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Giga: The AI Platform for Enterprise Support

Giga is building the next generation of customer support — real-time AI agents that can understand emotion, resolve issues instantly, and scale across the world’s largest enterprises. The company recently raised $61M to power its growth, combining contextual reasoning, secure orchestration, and sub-second response times to deliver human-quality conversations at scale. In this interview with YC's Harj Taggar, co-founders Varun and Esha share how they’re reimagining enterprise support from the ground up, what it takes to build AI for high-compliance industries, and why emotionally intelligent agents are the future of customer experience. Learn more about Giga: https://giga.ai Chapters: 00:00 – Intro & Origins of Giga 00:40 – The Problem with Customer Support Today 02:25 – What Giga Does and Who It Serves 05:10 – Building Emotionally Intelligent AI Agents 08:15 – Real-Time Responses at Enterprise Scale 11:45 – Designing for Compliance and Security 15:00 – Human-Quality Conversations at Machine Speed 18:20 – Lessons from Early Customer Deployments 22:10 – Raising $61M to Power the Next Generation of Support 26:45 – What It Takes to Build for the Enterprise 30:15 – The Future of Customer Experience 33:40 – Advice for Founders Building in AI

Harj TaggarhostEshaguestVarunguest
Nov 5, 202535mWatch on YouTube ↗

At a glance

WHAT IT’S REALLY ABOUT

Giga builds productized AI support agents scaling complex enterprise calls

  1. Giga won large enterprise customers like DoorDash by shipping a product that goes live in about a week rather than relying on slow, forward-deployed consulting implementations.
  2. The platform’s key technical wedge is an “AI forward-deployed engineer” approach: business users express outcomes in natural language and the system turns them into executable logic (via controlled Python/JSON-to-instructions) inside the product.
  3. Giga focuses on the hardest support edge cases (e.g., real-time multi-party phone workflows) to build trust and push automation toward ~98% resolution rather than stopping at partial coverage.
  4. The founders argue customer support is a current “sweet spot” for LLMs because the context window is bounded and can be supplied, making it more tractable than many general management tasks.
  5. They position Giga as a future ops-automation platform that expands beyond support by leveraging the compounding advantage of enterprise context, data, and cross-functional operational workflows.

IDEAS WORTH REMEMBERING

5 ideas

Productization can beat well-funded consultative competitors in enterprise.

Giga claims differentiation versus a Palantir-style approach by avoiding bespoke builds and enabling rapid time-to-live (e.g., a week vs months), which matters when enterprises have massive ticket volumes and many vendors competing.

Treat “forward-deployed engineering” as a software capability, not a services org.

Their bet is that AI will write much of the integration/business logic, so the product converts operator intent (natural language + internal configs) into code and protocols—reducing dependence on human FDEs and speeding deployments.

Make extensibility universal: no customer-specific features.

Internally they enforce a rule that nothing is built “just for DoorDash”; features must land in core product so complex enterprise requests become reusable primitives for all customers.

Win trust by solving the hardest edge cases first, not the easiest 70%.

Instead of optimizing for early coverage metrics, they lead sales with the most complex workflows (multi-party coordination, fraud constraints, outbound calls) and use that to earn confidence to automate the rest.

Multi-party real-time orchestration is a decisive capability in voice support.

Their DoorDash example runs parallel calls (dasher + customer) with shared context and actions (e.g., verifying address-change intent and marking delivery), outperforming humans who must put callers on hold.

WORDS WORTH SAVING

5 quotes

“We went live in a week.”

— Varun

“You don’t build anything custom for any customer. Everything has to be a part of the core product.”

— Esha

“It’s like… the FD part of a sales process is done by an AI.”

— Esha

“Humans cannot stay on call with two people at the same time. Now… the Dasher can still talk to the AI.”

— Esha

“I want to get every single one of my customers to 98% resolution… [because] there is no trust.”

— Esha

Product vs forward-deployed consulting modelDoorDash win factors: speed + complex use casesAI-generated business logic (Python as first-class primitive)Multi-party, parallel call orchestrationTrust, CSAT, and resolution-rate targets (98%)Why customer support is ideal for current LLM context limitsFounder origin story, pivots, and fundraising dynamics

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