GigaML builds AI agents for customer support and works with companies including DoorDash. Varun says traditional support systems deflect about 10 to 15% of requests, compared with 60 to 70% for AI, with a goal of reaching 90 to 95% for top customers.
Why Two IIT Engineers Turned Down $550K Jobs To Build A Startup
Speaker
Varun, co-founder of GigaML, discusses building AI customer support agents and the company’s startup journey with the Y Combinator host.
Varun grew up in a small town in Andhra Pradesh, studied electrical engineering at IIT Kharagpur, and began LLM research during college. He and his co-founder initially pursued an EdTech idea, but YC partner Harj encouraged them to choose another direction. They later built fine-tuning tools, released open-source models, and raised a $4 million seed round.
After about a year, the founders found that customer support and coding were the strongest use cases among their fine-tuning customers. They focused on support, with Zepto as their first customer. Varun says their eight-person team later won DoorDash’s business after a three-month pilot, which helped build trust with other large companies.
Varun says enterprise AI agents depend on iterating on policies to improve measurable outcomes such as resolution rate and customer satisfaction. He advises founders to test whether customers will pay before building extensively, and says GigaML now seeks customer commitments for new products. He also says the company is developing an AI forward-deployed engineer to help customers make policy and dashboard changes.
GigaML encourages employees to automate work, including using OpenClaw to schedule meetings and Claude Code to build tools for analyzing sales call transcripts. Varun estimates the company would need six to seven times as many engineers without coding agents, while stressing that understanding code remains important. Its interviews ask candidates to write code with AI, then modify it without the tool.
Takeaways
- Start testing whether a customer will pay while the product is still taking shape.
- Look for promising use cases in what existing customers already do with your product.
- Measure AI agents against business outcomes such as resolution rate or customer satisfaction.
- Keep technical teams small when AI tools help people own more of the work end to end.
- Build close to customers, and locate the team near the research or market it depends on.
Mentioned in this talk
Search inside this talk
What was claimed 11
- role at
“Varun from GigaML.”
YouTube Unverified1 source· Talk
- built
“We build AI agents for customer support.”
YouTube Unverified1 source· Talk
- metricAI agents for customer support: 60 to 70% deflection rate; aiming for 90 to 95% for top customers
“Deflection rates are closer to 60 to 70%. We’re aiming to get them closer to 90 to 95%.”
YouTube Unverified1 source· Talk
- featureGigaML: Human-like phone support that resolves issues without placing callers on holdHuman-like phone support that resolves issues without placing callers on hold
“you call, and it’s entirely like talking to a person.”
YouTube Unverified1 source· Talk
- metricVarun: Earned about $50,000 from Kaggle competitions
“I think I made a lot, like $50,000 or something.”
YouTube Unverified1 source· Talk
- metricGigaML: Raised a $4 million seed round
“we raised a $4 million seed round.”
YouTube Unverified1 source· Talk
- usesfirst customer for customer support
“Zepto was our first customer for that.”
YouTube Verified2 sources· Talk
- metricGigaML: Eight employees when competing for DoorDash
“we were eight people going against this very well-funded company”
YouTube Unverified1 source· Talk









