Varun’s Journey: From IIT to AI Agents Powering Customer Support

Name: Why Two IIT Engineers Turned Down $550K Jobs To Build A Startup
Uploaded: 2026-05-29T12:30:27+00:00
Duration: 24 min 29 s
Channel: Y Combinator
Description: Summary and key takeaways on Varun’s Journey: From IIT to AI Agents Powering Customer Support, covering Early Life and Education Varun grew up in Andhra

Y Combinator

May 29, 2026

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24 min video

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2 min read

YouTube video ID: 2Ap1dnv-GXA

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Varun grew up in Andhra Pradesh, India, the son of government teachers who pushed him toward engineering or medicine. He attended IIT, where his early college years were marked by partying before he refocused on large‑language‑model research. A stint at Stanford deepened his expertise in transformers, and he earned a $550 k job offer from a New York quant firm, which he turned down. He also competed on Kaggle, winning roughly $50 k.

The Y Combinator Experience

Varun applied to Y Combinator with a co‑founder on an edtech concept. During the interview, YC partner Hajj rejected the idea and urged a pivot. Acting on that advice, Varun shifted to fine‑tuning LLMs to cut costs and latency, a move that secured a $4 million seed round based on open‑source model benchmarks.

Building GigaML

The fine‑tuned models found their strongest use cases in customer support and coding automation. Early contracts with Zepto and DoorDash validated the approach; GigaML won the DoorDash deal with a team of eight against a 400‑person competitor. The YC network helped establish enterprise trust, and the company now focuses on an “AI forward‑deployed engineer” that automates configuration and policy updates across Slack and Google Meet.

Business and Hiring Philosophy

Varun rejects the notion that founders need a traditional business background. He emphasizes finding the right Ideal Customer Profile and proving product value before scaling sales. The internal mantra—“Automate, automate, automate”—has shrunk the engineering team to 6‑7 times fewer members than a comparable non‑AI shop. Hiring prioritizes “spikiness” and deep technical skill, and interviews feature “vibe coding” to assess candidates’ ability to write code without AI assistance.

Mechanisms Behind the AI Agents

Traditional IVR and chatbot systems achieve only 10‑15 % support deflection. GigaML’s AI agents iterate on markdown policy files, directly influencing KPIs such as resolution rate and CSAT, and currently reach 60‑70 % deflection with a target of 90‑95 % for top customers. The AI forward‑deployed engineer joins communication channels, takes notes, and automatically configures dashboards or policy changes, turning enterprise deployments into high‑leverage, low‑overhead operations.

Takeaways

Varun rejected a $550 k quant job, pursued LLM research, and founded GigaML after early successes on Kaggle and at Stanford.
Following Y Combinator’s feedback, he pivoted from edtech to fine‑tuning LLMs, raising a $4 million seed round to build AI agents.
GigaML’s agents have lifted customer‑support deflection from the industry norm of 10‑15 % to 60‑70 %, aiming for 90‑95 % with clients like DoorDash and Zepto.
Using AI coding agents, GigaML operates with an engineering team 6‑7 times smaller than a comparable non‑AI shop, living by the mantra “automate, automate, automate.”
Varun’s “burn the boats” mindset drives a product‑first strategy, hiring for technical depth and proving value before expanding sales or staff.

Frequently Asked Questions

How does GigaML achieve higher support deflection rates compared to traditional chatbots?

GigaML’s AI agents replace IVR and basic chatbots by iterating on markdown policy files that directly influence KPIs such as resolution rate and CSAT, delivering human‑like, real‑time answers. This approach lifts deflection from the typical 10‑15 % to 60‑70 % and targets 90‑95 % for key customers.

What is an “AI forward‑deployed engineer” as described by Varun?

An AI forward‑deployed engineer is a system that joins Slack or Google Meet sessions, records discussion, and automatically updates dashboards or policy configurations to improve performance metrics. It acts as a virtual engineer that handles enterprise configuration and policy updates without human intervention.

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Helpful resources related to this video

If you want to practice or explore the concepts discussed in the video, these commonly used tools may help.

Paul Graham Hackers And Painters Book Recommended

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Full Transcript YouTube

I was just panicked because I never
prepared for anything. I mean I prepped
so much for the interview that I chatted
with a lot of XYZ interview founders
etc. They all told me what's your idea
what's your TAM and everything but HJ
didn't ask about any of those things and
I thought genuinely interview went like
so horrible that we are not going to get
in. Hajj told us you guys are really
good engineers just pick something else
and work on it.
All right, we're so pumped to be here
with Varun from Giga ML. Um, thank you
all so much for coming. Varun, why don't
you start by telling us a little bit
about what GigaML actually is?
>> Sure. We build AI agents for customer
support. We work with some of the
biggest companies in the world like
Doash. We work with one of the biggest
crypto exchanges in the world, top three
telecom providers in the world. Yeah.
>> And so what does it mean when you say AI
agents for customer service? give an
example of how someone is using your
product maybe even how people here have
used their your product and not even
realized. The traditional way that
support works is whenever is call the
support it actually uh get to an IVR or
a chart bot then goes to a human the
deflection rates are closer to like 10
to 15% with AI it's just like you call
it's entirely like humanlike experience
closer to like 60 to 70% deflection
rates we're aiming to get it closer to
like 90 to 95% for top of the customers
it's just like a better experience you
have never need to be in hold again you
can just call and get the issue resolved
like really fast.
>> Yeah.
>> And why don't we start by rewinding the
clock a little bit and hearing about the
very early days of your company. So
maybe even before you started a company
why don't you just tell us a little bit
about what was your upbringing like like
how did you uh get introduced to
technology in the first place? Yeah, of
course. Uh my
uh I was from this town in small town in
uh Andhra Pradesh and my parents were
both like government teachers. They of
course wanted to be an engineer or like
a doctor. So we were uh I grinded myself
out to get into IIT which was a great
experience. I I got into like IT
corrector uh in electrical engineering
and my first two years I did not like do
much because it was like a covid year
we're just like partying and like not
much studying and uh during third year I
started like uh doing my research in LLM
in Stamford and that's when we started
like uh seeing this is like a pre-sharg
moment we're working on transformer
models like bird etc and uh
>> this was like when you were in college
or still in high school. Okay.
>> Yes. Yeah. This was when I was in
college and I actually got like a pretty
good job when one of our quant firms uh
one of the leading quant firms in New
York. I got offered like 550k at that
point of time. It was like a big thing
uh and I also got my PhD in Stamford uh
to join and then charge launched uh this
this uh around December is when I got in
and then charge launched. We're like
super excited. Uh the super exciting
moment is it was able to write code and
a lot of things. So we just wanted to
build something on top of it and give
getting into YC a shot because I was I
was reading PG's essays launching like
the 2014 YC startup school. It's a
classic one and I just wanted to give it
a shot and try to get into YC. So me and
my co-founder, we both know each other
from like freshman year and we just
thought like what could go wrong? You
can just apply and always see right. And
it's so dramatical because I was
supposed to join. We were supposed to
like join in like 3 days. That's finally
when we got in and the interview was
also like so different than what we
thought because we uh we were trying to
build an edtech uh on using like LLM and
we go to interview I clearly remember HJ
asking me that hey this is edtech it's
not going to work pick something else
and it was never even about like the
idea or anything we wanted. He was like
you have like research experience in LMS
pick something and etc. And
>> and so so when you heard that did that
increase your ambition or reduce it or
how were you thinking about that?
>> I was just panicked because I never
prepared for anything. I mean I prepped
so much for the interview that I chat
with a lot of XYZ interview founders
etc. They all told me what's your idea?
What's your TAM and everything but HJ
didn't ask about any of those things
>> and I thought genuinely interview went
like so horrible that we're not going to
get in. And yeah, Hajj actually wanted
us to join YC and he told us you guys
are really good engineers. Just pick
something else and work on it.
>> That's how I mean we would you would not
have existed without Hudge taking a bet.
>> Yeah.
>> And so Okay. So I imagine then you went
back to your roots a little bit as
engineers. Tell me a little bit about
what were your roots as engineers like
like when you were in college. Were you
like a by the book guy or were you more
of a hacker? Like what was your kind of
personality as an engineer? Yeah, it's
it's very interesting because my
co-founder was I think third ranked in
entire IIT and he only got one B in his
entire career. That two he really messed
up actually. Uh there is uh he actually
went to uh class and gave attendance for
a wrong guy as a friend and he got
penalized 10 marks. That's why instead
of ex he got B or else he would have
been like top of the campus. He's like a
very book guy kind of a person. For me
honestly I never my grades are bad. I
never like really studied that good and
I used to do a lot of Kaggle
competitions primarily because you can
make money if you win the competition.
That's pretty much it. So I would say
I'm more kind of like a hacker and he's
more kind of like a book guy. But
eventually we both turned out to be like
a hacker kind of people. Yeah.
>> Wait, can you Yeah. Can you tell
everyone about your history with K
competitions? Like how many of these did
you do and like how much money did you
make doing this?
>> I did a lot of them. Uh I think I made
like $50,000 or something. That's how I
landed to like one of the one of the uh
one of the high frequency trading jobs.
I gamed it so much that they banned me.
>> I Yeah, we we I gamed a lot of that
thing. Yeah.
>> So, okay. So, when you were in college,
you're now spending a lot of your time
doing Kaggle competitions. You're
experimenting with machine learning
models. Um you apply to YC eventually.
Harge tells you, okay, this like edtech
idea is dumb. Do something else. How do
you now bridge those two worlds? Like
what's kind of the next thing you
decide, okay, let's try to give this a
shot.
>> Yeah, it's Hajj did a pretty great job.
What he did was he made a shot with
Corsera COO and a bunch of people who
have like some of the most successful ed
techs. All of them told it's a bad idea
to do edtech. So we decided to pivot
pretty much after a month into the batch
and we were again we had like a pretty
bad experience because both of our B1
Bas got rejected but this was the first
time YC turned into in person we're just
doing fully remote. We had experience on
finetuning so we just uh I just read
this uh research paper from one of the
data bricks co-founders which is
basically LLM uh caching LLMs to reduce
the cost. At the time GB4 used to be
super expensive. Instead of just
caching, we thought that fine-tuning
might be better using a smaller LM and
everything. So that's how we got started
with fine-tuning and we open sourced a
bunch of models. We topped hugging face
benchmarks. That's how we got like a lot
of traction and a lot of people reaching
out to us and we raised a $4 million
seed round.
>> So okay, so you did YC then you end up
raising your seed round. You're you're
working on this finetuning idea. That
doesn't sound anything like customer
service AI system. So how did that first
thing become the second thing? What was
that what was that story like in which
you found the idea you're now working
on?
>> Yeah, the interesting part about
fine-tuning is it's a really bad market
for a bunch of reasons. The only reason
you want to fine-tune is to reduce the
cost and make it faster. Or else there
is another use case which is if you are
so secure it's very hard to sell to
those uh big insurance companies or
healthcare as an engineer because it's a
sales process not engineered process. We
have realized it after like a year or so
and the interesting thing was the only
two use cases on Gigab customers which
are growing very well are customer
support and coding. So we decided to
customer support and
>> so so you basically saw that from your
customers, right? It wasn't like you
just like looked and said, "Oh, this is
a big market." You just you naturally
discovered it from the thing you were
building.
>> Yeah, we saw it from customers and Aziz
was just here and Zeppto was our first
customer for that thing. We reached out
to them and as they were scaling really
fast and they tried us out and they
turned out to be our first customer.
>> So okay, I mean this is where I think
things can be very counterintuitive,
right? like when you chose to switch
into customer service, there existed at
least one or two other companies, some
of which who have very famous founders
who are very well capitalized, companies
like Sierra, for example, that already
existed uh at the point of you guys
deciding to go all in on this. Why did
you think that you could do it anyways
and still win? Why did you think that it
didn't matter or it was worth it for you
to go after that market? Firstly, we
don't know CR and Tech1 existed when we
signed Z2. And
>> so like there's a little bit of benefit
to naive there. Yeah,
>> that thing. And we didn't think of
competition much. That's pretty much
what I would say. We never I mean even
right now our entire product is is if
the is the are the customer willing to
pay you? Can you deliver a lot of value
to them just like D was like a pretty
much a stronger mentality against
competition. But again as you scale you
need to do differentiate and everything.
Then after Zepto the real competition
happened between us and one of the
leading companies that you were seeing
at Dash. We have like eight people going
against this 400% wellunded company and
we won it against them and as you know
Dash is one of the massive support
logos. That's when we truly like us
realized that there's a lot of arbitrage
of actually building a great product
rather than a sales team.
>> So okay so that's kind of crazy right?
You you won Door Dash's contract. you
were only a team of eight people when
you did that. Why was it that Door Dash,
which is a huge company, was willing to
trust you guys when you were only eight
people? You could imagine they're like,
"Oh, these guys are too small. They'll
never, you know, they'll never trust
me." Like, you know, 10 years ago, the
assumption would have been that big
enterprises would never buy software at
that scale from a startup. It seems like
that was no longer the case for you. Why
do you think that is?
>> I think u you got to have some unfair
advantage, right? for us YC was that
Gary introed me to uh Tony and it's a YC
company and Doash has a basically a YC
company so it's just like inherent trust
to it and again it's I mean we piloted
for like 3 months we never went down and
all the metrics were good and Doash is a
very meritocrative company and I really
kudos to them and it's a company at that
scale picking a small company would be
hard and now a lot of companies pick us
because of Doash and a lot of other big
public companies users yeah
>> so how is that what does the arc look
like since then. So you got companies
like Zeppto and Door Dash in the early
days. What does your company look like
now and how have you had to evolve what
you're doing over the last uh few years?
>> Yeah, last few years uh I would say
we as I mentioned we work with the
biggest crypto exchange in US and after
getting like a customer at that scale we
uh we are working with a lot of fortune
500s etc like trying to automate the
support. Another big interesting thing
that I have observed with AI agencies is
it's fundamentally boiled downs to two
things. This is for true for almost any
agentic company. It's like a policies or
the markdown file and how can you
iterate the markdown file to affect a
business KPI that is support for support
is resolution rate or like seesat the
all thing that matters is you let's say
we start at like 30 to 40% resolution
rate how to get to 90% how can you
iteratively improve to get there the
same fundamentals apply for compliance
ITM ITSD and everything so we're seeing
customers some of the biggest uh
consumer companies in the uh US piloting
us for internal support even compliance
and etc. It's very interesting with a
agents. It's fundamentally bogd down to
markdown and like how can you
iteratively improve markdown to move a
KPI. Okay. So I want to change the
change gears here a little bit and talk
a little bit about the advice you would
give for this people in this room.
Right. So the people in this room are
predominantly college students or young
people recently out of college. you
know, if you were rewinding back to your
time as a college student, is there any
advice you would give that's maybe
different from the advice that they
might be hearing from their peers or
what is kind of the norm, you think?
>> Yeah, I mean like a lot of people
thought I was stupid when I rejected the
job offer. I mean, like it was so insane
because um
>> they were like, "You're turning down
this great offer from a quant firm. Like
why would you do that?" Yeah,
>> it's just for me and my co-founder, we
just wanted to give it a shot. We
thought like we just wanted to see how
how how high we can go. That's pretty
much like a thing and that's the reason
that's the pretty much um the entire way
that we build the company like yeah as
scaling like even den uh acquisition
office from some of the biggest
companies in the world. That's how we
pretty much like build entire company.
We just wanted to reach to our potential
and see is this the best we can do and
push it. It's kind of more than me. It's
my co-founder's mentality. He's
fundamentally like a zero motivated by
money person. So yeah, that's where it
came from. And I read Paul Gram's essay
on how to do wealth. It essentially
boils down to doing a company and having
or like doing equity in something big.
>> That's pretty much it. Yeah.
>> What did like your parents think about
that in those early days? Like were they
excited? You you said, you know, you
grew up in a very modest upbringing.
Presumably getting this highpaying job
would have been life-changing for your
family. Like how did how did that go?
That conversation? you're like, I'm
moving to SF. I'm going to start this
crazy thing.
>> My dad was super mad to be honest. And
it's just uh I mean, it's a it's kind of
like a big fight thing in home, but it's
fine. I mean like they're like what can
they really do? They can't like force a
kid to do something. But it just was
really like a hard conversation. I
actually showed them what YCU is, showed
them YC videos, and showed them these
are the companies that they do. And I
showed them like even if I don't succeed
in a year or two, I can just go back to
the job and do the thing again. When you
come from like a middle-ass family or
build a middle- class family, it's just
like a lot of expectation to do things
and especially when I got like a really
good job offer and my parents were all
happy about they I mean it's it's just
not my parents even internally even I
felt am I doing something wrong but
again my entire philosophy has been like
just take a shot and see if it works out
or not. So when you think about now
how college students or young people can
put themselves in a position to find
great startup ideas, what are some of
the lessons from your experience that
you think apply to the people in this
audience? The biggest thing we made at
least a mistake even after getting into
YC is uh we worked on a lot of stupid
ideas which didn't make any revenue or
doing anything. Uh for a long time I
mean like people just want have like a
lot of ideas. I can just go into charged
and get like 10 ideas on what to do
right. Uh it's never about the idea.
It's about if somebody is willing to pay
you money for it and is I mean I don't
even think about you need to care about
market to be honest. is somebody willing
to pay real money if you solve the
problem for the value that you
delivered. That's the strongest approach
we took and I made a lot of it took me a
lot longer to realize right now even for
our new products that we build we make
sure that the customer can actually pay
and we predict like this is the amount
you're going to pay and get a commitment
from the customer and then go and build
it.
>> And why is it that you know charging for
a product so early is useful. You know,
I think a lot of people in the audience
might think, "Oh, I'm just a college
student. No one's ever going to pay me
for something I make right now. I should
I should just get started for free or
something like that." Why does that not
tend to work in your mind?
>> If it's an important enough problem,
people would pay either with money or
with time. I would say, I mean, social
media networks are just time. They don't
charge you any money. But in general,
for any single B2B company, if the
problem is important enough, people
should be willing to pay money for it. I
was like you're just solving a fake
problem.
>> So you are also part of a new generation
of Indian origin founders who is
building your company both here and in
SF. How do you think about that? I mean
do you think about that or is there uh
any any particular framework you have
for how to expand your company and take
advantage of your background from India?
>> I think that you should just stay close
to customers wherever you are. But if
you're doing anything closer to like
genai and very like research based
things, I strongly think SF is a place
because the amount of access and you
know with the researchers and etc you
get is insane because almost all the
innovation in this geni field is getting
drive on Bay Area alone compared to
India. But again if your customer is
primarily based out of uh India it
should be here. What do you think when
it when you look forward to the future
and you think about, you know, all of
the pivots your company has gone through
to get to where it's at now? How do you
think what do you think the next few
years look like for your company? Like,
do you see yourself now? Do you feel
like you found the thing that's going to
become really big or do you see yourself
continuing to evolve? And and how are
you evolving yourself?
>> Yeah, I think I think we're I'm very
confident that we are moving in a great
direction. The biggest bottleneck of
every single enterprise a deployment
regardless of support or any automation
that they want is this concept called
forward deployed engineer. You need to
have them u bunch of them coming and
sitting with the customers and
configuring it. We're trying to build an
AI forward deployed engineer. We're
going to launch it soon. Yeah,
>> and that is where I would say that I'm
very competent on because whenever you
want to make these policy changes
whenever you want to like just spin up a
new dashboard for me or how can I go
from like 40 to 60%. Our aforemention is
going to join is on slack is going to
join Google meets and take all the nodes
and do the changes automatically. So I'm
very confident on the direction that we
are moving on uh how we are going to
tackle uh AI adoption in enterprises
because the biggest bottleneck right now
is forward deploy engineer and we're
going to take it over.
>> Could you tell us a little bit about how
your company actually runs internally
using AI also like you me obviously your
product is very AIdriven but in terms of
the company itself like what are the
tools that your engineers and
salespeople and whatnot are using what
does that actually look like? It's funny
because we have a one of our values is
automate, automate, automate. So we
force people to like use as much
automation as possible and the in
general the boral mission of the company
is to automate all of the world's work.
We are intentionally like moving into
that direction of generic automation
builder able to automate anything on top
of us. Some of the examples were you
don't need to have a personal assistant
just add it open cloud runs and
schedules the meets for you to actually
like uh sales people use it very
interesting. They pull transcripts from
Kong to do analysis of what are the
biggest things that work working against
a specific competitor across like a
multiple things. So yeah, everybody's u
that's the one thing I love about cloud
code. It turned a lot of people into
builders
>> and people are using it innovatively to
drive specific insights that would have
like taken like humans go through a lot
of things and doing it.
>> Yeah. I mean, if coding agents didn't
exist, how many engineers do you think
would be at your company versus what it
currently is? Like, how much what would
the company look like without all of
that?
>> I think they should be at least like six
to seven times more compared to now.
We're relatively like a very small
engineering team. It's like a very
strong talent and very small engineering
team.
>> That's crazy, right? Like 7x the number
of engineers you would need if you
didn't have one tool, which probably
costs you much less than seven times
your engineering team.
>> It's it's a it's more than cost. It's
just it's better w without context
switching. It's better for you to own
the thing and build the entire thing
rather than having like a lot of people
working on it. You can just ship much
faster and the context transfer actually
kills a lot of things and slows down
things.
>> So given that I imagine the kinds of
people you look to hire on your team
also are very different from the average
SAS company 10 years ago or at least in
some ways different. I'm curious how you
would describe that. Are there things
that you guys look for that are maybe
different than what like a big tech
company who doesn't use that many that
much coding agents yet uh might look
for?
>> Our interview process specifically
designed we ask people to wipe code and
remove AI and ask them to like change
the code. So we
>> you actually have them vibe coding in
your interview process.
>> We ask them to wipe code and then remove
access to the tool and ask them to
change the code without AI.
>> It's intentional because we want people
to understand the code as well on how it
works etc. So we are I mean again this
is also keeps evolving as AI models keep
getting on better and better do we
really know to know how the code works
even if cloud does the entire thing but
that's how that's where we landed on
generally the things which we look are
some sort of like extraordinary ability
and spikiness that we can like relate
into for me and my co-founder that for
us was me having the highest uh one of
the highest offer jobs my co-founder was
third in the entire and um he also got
the I think the highest paying job offer
in India in a in a quant firm and we're
trying to look like a very spiky things
on
>> which is the.1% of people would do and
that's how like we're generally going at
things.
>> Yeah.
>> So, you know, I think a lot of people
might look at themselves and say like
I'm a technical person, I'm an engineer,
but like what do I know about business,
right? Like I don't have a business
background necessarily. How did you guys
think about that? Right? You and your
co-founder are both computer scientists.
I assume you did no business before you
started this company. What has been your
experience on whether that's that that
preexposure matters whether if you just
kind of learn it as you go?
>> I mean there are a lot of people who
will buy your product without business
background. You just got to find the
right buyer. I mean like Zipa is one of
those companies they didn't care. You
have like a bigger salespeople. Doash
doesn't care about sales people. You
just got to find your ICP.
>> But I guess I mean more for you guys as
founders like in your own founder
ability. Do you feel like it would have
benefited you or do you feel like your
technical abilities allowed you to get
somewhere in itself and then you know
that's what kind of gave you the
opportunities?
>> I I personally like bias a lot towards
builders and sellers because uh this is
the mistake I made while starting the
company. Me and my co-ound had this huge
debate because I thought sales was the
most important thing in the company. I
was so wrong in like so many ways. If
you take a look at all the successful AA
companies is product none of them are
succeeding. I mean nobody uses anthropic
for the best sales team. I don't think
that pay I mean anthropic and open air
doesn't even pay sales people
commissions. That's how they don't care
about like a sales and I think with AI
product is the most important thing. How
how how good is your product at
delivering a lot of value to the
customer at short amount of time. If you
can prove that everything else should
follow through.
>> Yeah. Okay. We're almost at time here.
I'm curious if you have any parting
final thoughts or reflections. you know,
you've now been at this for 2 or 3
years. You're no longer in school.
You've been spending your time split
between San Francisco and Bangalore. Are
there things that you now realize about
how the world works
um that maybe weren't obvious to you
when you were getting started and you
can leave people with some parting
advice.
>> Yeah, the biggest thing is just getting
started and trying to sell the thing is
just getting started and jumping the
thing and burning the boards is a good
thing to start. it it is only like
really valuable and things get really
real if you burn the boats. That's when
I really felt because we know that when
the company was not working, me and my
co-founder were thinking, "Oh my god, we
rejected all these job offers. What are
we going to do?" It actually forces you
to make things. And I think it's not
like really really burning boards per
se. If you have a job, you can get a
job. It's not going to go anywhere. But
in general just like going at it and
like doing things has like a lot
stronger value. Especially with AI it's
just yeah
>> the cost of building things is so low.
Uh people should just build things and
try to like uh deliver as much value as
they can to very small set of customers
and see if they can pay their money.
>> Awesome. Thanks so much for coming
Varun. Really appreciate it.
>> Thank you.

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