the agent do the

feature. And then when you see if it's

working, you figure out, okay, what

parts of this should be deterministic?

How do we support this in code? How do

we optimize it?

>> But you're really relying on the agent

>> as your MVP to figure out does do

customers even want this? So, it sounds

like you guys stumbled on a very similar

idea, which is very cool.

>> Yeah, I have a concept that I've been

going through back and forth in my head.

I'm still trying to grasping into it,

but I'm starting to see ourselves as

building these products through how

there's business driven design or

business driven development, test-driven

design or test-driven development.

>> I think we're on the path of becoming a

company that that does conversational

driven design

>> because it's all about so this

conversation has to have this outcome

and it's not like the system or this

test. It's basically the conversation is

what triggers everything. So work on

that.

>> Yeah, I like that. I want to go back to

just your tool architecture and there's

something you said that I think is

really innovative that I want to dig

into which is your agent has tools and I

sounds like you're both at the prompt

level. You're injecting like I'm

assuming your agent needs to know for

this company this is what an order looks

like. These are the required attributes.

I can imagine that could happen at the

prompt. It could happen in the tool.

Just making sure that the agent has the

right stuff. I can see how this could be

a very, it's weird saying traditional

for a technology that's three years old,

but like standard agent turnbased.

You get a user message, you call a tool,

you do your thing, away you go. But

Juan, there's something you said that I

thought was really clever. You're

interjecting before the agent does a

tool call. You're looking at the user

message and trying to guess. Yes,

>> I want to give the agent more data so

that it doesn't have to do a tool call.

>> Say a little bit more about what how

you're anticipating there and what's

happening there.

>> So I want to give credit where credit is

used. So this was an idea that Santi

brought. So we just give it the message

of the user to the prompt and have it

understand that this that of course the

idea was amazing but it failed if we

just injected the prompt. So I had to

figure out a way to to do that and so

what we're doing is basically we have

smaller agents that are super fast

basically reading either just the

message of the person or like the last

conversations. And for example, we have

one super quick agent that what it does

is builds product queries for searching

products. So basically figures out

whether there's a product being asked on

the message and from that it builds okay

let's search for this product and so

while other stuff is happening it goes

and queries creates that query goes and

search and pulls that information.

Something similar happens with knowledge

base. So for example, instead of we're

to avoid having the agent call knowledge

base a lot, we grab the message of the

person and we just basically do a search

on the knowledge base just to see if a

one shot uh a fire just a single shot

gives us a result of knowledge base and

we also inject that. So we try yeah we

try to build that. Sometimes it's huge

and sometimes there's a lot of

information that is not relevant at all

which is the hard part because the agent

starts getting mixed up mixed up. But we

we are looking we still looking and we

have solved a lot of those but we're

still looking for so to improve that.

>> But that's how we're doing it even we

always on that session we inject the

state of the order for example. So it

knows a lot of things

without even needing to go through the

through tool calling to refresh its own

memory again and again. So like it's

like

>> we build temporary working memory on the

spot instead of having it processed. Of

course we have memory implementations

for for specific some the current

episode the whole session or even the

just the whole person

>> where we do know what the personnel

orders and kind of do suggestions or we

know who the person is. But in the end

yeah that's the strategy we use for now.

This is I think one of the most fun

parts of building AI products which is

just this like I to me it's bigger than

just context engineering but it's like

what goes in the prompt what data is

most relevant at this moment in time

even what tools should be available at

this moment in time and almost thinking

about your agent as a person and like

the way that we would train a person is

we don't tell them everything all at

once. We really look at based on the

conversation, what's the relevant

information for you right now? Like how

do we constrain your space so that

you're more likely to do a good job. I

could dig into this for the next hour,

which we probably don't have time for,

but it sounds like you do keep a profile

about your customer, so there's context

there. I imagine there's a lot of

context around here's what here's this

restaurant's menu. Here's all their

business rules about what goes with

what. on top of all your the goal is to

build an order and what does an order

look like for this company? Just tell me

a little bit about you must have had to

learn a lot just to make all those

pieces work together. What tell me a

little bit just orchestrating all of

that.

>> I think I have to tell you a little bit

about myself. So, as I mentioned, I've

been a developer since I was seven years

old. My degree is on music, not on

computer science. And that's because I

just learned to program and I learned

engineering just because I liked it and

I'm a self-taught engineer. So what that

gave me is not only I didn't only learn

how to build products or how to build

programs, how to correctly engineer or

architecture stuff, but also taught me

how to quick learn by just doing right.

And so in this scenario, I always been

like, let's do let's test if this works

and let's validate and then see or come

up with an idea just quickly see if

that's something that can happen. So for

example, this idea of having that system

prompt that immediate system prompt that

I was mentioning like I said we have two

system prompts. We have the main system

prompt which is what people mostly use

but then we have this smaller immediate

system prompt. And I came up with that

idea, but I wasn't really sure whether

that was supported by models. So I had

to go and search, hey,

>> would the model allow me to have two

system prompts like a main one at the

top and then one immediate injected and

then on the history of the chat that

system prompt wouldn't show up anymore.

It's just always appends itself on the

last message. Is that something that's

supported by model? So I had to go

through and learn about that. But it's

not that all the knowledge I had for how

models worked was already there because

I'm always like see okay this new thing

came out how do you use it? I already

knew it even before we can use it. One

quick example I want to give on that is

just for example I'm not sure if you

heard but just recently the second

version of Mercury came out by

Inception. Have you heard of Inception?

>> Yeah. this diffusion large language

model and I've been on that since their

first vision. I was like, man, this is

really something interesting. I think

this is really the path because it has

this uh this property of

being able to correct itself like

previous tokens can be corrected because

of just how it works instead of having

to you write a token and that's it. The

token is there. So learning all this

stuff gives you like the advantage of

already knowing what's possible and when

not. So when you have a problem you

already have the knowledge of the

different technologies on whether of

what like your tool set. is basically

the tools that you have at disposal.

Right?

>> What you're describing is selfishly why

I started this podcast.

>> Amazing.

>> Right. My thinking was if I collect all

these story I saw a need. I would see

lots of teams trying to learn this but

selfishly I was like if I interview a

bunch of teams about how they build AI

products by the time I'm building my AI

products I'll have heard a lots of ways

for how people have already solved the

same problems. That's a great way to do

it,

>> which is really fun.

>> That's great to see what you build.

>> Yeah. Yeah. I mean, yeah. I have several

AI products in the market now, which is

really fun. All around discovery

coaching.

>> Okay. Let's get into something you said

earlier, which is you told me about this

moment of doubt. This is really hard.

Can we do this? It wasn't good enough.

which really raises the question of how

are you evaluating if your agent is

working and I think with orders in

particular I could imagine some failure

modes that are really catastrophic. So

what are you doing to make sure this

works well?

>> We like to stick to one KPI which is how

many items did we identify correctly.

That is our most important metric to

really understand how well our agent is

working. Then it can say strange things

or it can use not the perfect

vocabulary. All of those things can be

improved. But the main thing main KPI,

how many of the items did it get

corrected?

That's it.

>> Yeah, I love the simplicity of that. And

I think from a customer standpoint,

that's probably what they primarily care

about. That's the only That's the only

thing they care about. Yeah.

>> Do you see like in your conversations,

do you get data to measure that? If you

mentioned at the end of a order, they

get a payment link, they pay for it, the

food gets delivered, they get a text

saying it's delivered. If something's

wrong, are they adding that to the

WhatsApp chat chat? Are they saying I

got the wrong thing?

>> I don't think if it ever happened,

maybe.

Yeah. I I think that the whole picture

here is that the agent can actually take

how do you say reclamosanti

>> claims

>> claims yeah so the agent can take claims

from the customer when there's a problem

and it even sends an email to the venue

so that they know about the problem if

there was a problem or how it was

delivered they even if this the user

sends a picture they can send it the

email will come with all the attachments

needed if there's an error in an order

it's more of a post post sale service or

customer service or that okay let's see

how we can solve this problem but to the

point that Santi was mentioning what we

tried to do is previously validate it

and Santi built an amazing tool and this

is amazing he just built this with

loable just a quick mention Santi is the

number one user of lovable in Argentina

in South America Latin America Santi

something like that

>> no in Argentina

>> okay within the 1% of users so They

reached out to him to say ask him but he

built this amazing tool which is mostly

front end of course there's a lot of

things happening but basically this tool

what it does it just uh he came up with

a way I mean something you want to talk

about it

>> while testing it we realized that

if we want to move fast you want to go

fast there are some things that can go

wrong of course especially with with AI

so we said okay there's actually two

things that we need to do because our

main goal again is to take the orders

correctly all of the items correctly. So

we can do that in two ways. The first

one is safe that works every time which

is human takeover. If we we do audits of

the live audits of the conversations. So

whenever we find things that are off, we

take over and we correct them by hand

and then we automate that. We fix that

problem and we automate. But before we

even get any of the agents into the

customer's hands, we do thousands of

tests. How do we do that? We actually

trained an agent that acts as a customer

to test the agent. We run literally

thousands of those during a night. uh

when they are done when one conversation

is done there's another agent that

analyzes the conversation and checks if

the order uh was correct if all of the

items were correct h and then after x

number of runs we have another agent

that analyzes wherever there was an

error right

and we start fixing that of course the

first time we did it we had a huge error

rate huge error rates but then we

started improving doing each of the

things that were happening. And now,

honestly, our production products might

make a few mistakes, but then we fix it

by hand if it ever happens. Once it

happened that we didn't catch the error,

uh, and we added an address that was not

the right one, one for delivery.

But since the customer gets a

confirmation ticket, he saw that the

address was not right and he called the

restaurant. So it actually was quite an

easy fix. Uh but there's a whole bunch

of agents testing the customer agents so

that the items are understood perfectly.

>> I also realized there like in my air

that I example that I gave I focused on

the wrong food getting delivered. That

was because of my bias with Door Dash.

feel like my experience with Door Dash

is I often get food from a totally

different restaurant that I ordered

from. It's just a weird experience.

>> Yeah.

>> Um, but I realized in your case,

>> you send them a payment link and I'm

assuming they get to see their order on

that page.

>> So, there's this like human in the loop

before the order is even finalized. Is

that true?

>> Yes. But that human is a customer and he

assumes that everything is okay. So they

are not testing it. They have

>> Okay. So you're not relying on that as a

feedback as a human in the loop.

>> No, no, no. The human in the loop is

someone at our team that jumps in case

there are any errors.

>> And how do you literally have somebody

monitoring all the conversations? Like

how are you detecting errors real time

>> for when we start with a new customer?

We try to audit them

>> all.

>> Okay. We have uh we have team members,

we have freelancers that helps us with

that. We do it ourselves as you might

imagine at 12 a.m. or 11:00 a.m. in the

night. We are reviewing conversations

many times.

But after finding out that was super

painful, we also have an agent that does

a revision automatically. Okay. and send

us an email, an alert email in case

there's any anything that needs to that

that that requires her attention.

>> So, this is part of onboarding a

customer,

you go through this testing phase to

make sure, correct,

>> the agent is interpreting the menu

correctly. It knows how to construct an

order. It's not something that you have

to do indefinitely. It's just part of

the like fine-tuning.

>> Just a few weeks,

>> correct? Just a few weeks of on boarding

and then it just starts on its own.

Yeah.

>> Yeah. It used to be three months and as

we improve this the time for boarding is

reducing which is basically our biggest

challenge and what we're working on is

like improving the on boarding time to

make it super super fast. Like we think

we've already solved the messaging part.

We I mean even today when we are

reviewing calls messages with Santi just

so you know the idea it's like every

noon or evening where like lunch or

dinner time Santi and I are sitting on

our computers just watching these

conversations going in orders being

placed and just seeing them go and it's

like we message each other going did you

just see what it how it solved the

problem is we are even are amazed about

how it's working on so Yeah,

>> it's impressive. So, we think that part

is solved. And the part that we're

trying to solve right now is basically

decreasing the onboarding times.

>> Times when the type of restaurant is

new, you might have a longer on boarding

because of all of the different

products, but for example, you can get

us any pizzeria and we will get it at

pizza stores and we will get it set up

pretty quickly because we know how that

business works. That's it's cool to see

like how you can build iterative domain

knowledge and start to reduce that

onboarding time for different types of

businesses. This is great. It's really

clear you're passionate about your

problem space and that you've really dug

in and that you have an equal passion

for the technology which is fun to see

too. Is there anything you wish I had

asked you that I didn't?

I think your questions were

amazing. We got to talk about a lot of

stuff.

>> All right, then let me ask you one last

question before we wrap up. What's next?

What's the big challenge that you're

tackling next?

>> Yeah, it's a great question and honestly

super proud of our product and we've

seen it working in lots of venues. So

now our goal is to scale it. We want to

be we want restaurants all over the

world to be able to use this tool. We

are focusing on Argentina, Mexico, USA,

and Spain. But we really believe that

this we're already seeing the numbers

and it's impressive how much it helps

businesses that that can use these type

of tools. So next step, let's get let's

put let's get it out there. Amazing.

Hopefully this episode will help get it

out there. Santi and Juan, it's been

super delightful to hear about your

story and to learn about your product. I

look forward to when I get to actually

order a meal through WhatsApp. So, I

will keep an eye out for it here in the

US.

>> You will. Thanks so much for having us.

Group super good questions.

>> Super super nice. If you enjoyed this

conversation, please subscribe in your

favorite podcast app and give us a

rating as it helps others find the show.

Thanks. I appreciate it.