Giga: The AI Platform for Enterprise Support

[Music]

Today I'm here with Verun and Esher,

co-founders of Giger. Giger builds AI

agents for customer support and are used

by some of the largest companies in the

world like Door Dash to handle millions

of customer support calls. Verun is

thanks so much for joining us.

>> Thanks.

>> Thanks. Thanks for having us here.

>> This is a very competitive space, AI

agents for customer support. Um, there's

obviously very well-known and wellunded

companies in the space like Sierra. So

how has Giga um been able to stand out u

and get an amazing customer like Door

Dash?

>> Firstly starting off with like Door Dash

is a extremely meritocratic company and

that's one of the massive volumes where

we had to compete against like 20 plus

vendors uh to win a logo like that. The

fundamental approach which

differentiated us from vendors like CR

is we build product and they took a

basically more of like a consultative

palentry type approach which takes a lot

of time to go live. If you consider Doda

scale hundreds of millions of calls

where if you say to build use case by

use case it takes months to get it to

solve the entire thing. For us we

primarily built a product which makes it

very very easier for them to go live for

example compared to other vendors where

we piloted it took them couple of months

to go live. We went live in a week

that's how fast it is and that's one of

the major reasons and I can add more

along the complexity side of parts.

Yeah, just just you know like just so

you know the although like customer

support seems like you know fairly easy

use case to solve uh but for companies

at a scale of Door Dash who have

millions of calls there are decent

percentage or even like tens of

percentage of calls where they are

extremely complex where you have to

coordinate with like multiple parties

over a real-time call and that is

something none of the other products

seem to have like they didn't seem to

support it and we were the only ones who

built the product in a way which is very

general and can also solve extreme

extremely complex use cases at scale. We

are live with some of these use cases

right now. Um so majorly it's just like

a purely product play.

>> Many other AI companies take this sort

of forward deployed engineer consulting

approach um where they're almost

building some amount of like custom

software per customer to get it working.

But you've been able to build something

that mostly worked out of the box even

for Door Dash's scale.

>> Yes. I think that we have a rule within

our company for the product team. It's

like you don't build anything custom for

any customer. Everything has to be a

part of the core product. So you build

something for Doash, every single other

customer should be able to use the same

product feature. So when we get like

very complex use cases for some of these

customers, we don't build it out

separately. We just figure out how can

we scale our product to support this

much more complex use case. Now every

one of our customers can also use the

same feature for their more complex

products as well. The reason I feel like

most companies take the palenteer for

deployed engineer model approach is

because it's essentially too hard to do

that.

>> There's so much like custom work you

need to do per customer to get the AI

working consistently like 100% of the

time. Um, how have you been able to

solve that challenge? How are you

actually able to have a product that

generalizes across the enterprise?

>> So, this is a bet we took a few I would

say like pretty much like 8 to 9 months

ago. Um this is something like I think

very other few companies actually do it.

So Python and programming is the

fundamental way to build anything you

want. And one of the approaches we took

is that Python is a first party member

within our product in the sense that you

can write Python within the product to

do stuff. And the bet we took was AI is

going to write so much code down the

line. AI is going to get so good at

writing code you don't really need to

write that much code yourself. And

that's paying off like im immensely for

us right now because someone from

operations team on door dash can come in

talk to our AI and write code

effectively themselves and they just

need to put in the business outcomes and

then AI is going to write code for them

and build like a very very complex use

case without like much involvement from

engineers. And since you can write

Python within the product, obviously

there are like very good primitives on

where can you write, where can you not

write, where can you inject custom

Python code. Since the primitive is like

literally Python, it makes our product

by default extremely general. And it

makes it very easy for us to add new use

cases because we just need to develop

protocols on top of these.

>> Okay. So that's interesting. So it's

almost like the forward deployed engine

model is basically there's business

logic that lives in the head of the

customer's ops team or whoever they have

to

>> translate

>> translate it's basically the four

deployed engineers role is to like

translate that business logic into code

but you've actually just got AI doing it

and so it's like the business logic is

natural English they they're typing it

into your system and your system is

turning it into Python code that makes

sure that the underlying product works

perfectly for each enterprise customer.

>> Exactly. And it may not be Python as

well. We convert like for Doash, we

convert tens of thousands of lines of

JSON files, their internal JSON files

into actual natural language,

interpretable instructions and also

Python code based on like their stuff.

So it's majorly like we optimized it to

an extent where even the FD part of a

sales process is done by an AI. So it's

part of our product instead of like a

human actually doing it.

>> You do have a forward deployed engineer.

It's just sort of like an AI forward

deployed engineer versus a human one.

>> Yes. That's fascinating. And you were

working on even as I understand it even

when you were at IIT as undergrads you

were working on finetuning of LLMs.

>> Warun did like research internship at

Stanford for training these like LLMs

back when like even charges pre-charge

launching right.

>> Yeah it's a it's like it's not

transformer is not not really a lot but

yeah roughly. Yeah.

>> Yeah. I think like you know our

experience has majorly been around like

fine-tuning these massive models like we

were the first people to extend Llama to

context length from 4 to 32K. we

outperform cloud 2 and everything that

immensely helps us because we know

exactly what AI can do and where it's

going to not where it's going to like

you know just blow up and we will only

automate the parts where we know AI can

do really well and especially when when

there are some of our products which are

extremely token intensive and that's

where you need to optimize for per token

cost and that's where like you know all

of our previous experience also helps

immensely for getting maximum per per

let's say dollar

>> so going back to the door dash contract.

So the speed in sales matters a lot. So

it sounds like a big part of the reason

you're able to win a huge contract like

that is your time to show them the

product worked was a week versus months.

>> That's definitely one of the ma massive

reasons. Other bio reason is just

complexity of the use cases that one

thing which I want to say is I mean this

is a very common use case in door dash

which happens a lot which no other

vendor in the market was able to solve

which is let's say if we are here at YC

office we ordered food and you went to

your home and it it happens a lot people

order food with like office address at

home and home address in office because

they just keep switching and you ask the

dasher saying that hey can you deliver

it to my home because I'm at home it's

on the way a lot of times dasher being

the nice people they deliver it to your

home and then they won't be able to

market as delivered because it will be

triggered by their fraud system because

they're outside the geoence

>> because they're not at the office,

right? So then they make an outbound

call to our AI agent asking, "Hey, uh,

can you mark the order as delivered?"

This is our AI talking to them. Firstly,

our agent does this. We we check a chat

history between you, the customer, and

Dasher to see if you actually requested

the address change. If not, while the

dash is in the call with our AA agent,

we make an outbound call to the customer

in parallel. They're like right now

there are two calls running and we ask

you hey Hajj the dash is saying that he

requested a new address did you actually

request it yes or no once you say yes we

go back and mark the order as delivered

for the dasher this this is a one

multi-party communication and we it's

live right now in do dash so these are

some of the complex use cases which we

solve that's one of the that's one of

the primary reasons that we are able to

win over a company like do dash it's

primarily ability to solve very complex

use cases which stems from ability to

write Python Yeah, it's like you know s

I think like when we were starting the

pilot they gave us this use case I

remember looking at it I was like oh wow

this is so interesting um and then

initially like our product was able to

do it to a decent extent but there was

not like really very strong I wouldn't

say like it would not be like the best

experience but we changed our product a

little bit and there's like a new

protocol where you can make outbound

calls from within a single agent so

basically like a sub agent if you think

and then like you know the experience

becomes comes much more much more better

and then like we are the only one who

can do it like at such scale

>> in this case the the complexity in that

example is that you've essentially got

you like a sub agent or two calls

happening in parallel and they both need

context on the what's happening on in

each call

>> pretty much it's like you know h like

did you actually receive your burrito

you order to IC's office because the

dash is claiming that he delivered it to

your home is that actually true

>> and how does it work like so once if you

call me and you verify that I actually

wanted the burrito delivered there. Then

once that call's completed, it tells the

other agent that's on the other call

that what happened. And then that agent

knows to tell the dasher, hey, this is

totally fine.

>> And actually marks the order as

delivered. Yeah.

>> And then the the funny part here is like

previously, yes, humans used to do it.

It was a worse experience with humans

because humans cannot stay on call with

two people at the same time. Now like

while we are on a call with the

customer, the dasher can still talk to

the AI. They can still ask about their

pay. Previously, humans were like,

"Okay, I'm going to put you on hold. let

me talk to the customer and then put the

customer on hold, talk to the dasher.

Yeah. So, it's basically a better

experience.

>> This is I'm smiling because this is sort

of like the u it's like the enterprise

customer support version of her than

>> maybe. Yeah. Like the same agent talking

to all these people at the same time.

Are there any other ways in which the AI

agents are actually better at customer

support than human agents would be?

>> Yeah, the first thought pointing is like

we all hate hold times. I mean like I

remember we have been hold for six hours

for IRS which is like nuts. If it is 6

hours I mean yeah firstly hold times go

away that's like the biggest thing for

all the thing all the agents and the

second thing is like resolution times

are like much faster you get to the

resolution faster it's again saving you

a lot of time multilingual is a very

major thing for a company like u do dash

and understanding multiple accents

multilingual with multiple accents is

very very tricky because a lot of even

people with not native language try to

speak in very broken English now you got

to understand the entire thing I mean

Yeah, that's uh those are some of the

major benefits that we saw which is uh

fundamentally like the seesat also went

like significantly up for the for the

dashers. Yeah.

>> Okay. So now you've raised this huge

series A um why did you raise the round

right now and what do you plan to do

with the money?

>> Yeah, it's primarily to just deal with

our demand. We have like a lot of

customers on the pipeline and a lot of

massive fortune 500 companies that are

piloting with us and we need just need

to serve them and deliver like high

quality experience for all of their

customers as well. That means we need to

hire faster and scale faster.

>> You actually have one probably the

strongest sales pipeline of any startup

I've seen. Um how has that happened?

>> Firstly, our contracts as you know like

very massive compared to like a lot of

startups of our scale and that means

that we generally tap to like sea level

decision maker once they like the

product really really well. It generally

like cross travels a lot and they refer

it to the other sea level expects who

are friends. So I mean if it's is

fortune 2000 how many people there

should be like 2,000 people if you

hypothetically think of every 1% of us

to 10 people it should be like pretty

easier for you to like get a warm

network to all the intros. I think

that's the primary reason I mean ours is

a very I mean if you think of AI in

general the only impact it can make is

like customer support or coding or the

real use cases which we are seeing in

the market and we are becoming like sort

of like board's answer to like what's AI

strategy in this company and Giga is

becoming one of answers. I think that's

the reason which we have like a stronger

pipeline to add anything else

>> and I think like you know obviously

getting into the door is one thing but

once you go there and talk to these

people who are like sea level execs they

are obviously very curious about the

product and the results we have like you

know delivered for Door Dash and also

like you know the the innovation we have

on the product even though they're not

very technical people just talking to

them about it gets them so excited okay

this is this is going to transform my

business and that's the excitement we

see and that's how we just like push get

pushed up. Yeah, I think you guys are

just the the epitome of what's really

unique about AI startups right now is

that by having the technical expertise

that you do, you're able to build the

product that actually works. And just

the product that works um is enough to

get a giant company like Door Dash as a

customer and then that's going to spread

via word of mouth and open up all these

other Fortune 500 companies that want to

work with you. And I just don't I think

that was very hard to do in the past

because most people could get the

product to work reasonably well or at

least well enough to close the sale. Um

uh but now we're in this new era where

if you don't have the skills technical

skills that you two have as founders,

you actually just can't get the products

to work.

>> It's definitely mix of market plus great

product. I would say as you know all the

companies are thinking what AI that they

need to do and customer support is one

of the massive things of them. Yeah. Why

do you think customer support has taken

off and become such a competitive space?

As you know like you know currently AI

models like you know the recent ones

they can get gold medals in IMO they can

get like gold medals in IOI and the

biggest challenging part there is like

you know just the context let's say an

model can get like gold medals in IMO

why cannot it do like the job of a

manager in an enterprise the biggest

reason is it doesn't know everything the

manager knows but if you think about

like programming and customer support as

verticals a customer opens up a chat

with you or some conversation with you

you'll learn everything about the

customer in context and you will solve

it. So the context scope is fairly

narrow for customer support and coding.

Coding is also kind of similar. You

learn, you look at all the files, you

threes and then you come up with an

approach. Obviously like AI is here to

just make people more productive and

optimize a lot of these enterprise

processes. Customer support and

programming according to us at least is

are the ones with like which are really

fit well to deal with the current

context limitations of the current LMS.

Um and it's just going to get like you

know as context gets better and

everything these models are going to do

everything.

>> Yeah. And people try I mean right now

people are like even trying to

proactively give more context as well

even that also like solves the solves

the issue and more and more use cases

get unlocked but customer support is

definitely one of the

>> yeah customer support is like in that

sweet spot where yes you need to know a

lot of the policies and everything. Yes,

you need to know a lot of the customers

but it's not like too much the people

people problems and things like that.

It's not just too much for the LLM to

just get confused about.

>> Yeah. The reason we're to spend so much

time on just like the product and the AI

for deployed engineer stuff, this is

going to be gold that like the

especially the audience that watches

this stuff is very very deeply

interested in this kind of stuff and

we've spoken so much on our content

about like the four deployed engineer

model and there's so much interest in

that. So AI for deployed engineer is the

first like I don't think anyone's ever

heard that before.

>> Yeah. It's like if you're building an AI

product, you need to build your own FD.

>> Yeah.

>> Yeah. you guys basically this is going

to position you as like at the cutting

edge like it's basically like everyone

else is sort of doing the human for

deployed engineer part and you're

already like leaps and bounds ahead

because it's the AI is doing the full

deployed engineer part. So Giger is 20

people today um and you're going to be

hiring aggressively. How do you think

about the culture you want to build as

the team grows? I want to start off with

saying that not everyone is fit to join

us. Um, and the people we hire, they

need to be like extremely smart and very

mission aligned. And as an engineer,

like how I would like put the role as in

in our company is that we are one of the

only companies or we are one of the few

companies where you can have direct

impact on potentially like billions of

people if you join us. We are here to

transform like you know B2C enterprises

to a massive degree and it can it it's

starting through customer support. We're

going to expand into other verticals. We

will transform a lot of these

experiences a lot of consumers have with

these B2C companies and the direct

impact let's say we go live with one of

the big like fortune 500s which have

like billions of customers and we

automate support for it you are

basically making the experience better

for billions of people and that's one of

the ways like you know they need to be

very impactdriven they need to have the

drive to make a lot of these experiences

really really good. Yeah, it's it's

primarily a little bit of like a very

high agency and very raw IQ is what I

would put at it. And right we have like

some of the amazing people who are

joining us. We have literally convinced

a guy to not join open a and anthropic

who were paying us like ridiculously

high amount than us to join Giga.

>> So as a startup even one that's growing

as quickly as you are. Um how do you

convince people to join you instead of

joining meta or open AAI and the giant

amount of money that they pay? It's

primarily directly because the amount of

impact see the by the way this was a

back end engineer and one front end

engineer who are like getting competed

by had a both competing offers the thing

was the impact that he's going to make

on any of these companies I mean opening

is a massive company it's not a small

company anymore and the impact he can

have at Giga can the code literally

which he writes can be transformed to

hundreds of millions of people

potentially every customer of Door Dash

will get a sim or like any one of these

massive customers who work with us will

get get the direct impact. A lot of

people who are not motivated by money,

who want their work to be used by a lot

of people get motivated uh to join gig

again but this is also not going to last

to us if you become thousand people but

again right now is a sweet spot where

you build something which impacts a lot

of people. Our primary mission of the

company is to build a world towards

perfect execution and we'll try to

optimize every single thing in this

massive enterprises and try try to

optimize every process and every

operation process and make it more and

more efficient and execution driven. Uh

that's one of the biggest missions and

we will so we're starting with support

and we'll go to every single opex heavy

thing in these massive companies and

we'll try to optimize it.

>> So you think you'll go beyond um

customer support at some point? We're

already like customers are literally

like asking us to like uh it goes

something like this right some big of

what are the biggest OPEX things in the

company uh support where they have like

a lot of uh hundreds of thousands of

people and then there's compliance

there's like a bunch of other things in

the in these companies and they're

literally asking us can you guys since

you're doing this can you also do this

so we're already seeing a lot of

interest uh in in these things and what

one more interesting category of people

which we have seen work out for Giga

very very well are former founders who

just wants to have high agency and a lot

of impact. We already have these

customers. We are already running pilots

with them. We can they can work and

directly work with the end customer and

build a massive impact. That's one more

category where we are seeing a lot of

success who are of the people who are

being really successful in giga former

founders.

>> How about we talk about the two of you a

little bit actually I'm curious about um

tell us a little bit about your founding

story like how did you two meet each

other um decide to start a company

together and give us the origin story.

So we both know each other since like

2019 when we both went to IIT Karakpur.

Um and then like Vun is like really

passionate about startups. I think like

since he was like six or seven and then

he has been reading program essays, Elon

Musk essays and all of that. Um and then

he has been trying to do a lot of

startups during the college and then we

eventually applied to YC in 2023 and you

were our interviewee.

>> Yeah, you applied with a completely

different idea.

>> Applied with an Yeah, you should tell us

about the idea. Oh, our idea was to

build an education platform for college

students in India to get jobs and that

was the idea we applied with and I think

you know this as well like you know when

we came into the interview your entire

questions were like oh can you vun you

have this experience here can you talk

about like what other companies can you

build from this experience and then we

we were like so surprised we were like

okay

>> I mean I I rehearsed for this interview

so much I mean like there are like 10 YC

founders who interviewed interviewed me

and I mean I know answers to all the

questions which is like why this what's

the big time and everything and you come

to the interview and you ask for a

completely different question which is

like a pick a new idea and I was like

what and it's it's a

>> and then and then our thought process

was like okay VC likes people who are

very gritty so we should stick with the

idea we came in with we're like no h

like yes there are other ideas but we'll

do this and then I think like one of the

questions you asked was oh if we don't

if y doesn't take you are you going to

keep doing your company and then We said

yes but I think you knew we knew we

wouldn't have done it.

>> Um yeah I think Y is one of the reasons

like we even exist at this point in time

>> is honestly like kudos to you for just

taking a bet on us. Uh I don't I mean it

we got into this instance stage I mean

like we had uh we both had like a high

frequency trading offers paying us more

than 600k out of college and I applied

to a bunch of IV leagues and I got into

all of them and at that point of time YC

was always this like northstar who one

company who I mean I thought that YC was

one of those things which creates this

massive generation of companies.

>> Yeah that was always something that

stood out for me. I was always curious

about this. you don't think it'll last

as you. But yeah, like I mean I feel

like turning down quant jobs if you're

in in America and you grew up in America

is sort of like one level of risk to do

a startup but like being growing up in

India and like turning down like super

high paying jobs offers like must just

be how did you think about the risk of

doing that and were your friends and

family supportive of it?

>> India is like generally like not a

risk-taking. There's like very low risk

>> and friends don't like their friends

outgrowing them typically. So it's it's

always tricky. My dad thought literally

I was like crazy. I mean

>> I mean maybe he was he was right

probably. Yeah.

>> I mean like I mean I clearly remember I

got like this highest paying job out of

ever out of all I this high trading firm

is paying me this much amount of

>> because you guys had some of the highest

ranking scores at IIT. Right.

>> He he not me.

>> Where did you rank at IIT?

>> I was like third in in KGP Karapur. Um

but I mean the reason we decided to do a

startup I think like for War Vun it was

majorly around like passion and he's

like okay I just want to do this like

even if I go to like you know let's say

this HFTs I'm still going to want to do

a company I will do it like regardless

of what I want to do. For me it was more

along the lines of like I was just

logically convinced that okay just doing

a company than working at like let's say

these HFTs for 3 years is still a better

outcome for me even if I fail uh by

going into IC. That was just like a

logical conclusion I came to. I was

like, "Okay, sounds good. Let's actually

give it a shot." And then, yeah,

>> for me to be honest, it was just doing

the hardest thing possible. I mean, it

translates to a lot of our company

culture as well, just picking up the

hardest problem and solve it. I thought

genuinely like a lot of rich people and

powerful people build their own

business. So I thought I wanted to do

something along those lines and I

thought it was super hard and yeah

>> it's kind of surprising because this

aspect of okay we want to pick the

hardest problems to solve kind of

translates so well because we go into

these sales calls and then we talk to

these let's say sea level people a lot

of the other competitors generally ask

them okay what's the biggest chunk of

support problems we can solve how can we

get you to 70%. We don't do that. We go

them and ask them what's the most

complex issue you have. Let me go and

solve that for you. And then you trust

me to take over the rest of the entire

support ticket. And I mean this is my

personal metric to build a product. I

want to get every single one of my

customers to 98% resolution. And not

because I believe it's a very good like

ROI. Yes, it is a good ROI, but I

genuinely believe like you know it's a

better experience for the end customers

to get like an AI which can solve 98% of

your issues. The biggest reasons humans

still say like agent a human human this

is like classic behavior we see is

because there is no trust there is no

trust that AI can actually solve

majority of the problems but if you

build an AI which can solve 98% of the

problems why would they not like you

know talk to it

>> to me it's just like the the area to

build a startup now is different feels

different post chat GBT than before and

in particular it's just there is a real

return to being a deeply technical

founder um and you can just build your

company a different way like going in I

think it's going in and being like,

"Give us your like building your whole

sales process around just give us your

most complicated like

>> and then I'm going to show it to you

that we can do it."

>> Yeah.

>> Yeah. I remember so I remember the

interview. I remember you guys being

very stubbornly attached to your like

college student idea. Um talk us through

like actually um going through YC and

you even had like difficulties getting

into the country like just tell us the

whole journey.

>> No uh first I was more curious on why

did you take us in?

>> The core thing we do is just invest in

people. So it was very very clear to me

even in the 10 minutes and from reading

the application that two of you like

extremely high IQ um uh extremely

determined um that yeah the fact that

you were you had mentioned that you were

turning down the HT offers and you were

turning down actually I was impressed by

the fact that you were even willing to

turn down um your research offer from

Stanford um so in general a lot we do at

YC is try and find people who are like

unusual compared to their peers and so

the fact that you are like IIT and yes

I'm not I wasn't born in India but my

parents from India and I just know

culturally it's so risk averse that I

was like, wow, like if they are willing

to take this amount of risk, they're

probably quite unusual people. And in

the 10-minute interview, it was clear

just that not just that you were smart,

but you just had a certain intensity

about you. And yeah, one thing I've

learned doing this for a while is there

sort of yes, like I didn't like the

idea. It was probably not very obvious.

Um, and it is a bit of a high-risisk

strategy to keep defending the idea

anyway, but I

but I don't know. I' I've learned that

if you're sort of if you're just smart

and intense enough like I think those

people actually are always just sort of

stubbly attached to the idea. Um this is

actually it reminded me a lot of the

Amplitude interview. Amplitude is now a

public company doing analytics and this

is like a decade ago. Spencer Skates the

founder of that company also really

really high IQ very intense person. He

had an idea to build like a um voice

assistant for you while you were driving

but this is like 12 that yeah wasn't

really possible. Um, but he just

defended it so intensely. I remember the

interview thinking like anyone who's

this smart and this intense, you just

sort of have to invest in and work with

them. And it took them like a year and a

half to find like the amplitude idea and

it took you guys like a little bit of

time as well. But I just that intensity

I think is quite rare. That's why that's

why we funded you.

>> Yeah. Uh, coming to the YC thing. Uh,

yeah. We thought it would be so easy to

get into US to be honest that we go

there.

>> No. in the interview. I think when you

gave us the offer, you were like, "Okay,

when can you show up in like, you know,

in the US?" You were like, "Okay, we'll

be there in a week." And then and then

you were like, "Okay, are you sure you

can be there in a week?" We were like,

"Yes." And then actually like we could

have gotten there gotten here in a week.

We did have like a visa interview

scheduled. And then we went to the visa

interview like the I think like the

entire B1 B2 visa system is like kind of

like, you know, it needs a clean up. But

we went there, they asked us like, "Hey,

are you married? Do you have kids?" We

were like, "No." And they were like,

"Okay, you're rejected." We were like,

"What?" And then we come back.

>> Your your interview was at least like a

little longer. Mine was like just three

questions. Married, salary, how much is

your salary? I was like, "I have my own

company." And I was trying to explain

what Y is to that guy. And he was like,

"No, it's done."

>> For a minute, I was just like I did not

even realize that people can reject you

on a visa interview. Oh yeah.

>> I was like, "What's happening with my

life? It's just u

>> Yeah, I remember. So then cuz this was

for the summer 23 batch and so then we

basic you had to do the batch remote

essentially, right? So I didn't actually

meet you in person um probably

>> until like January 2021.

>> So it's kind which is getting close to

like almost close to a year since um the

the first interview probably like 9 to

10 months after the interview.

>> The instant crazy thing was everything

became harder, right? I mean we don't

know what our peers were working on and

it's it's we I mean it's it's our first

time trying it out as well. Then we put

it to finetuning and we picked a really

vertical where we can nail it. that

definitely by on that note it definitely

felt to me like the you getting here

being Silicon Valley and being here at

YC in person definitely really helped

accelerate the process of finding like

the new idea right

>> a lot and a lot to be honest it's

extremely helpful because I mean I

remember back in I was India this was

again thanks to Gary Tan that reason the

fundrise happened right I mean I I was

asking my peers how much money they were

like raising and they're like there like

60 interviews scheduled for everybody I

remember asking I was like how I have

only eight eight investors am I doing

anything wrong

>> this is for you dem day fund raise.

>> This is where demo day fundra and you

were like no no just go with it go with

the flow and everything and almost

surprisingly almost all of them end up

investing and Gary Tan told from our

seed investor from uh Nexus you must

invest in giga and they took a bet on us

and it's a I mean that really that fun

really helped us a lot because we're

back in India and it's

>> yeah I think like just having zero peers

and then like we both are like okay we

just quit everything and then now now we

are stuck in India we want to be there

And then we don't even have like

especially with the education YC really

helped us because you connected us with

a lot of people in education education

and you were like you were like pretty

determined to get us off of that idea

>> and then and then we spoke to a few

people we were like okay yeah that

doesn't make sense let's pick something

where we are good at we picked like

fine-tuning and then they inference that

vertical and then yeah it's majorly

because of Gary I think our seed got

done pretty much

>> I remember so like the timeline was

basically you applied with the student

idea you pivoted into the finetuning as

a service idea like

>> in a in a month

>> in a month. Yeah. So hitting almost the

midpoint of the batch and then you

raised at demo day on the fine-tuning as

a service idea.

>> Um which actually I remember it got

pretty decent traction like early on.

What exactly happened to that idea?

>> Okay, so for some context, we were

basically training these open source

models, fine-tuning them for specific

use cases to get really good performance

outperform like let's say cloud or GBT4

or whatnot. And then back then GBT4 was

the only real smart model and it was

like crazy expensive. If you remember

the prices it's like $32 $32 for million

tokens or something. It had a good

traction because people were optimizing

for cost and we were like okay it's good

it's good. People want to optimize for

cost and then we're going to train these

models and you can use our inference and

then we had like decent revenue as well.

And the biggest issue with that idea was

it was like a broken funnel. people

would come in yes they would be happy

like it's nothing wrong with the product

but then open AI or anthropic like you

know they just released a new model

which is like let's say as smart as GPT4

but like let's say 10 10 times cheaper

which actually happened with like GPT40

it was like I think four times cheaper

and then we saw our customers they were

like okay we want to move to OpenAI now

because this new model can do this new

other things and our fine tuned model

was trained for this use case we had to

retrain it and then it was just a lot of

like you know continuous iteration we

had to do to keep up. And it felt like

we were kind of betting against the AI

wave than betting into the AI wave. It's

like, okay, if OpenAI stopped making

better models, we would be doing really

well. If OpenA continuously made better

models and cheaper models, we would be

doing worse. So that realization we got,

I think in February or March 2024, we

were like, okay, we don't want to bet

against AI, let's just go into something

where we bet on AI.

>> Okay. Um and then how did you come up

with the idea for Giga?

>> We were pivoting and trying out like a

bunch of different ideas and one of the

some of the thing we're at that point

we're trying to even build a software

engineer. So we're we're definitely like

jumping on like a lot of ideas again

Nexus might introduce Zeppto founders

and they told that customer and that's

when we got to know that like customer

support is like one of the real problems

and they became one of the first

customers. So you got you were

introduced to Zeppto and you just went

to talk to him in general about hey like

what are your problems using AI?

>> Yeah we tried like a little bit of

hypothesis I think there was one of our

mutual friends who again another YC

founder who worked at Uber to automate

their support and they were saying

talking about support problems just made

a hypothesis Zeppto might also have

similar problems.

>> It was a shot in the dark and it kind of

worked. Yeah, we we spoke with them and

then like you know they were like okay

there's this huge event coming up in

India we need to hire a lot of people we

if we can help use AI to automate

support it would be massive help because

we don't have to hire like what 2,000

people or something um and then we were

like okay this is the first application

problem so we flew to India we were in

SF flew to India we went in we went and

sat in their call centers for like you

know days and then observed how they

support people do this work and then the

solution we built was like so custom, so

random. It's like they didn't have API.

So we built like a web automation

solution on their CRM which was very

hard to scale but we scaled it anyways

and then it was like a very custom

solution for Zeppto. We built out

>> and that sort of laid the foundation for

the product as you have today.

>> Yes.

>> Yeah. At that point of time we realized

that we are not going to do consulting

again.

>> Yes. Oh yeah. No, we were like okay this

is no way going to scale and then that

was where we got our determination like

we have to build a product. there is no

way we can scale this because and then

it was a very interesting product

challenge as well like we knew all the

kind of support problems Zeppto had and

we knew all the kind of support problems

for a few other companies and now the

problem was how do you build something

the in the most simplest way which can

solve all of these different problems um

that was basically the product problem

we had to solve. Okay. So, if we look

ahead to the future a little bit, um I

mean you guys right at the cutting edge

like you're you're operating at your

agents operate at like probably like the

largest scale of any agents in the

customer support space. Um where do you

see this space evolving of AI agents in

the enterprise in general and especially

as the models themselves get more

powerful?

>> This is the bet we are also making. Um

data and context are the are the most

important things and AI gets better the

companies with the most context and data

would be able to perform the best. Even

if like open a had a much smarter model

but it does if it doesn't have the

context it cannot do much within an

enterprise and that's where we are

heading as well. We start with customer

support. We provide a lot of value to

our customers. That sets us up to win a

lot of these other operations within

these enterprises. And the vision of our

company is like you know this is this is

maybe like you know a decade away

potentially but we want to build a

platform where the next trillion dollar

businesses are built on top of and we

want to build the most efficient op ops

automation platform in the world and we

think we are like you know very well

positioned because customer support is

one thing people are looking to automate

right now and next things would be like

you know something much which involve

more context and since you already solve

support you have like all the context

about potentially any kind of issue

customers might face that will make you

make better judgments in other verticals

within the company. Um so that's where

we think like the market is heading

towards like you know potentially

consolidation rather than like you know

point solutions for each of these AI

verticals.

>> Since we have context from every single

customer and what the past interactions

and what are they looking you like both

like very micro and macro level view of

what's what all the customers are asking

it helps us solve other customers other

issues in the company very better.

>> Yes. It's like I mean fundamentally

every company is an optimization

function on top of like value you give

to your customers and if you understand

the value you give to your customers and

the issues they are facing you can write

that function in a really good way.

>> For Giger itself like when you think

about how big the company could be are

there are there existing big companies

that you look at that you you aspire to

either because of something they've done

and how they built the product or the

size of the company today.

>> I think we aspire parts of different

companies. So Salesforce I think we

really aspire like the way they built

the most general product and I mean

obviously like you know we don't like

the design of Salesforce but obviously

like they built the most general version

of CRM pretty much and open AI I think

they are always at the forefront

innovating and things like that. So

different parts of different companies

are something we aspire but I don't

think we are looking to be like oh we

want to be the next open AI or we want

to be the next Salesforce or something

like that. different parts of different

countries is like I mean anthropic for

some I think has a very very good taste

>> yeah taste and a lot of great talent

compared to open air right now who are

going to them yeah

>> verunisha that was fantastic thanks so

much for joining us that's all we have

time for today congratulations on the

round and I'm looking forward to seeing

giga continue to grow into a giant

company thanks

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