EVERY SPOKEN WORD
40 min read · 8,349 words- 0:00 – 0:40
Intro & Origins of Giga
- HTHarj Taggar
[on-hold music] Today I'm here with Varun and Esha, co-founders of Giga. Giga build AI agents for customer support and are used by some of the largest companies in the world, like DoorDash, to handle millions of customer support calls. Varun and Esha, thanks so much for joining us.
- ESEsha
Thanks, Hutch.
- VAVarun
Thanks. Thanks for having us here.
- HTHarj Taggar
This is a very competitive space, AI agents for customer support. Um, there's obviously very well-known and well-funded companies in the space, like Sierra. So how has Giga, um, been able to stand out, uh, and get an amazing customer like DoorDash?
- VAVarun
Firstly, st- starting off with, like DoorDash is a extremely meritocratic company, and that's one of the massive
- 0:40 – 2:25
The Problem with Customer Support Today
- VAVarun
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 Sierra is we built product and they took a m- basically more of like a consultative Palantir-type approach, which takes a lot of time to go live. If you consider DoorDash 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 a 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 Esha can add more along the complexity side of parts.
- ESEsha
Yeah. Just, just, you know, like, just so you know, the-- although like customer support seems like in a fairly easy use case to solve, uh, but for companies at a scale of DoorDash 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 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.
- HTHarj Taggar
Many other com-- AI companies take this sort of forward deployed engineer consulting approach, um, where they're almost building some amount of like custom-
- ESEsha
Yes
- HTHarj Taggar
... software per customer to get it working. But you've been able to build something that mostly worked out of the box, even for DoorDash's scale.
- ESEsha
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
- 2:25 – 5:10
What Giga Does and Who It Serves
- ESEsha
for DoorDash, 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.
- HTHarj Taggar
The reason I feel like most companies take those Palantir forward deployed engineer model approach is because it's essentially too hard to do that.
- ESEsha
Yes.
- VAVarun
That's the right side.
- HTHarj Taggar
There's so much like custom work you need to do per customer-
- VAVarun
Yep
- HTHarj Taggar
... 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?
- ESEsha
So this is a bet we took a few... I, I would say like pretty much like eight to nine 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 gonna 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 DoorDash 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 gonna 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.
- HTHarj Taggar
Okay. So that's interesting. So it's almost like the forward deploy engineer model is basically there's business logic that lives in the head of the customer's ops team or whoever. They have to-
- ESEsha
Translate
- HTHarj Taggar
... translate it. It's basically the forward deployed engineer's role is to like translate that business logic into code.
- VAVarun
Code.
- HTHarj Taggar
But you've actually just got AI doing it.
- ESEsha
Yes.
- HTHarj Taggar
And so it's like the business logic is natural English. They-
- ESEsha
Yes
- HTHarj Taggar
... they're typing it into your system-
- ESEsha
Yes
- HTHarj Taggar
... and your system is turning it into Python code that makes sure that the underlying product works perfectly for each enterprise customer.
- ESEsha
Exactly. And it need not be Python as well. We convert, like for DoorDash, we convert tens of thousands of lines of JSON files, their internal JSON files, into actual natural language, w- 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
- 5:10 – 8:15
Building Emotionally Intelligent AI Agents
- ESEsha
instead of like a human actually doing it end to-
- HTHarj Taggar
Yes, you do, you do have a forward deployed engineer. It's just sort of like an AI forward deployed engineer versus a human one.
- ESEsha
Yes.
- HTHarj Taggar
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 fine-tuning of LLMs.
- ESEsha
Varun did like research internship at Stanford for training these like LLMs back when like even ChatGPT-
- HTHarj Taggar
Yeah, this was pre-ChatGPT launching, right?
- VAVarun
Yeah. It's a-
- ESEsha
Like-
- VAVarun
It's like-
- ESEsha
Transformer models
- VAVarun
... it's not... Transformer. It's not, not, not really a lot, but yeah. Roughly, yeah.
- ESEsha
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 2's context length from four to 32K. We outperformed Claude 2 and everything. That immensely helps us because we know exactly what AI can do and where it's gonna not-- where it's gonna like, you know, just blow up. And we will only automate the parts where we know AI can do really well. And especially when-Then 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.
- HTHarj Taggar
So going back to the DoorDash contract, so the, the speed in sales matters a lot.
- ESEsha
Yeah.
- HTHarj Taggar
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.
- VAVarun
That's definitely one of the ma- massive reasons. Other massive reason is just complexity of the use cases.
- HTHarj Taggar
Yeah.
- VAVarun
That one thing which I want to say is, I mean, this is a very common use case in DoorDash which happens a lot, which no other vendor in the market was able to solve, which is let's say if we're 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 at my 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 mark it as delivered because it will be triggered by the fraud system because they're outside the geo fence.
- HTHarj Taggar
Hmm.
- VAVarun
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 AI 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 Dasher is in the call with our AI 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, Harj, the Dasher 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, uh, one multi-party communication and we... It's live right now in DoorDash. So these are some of the complex use cases which we solve. That's one of the re- That's one of the primary reasons that we are able to win over a company like DoorDash, is primarily ability to solve very complex use cases, which stems from ability to write Python.
- ESEsha
Yeah. It's like, you know, solve this extreme... 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
- 8:15 – 11:45
Real-Time Responses at Enterprise Scale
- ESEsha
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 much more, much more better, and then, like, we are the only one who can do it, like, at such scale with this
- HTHarj Taggar
And in this case, the com- the complexity in that example is that you've essentially got, like a sub-agent or two calls happening in parallel and they both need context on, on the-
- ESEsha
Yes
- HTHarj Taggar
... what's happening on each, in each call.
- ESEsha
Pretty much. It's like, you know, "Harj, like, did you actually receive your burrito you ordered to YC's office?"
- HTHarj Taggar
Yeah.
- ESEsha
"Because the Dasher is claiming that he delivered it to your home. Is that actually true?"
- HTHarj Taggar
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-
- ESEsha
Yes
- HTHarj Taggar
... that what happened-
- ESEsha
Yes
- HTHarj Taggar
... and then that agent knows to tell the Dasher-
- ESEsha
Yes
- HTHarj Taggar
... "Hey, this is totally fine."
- VAVarun
And actually marks the order as delivered. Yeah.
- ESEsha
And then the, the funny part here is, like, previously, yes, humans used to do it. It was a wo- 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 gonna put you on hold. Let me talk to the customer," and then put the customer on hold, talk to the Dasher.
- HTHarj Taggar
Yeah.
- ESEsha
So it's basically a better experience.
- HTHarj Taggar
This is, this is... I'm smiling because this is sort of like the, um... It's sort of like the enterprise customer support version of Her, [laughs] the movie.
- ESEsha
Maybe, yeah. [laughs]
- HTHarj Taggar
[laughs] 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?
- VAVarun
Yeah. The first start 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 you do six hours. I mean, yeah, firstly, hold times go away. That's like the biggest thing for all the thing, all the AI agents. And the second thing is that 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, uh, uh, DoorDash, 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've 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, fundamental. Like the CSAT also went, like, significantly up for the, for the Dashers. Yeah.
- HTHarj Taggar
Okay. So now you've raised this huge Series A. Um, w- why have you raised a round right now, and what do you plan to do with the money?
- VAVarun
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.
- HTHarj Taggar
You actually have one of probably the strongest sales pipeline of any startup I've seen. [laughs] Um, how has that happened?
- VAVarun
Firstly, our contracts, as you know, are, like, very massive compared to, like, a lot of startups on our scale, and that means that we generally tap to, like, C-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 C-level execs who are friends. So I mean, if it's, it's Fortune 2000, how many people? There, there should be like 2,000 people if you hypothetically think of. If every one person refers you 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 are the real use cases, uh, 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
- 11:45 – 15:00
Designing for Compliance and Security
- VAVarun
Giga is becoming one of answers. I think that's the reason which we have, like, a stronger pipeline to another anything else.
- ESEsha
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, C-level execs, they are obviously very curious about the product. And the results we have, like, you know, delivered for DoorDash 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 on.
- HTHarj Taggar
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 and is enough to get a giant company like DoorDash as a customer, and then that's gonna 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 think-- I think that was very hard to do in the past, because most people who 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.
- VAVarun
It's definitely a 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.
- HTHarj Taggar
Yeah. Why do you think customer support has taken off and become such a competitive space?
- ESEsha
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 any 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, 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 reason, 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 LLMs. Um, and it's just gonna get, like, you know, as context gets better and everything, these models are gonna do everything.
- VAVarun
Yeah. And people try-- I mean, right now, people are like even pr-trying to proactively give more context to LLMs as well, even that also like solves the-
- ESEsha
Yeah
- VAVarun
... solves the issue and more and more use cases get unlocked. But customer support is definitely one of the-
- ESEsha
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.
- HTHarj Taggar
Yeah. The reason we want to spend so much time on just like the product and the AI for deployed engineer stuff, this is gonna be gold. 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 full deployed engineer model, and there's so much interest in that. So AI full deployed engineer is the first, like I don't think anyone's ever heard that before.
- ESEsha
Yeah. It's like if you're building an AI product, you need to build your own FDE.
- HTHarj Taggar
Yeah.
- ESEsha
Yeah.
- HTHarj Taggar
You guys, basically this
- 15:00 – 18:20
Human-Quality Conversations at Machine Speed
- HTHarj Taggar
is gonna position you as like at the cutting edge. Like it's basically like everyone else is sort of doing the human full 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 Giga is 20 people today, um, and you're gonna be hiring aggressively. How do you think about the culture you wanna build as the team grows?
- ESEsha
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 f- in, in our company is that we are one of the only companies, or maybe 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 gonna 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 impact driven. They need to have the drive to make a lot of these experiences really, really good.
- VAVarun
Yeah. It's, it's primarily a little bit of like a very high agency and very raw IQ is w-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 OpenAI and Anthropic, who were paying a, like ridiculously high amount than us to join Giga.
- HTHarj Taggar
Yeah. That says-- 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 OpenAI and the giant amounts of money that they pay?
- VAVarun
It's primarily directly because the amount of impact. See, the-- whether this was a backend engineer and one frontend engineer who were like getting competed by-- had both competing offers. The thing was, the impact that he's gonna make on any of these companies, I mean, OpenAI is a massive company, it's not a small company anymore.
- HTHarj Taggar
Yeah.
- VAVarun
And the impact he can have at Giga can d- the code literally which he did writes can be transformed to hundreds of millions of people. Potentially every customer of DoorDash will get a s- sim or like a, any one of these massive customers who work with us will get di-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 Giga. Again, but this is also not gonna last too long if it become thousand people. But again, right now is a sweet spot where you build something which impacts a lot of people. [laughs] Our primary mission of the company is, uh, to build a world towards perfect execution, and we'll try to optimize every single thing in these massive enterprises and try, try to optimize every process and e-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.
- HTHarj Taggar
So you think you'll go beyond, um, customer support at some point?
- VAVarun
Yes. 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?"
- HTHarj Taggar
Yeah.
- VAVarun
So we're already seeing a lot of interest, uh, in, in these things. And one, one more interesting category of people which we have seen work out for Giga very, very well are former founders who just want to have high agency and a lot of impact.
- 18:20 – 22:10
Lessons from Early Customer Deployments
- VAVarun
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.
- HTHarj Taggar
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.
- ESEsha
So we both know each other since like 2019 when we both went to IIT Kharagpur. Um, and then, like Varun is like really passionate about startups, I think like since he was like six or seven, and he has been reading Paul Graham 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 a-applied to YC in 2023, and you were our interviewer.
- HTHarj Taggar
Yeah, you applied with a completely different idea. [laughs]
- ESEsha
Applied with an-
- VAVarun
Oh my God. [laughs]
- HTHarj Taggar
Yeah, you should tell us about the idea. [laughs]
- ESEsha
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 te- te- Varun, you have this experience here. Can you talk about like what other companies can you build from this experience?" And then we were, we, we were, like, so surprised. We were like, okay, we-
- VAVarun
I mean, I, I rehearsed for this interview so much.
- ESEsha
[laughs]
- VAVarun
I mean, like, there are, like, 10 YC founders who interviewed me, and I know answers to all the questions, which is like, "Why this? What's the big tam and everything?" And you come to the interview-
- ESEsha
[laughs]
- VAVarun
... and you ask her a completely different question-
- ESEsha
[laughs]
- VAVarun
... which is like a pick a new idea.
- ESEsha
[laughs]
- VAVarun
And I was like, "What?" And it, it's, it's a, it's a-
- ESEsha
Yeah. And then, and then our, our thought process was like, okay, YC likes people who are very gritty-
- VAVarun
Yeah
- ESEsha
... so we should stick with the idea we came in with.
- VAVarun
[laughs]
- ESEsha
We're like, "No, Harsh." 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 YC doesn't take you, are you gonna keep doing your company?" And then we said yes, but I think you knew [laughs] we wouldn't have done it.
- VAVarun
No. [laughs]
- ESEsha
Um, yeah. I think YC is one of the reasons, like, we even exist at this point in time.
- VAVarun
I- is, uh, honestly, like kudos to you for just taking a bet on us. Uh, I don't thi- I mean, it, we got into this in some stage. I mean, like, we had, uh, uh, we both had, like, uh, high-frequency trading offers-
- ESEsha
Yeah
- VAVarun
... w- paying us more than 600K out of college. And [laughs] I applied to a bunch of Ivy Leagues, and I got into [laughs] all of them. And at that point of time, YC was always this, like, North Star who, one company who, I mean, I thought that YC was one of those things which creates this massive generation of companies. And-
- HTHarj Taggar
Yeah, and that was always something that stood out for me. I was always curious about this. I don't think I've asked you. But yeah, like, I mean, I feel like tr- turning down quant jobs if you're in, in America, in Europe and America is sort of like one level of risk to do a startup.
- ESEsha
Yeah.
- HTHarj Taggar
But by being, growing up in India and, like, turning down, like, super high-paying jobs [laughs] offers-
- 22:10 – 26:45
Raising $61M to Power the Next Generation of Support
- ESEsha
logical conclusion I came to. I was like, "Okay, sounds good. Let's actually give it a shot," and then, yeah.
- VAVarun
And 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 [laughs] their own business, so I thought I wanted to do something around those lines, and I thought it was super hard. And yeah.
- ESEsha
It's kinda surprising because this aspect of, okay, we wanna pick the hardest problems to solve, kind of translates so well, because we go into the sales calls, and then we talk to these, let's say, C-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 a 98% of your issues. The biggest reasons humans still say, like, agent- a human, 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?
- HTHarj Taggar
To me, it's just like the, the era to build a startup now is different, feels different post-ChatGPT-
- ESEsha
Yeah
- HTHarj Taggar
... than before. And in particular, it's just there is a real return to being a, a deeply technical founder. Um, and you can just build your company a different way. By going in, I think it's, going in and just being like, give us your... Like, building a whole sales process around just give us your most complicated, like, most complex-
- ESEsha
Yeah, and then I'm gonna show it to you that we can do it.
- VAVarun
Yeah. [laughs]
- ESEsha
Yeah.
- HTHarj Taggar
Yeah, I remember. So I remember the interview. I remember you guys being very stubbornly attached to your [laughs] like, the college student idea. Um, talk us through, like, actually, um, going through YC and you, you, you even had, like, difficulties getting into the country.
- VAVarun
Yeah.
- HTHarj Taggar
Just tell us the whole journey.
- VAVarun
No, uh, firstly, I was more curious on why did you take us in?
- HTHarj Taggar
The cool 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, the two of you are, like, extremely high IQ, um, uh, extremely determined. Um, but yeah, the fact that you were, you had mentioned that you were turning down the HFT 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 i- in general, all 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 are 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 are 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 the sort of... Yes, like, I didn't like the idea. It was probably very obvious.
- ESEsha
[laughs]
- HTHarj Taggar
Um, and it is a bit of a high-risk strategy to keep defending the idea anyway.
- ESEsha
Yeah.
- HTHarj Taggar
But I [laughs] ...
- ESEsha
[laughs]
- HTHarj Taggar
But I, I've, 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 stubbornly 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-
- VAVarun
12. [laughs]
- HTHarj Taggar
Yeah, it wasn't really possible. Um, but he just defended it so intensely. I remember in the interview thinking, like anyone who's this smart and this intense, you just sort of have to invest in and work with them.
- VAVarun
Mm.
- HTHarj Taggar
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-
- VAVarun
Yeah
- HTHarj Taggar
... as well. But I just... That, that intensity I think is quite rare. That's why, that's why we funded you.
- VAVarun
Yeah. Uh, coming to the YC thing, uh, yeah. We thought it would be so easy to get into US, to be honest.
- HTHarj Taggar
Yeah. [laughs]
- VAVarun
That we'd go there. [laughs]
- ESEsha
No, no, no. In, 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."
- 26:45 – 30:15
What It Takes to Build for the Enterprise
- VAVarun
And I was trying to explain what YC 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.
- ESEsha
Oh, yeah.
- VAVarun
I was like, "What's happening with my life?" It's just, um...
- HTHarj Taggar
Yeah, I remember. So then, 'cause this was for the summer '23 batch.
- VAVarun
Yeah.
- HTHarj Taggar
And so then we basic- You had to do the batch remote, uh-
- VAVarun
Yeah
- HTHarj Taggar
... essentially, right? So I didn't actually meet you in person, um, probably-
- ESEsha
Until, like January 2024.
- HTHarj Taggar
Yeah. So it was kind of... Which is getting close to, like, almost close to a year since, um, the, the first interview. Probably like nine to 10 months-
- ESEsha
Yeah
- HTHarj Taggar
... after the interview.
- VAVarun
The insane crazy thing was everything became harder, right? I mean, we don't know what our peers were working on-
- ESEsha
Yeah
- VAVarun
... and it's, it's, uh, we... I mean, it's, it's our first time trying it out as well. Then we put it to fine-tuning, and we picked a really vertical where we can nail it.
- HTHarj Taggar
That definitely... But on that note, it definitely felt to me like the you getting here, being to Silicon Valley, and being here at YC in person-
- VAVarun
Yeah
- HTHarj Taggar
... definitely really helped accelerate the process-
- VAVarun
For sure
- HTHarj Taggar
... of finding, like, the new idea, right?
- VAVarun
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 Garry Tan the reason the fundraise happened, right? I mean, I, I was asking my peers how much money they were, like, raising, and they're like, they're like 60 interviews scheduled for everybody.
- HTHarj Taggar
Yeah.
- VAVarun
I remember asking you, I was like, "Harj, I have only eight [laughs] eight investors. Am I doing anything wrong?"
- HTHarj Taggar
This is for your Demo Day fundraise?
- VAVarun
This is for Demo Day fundraise. And you were like, "No, no, you just go with it. Just go with the flow and everything." [laughs] And almost surprisingly, almost all of them end up investing. And Garry Tan told from our seed investor from, uh, Nexus, "You must invest in Giga." And they took a bet on us, and it's, uh... I mean, that really, that fundraise really helped us a lot because we're back in India and it's-
- ESEsha
Yeah. I think, like, just having zero peers, and then, like, we both like, 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.
- HTHarj Taggar
[laughs]
- ESEsha
And then, and then we spoke to a few people. We were like, "Okay, yeah, it doesn't make sense. Let's pick something where we are good at." We pick, like, fine-tuning, and then the inference, that vertical. And then, yeah, it's majorly because of Garry, I think, our seed got done, pretty much. [laughs]
- HTHarj Taggar
I remember... So, like, the timeline was basically you apply with the student idea, you pivoted into the fine-tuning as a service idea k- like-
- ESEsha
In a, in a month.
- 30:15 – 33:40
The Future of Customer Experience
- ESEsha
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 OpenAI continuously made better models and cheaper models, we would be doing worse.
- HTHarj Taggar
Yeah.
- ESEsha
So that realization we got, I think, in February or-
- HTHarj Taggar
Yeah
- ESEsha
... March 2024, we were like, "Okay, we don't wanna bet against AI. Let's just go into something where we bet on AI."
- HTHarj Taggar
Okay. Um, and then how did you come up with the idea for Giga?
- VAVarun
We were pivoting and trying out, like, a bunch of different ideas, and one of the... Some of the thing we're... At, at a point, we were trying to even be a software engineer. So [laughs]
- ESEsha
Yeah.
- VAVarun
We're, we're definitely, like, jumping on, like, a lot of ideas. Again, Nexus might introduce Zepto 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.
- HTHarj Taggar
Oh, so you got... You were introduced to Zepto, and you just went to talk to them in general about, "Hey, like, what are your-"
- VAVarun
Yeah
- HTHarj Taggar
..."problems using AI?"
- VAVarun
We, we, yeah, we tried, like, a little bit of hypothesis. I think there's 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. I just made a hypothesis that Zepto might also [laughs] have similar problems.
- HTHarj Taggar
Yeah.
- VAVarun
It was a shot in the dark, and it kind of worked. [laughs]
- ESEsha
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 APIs, 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 Zepto we built out.
- HTHarj Taggar
And that sort of laid the foundation for the product as you have today?
- ESEsha
Yeah.
- HTHarj Taggar
Yes.
- VAVarun
Yeah. At that point of time, we realized that we are not gonna do consulting again [laughs] .
- ESEsha
Yes. Oh, yeah, no. We were like, "Okay, this is no way gonna scale."
- VAVarun
Yeah.
- ESEsha
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 Zepto 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.
- HTHarj Taggar
Okay. So if we look ahead to the future a little bit, um, I mean, you guys are 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, a-especially as the models themselves get more powerful?
- ESEsha
This is the bet we are also making. Um, data and context is the, are the most important things, and AI gets better. The companies with the most context and data will be able to perform the best. Even if, like, OpenAI had a much smarter model, but it do- 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,
- 33:40 – 35:55
Advice for Founders Building in AI
- ESEsha
like, you know, this is, this is maybe, like, you know, a decade away potentially, but we wanna build a platform where the next trillion-dollar businesses are built on top of, and we wanna 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.
- VAVarun
Since we have context from every single customer and what their past interactions and what are they looking-
- ESEsha
Yes, and then, and then-
- VAVarun
... and even like both, like, very micro and macro level view of-
- ESEsha
Yeah
- VAVarun
... what's, what all the customers are asking, it helps us solve other customers, other issues in the company very better.
- ESEsha
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.
- HTHarj Taggar
For Giga 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're, you aspire to, either because of something they've done in how they built the product or the size of the company today?
- ESEsha
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-
- HTHarj Taggar
[laughs]
- ESEsha
... but obviously, like, they built the most general version of CRM pretty much. And OpenAI, 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 wanna be the next OpenAI," or, "We wanna be the next Salesforce," or something like that.
- VAVarun
Different parts of different companies. Like, I mean, Anthropic for some re- I think has a very-
- ESEsha
Very good taste.
- VAVarun
Yeah, taste and a lot of great talent compared to OpenAI right now who are going to them, yeah.
- HTHarj Taggar
Varun and Esha, 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.
- VAVarun
Thanks, Harj.
- ESEsha
Thanks, Harj. [outro music]
Episode duration: 35:56
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