Why We Invested in Hunar

The problem with managing the frontline

India is building some of the world’s most sophisticated frontline businesses. Quick commerce, logistics, at-home services and battery swapping are creating new ways to deliver goods and services, with operations spanning thousands of locations and a workforce that speaks different languages, operates in different contexts and is often constantly changing.

Consider hiring a single delivery executive. Someone needs to find a candidate, call them, explain the opportunity, follow up on documentation, complete onboarding and ensure they show up for work. If they drop off at any stage, the process starts again. The same effort repeats across training, shift management and retention.

Now multiply this across 250 million blue- and grey-collar workers in India, spanning logistics, retail, manufacturing, BFSI, QSR and other frontline-intensive industries. High attrition and geographically distributed operations mean businesses must employ large teams just to keep their frontline workforce running.

Traditional software can record a candidate’s details or flag an absentee, but the actual work of persuading, following up, resolving issues and getting someone to show up still falls on people. For businesses built on frontline execution, this is not just an HR problem. It is a constraint on growth.

Why Hunar, and why now?

Krishna and Shantanu believed in this opportunity well before AI made it viable. They built a human-led frontline recruitment and staffing business, taking a bet on a problem that was important but difficult to solve profitably at scale.

Their economics depended on getting better with every hire: the fewer hours spent finding and onboarding a candidate, the more workers they could place with the same team. Over time, this meant accumulating conversations, candidate context, workflows and practical knowledge about what it takes to get frontline workers hired and onboarded. They were not setting out to build an AI company; they were trying to make their existing business work better.

Then AI caught up with the problem.

Years of execution had given the founders something difficult to replicate: conversations, candidate context, workflows and a practical understanding of why people drop off during hiring. This became an unexpected advantage when AI matured. Their data helped inform better models, while their operational experience shaped the workflows and interventions needed to turn those models into hiring outcomes.

A generic voice AI agent can make a call. Hunar goes further, bringing together an understanding of the role, the candidate and the hiring workflow to move people from initial interest to onboarding. This is the strength of its vertical approach: building around the outcome, rather than simply providing voice infrastructure.

The approach is already seeing adoption. Hunar powers over 500K conversations a day, with volumes growing 46x in 12 months. Its customers include Swiggy, Zepto, Delhivery, Blue Dart, Pronto, Flipkart, Instamart, Aditya Birla Capital, Bajaj Finance, Navi Finserv, Tata Croma, Tata 1mg and Dr. Lal PathLabs, among others.

The customer outcomes are equally encouraging. Swiggy has seen a 37% reduction in recruiter bandwidth for the same work. At Tata Croma, HR bandwidth for frontline sales hiring and onboarding has reduced by over 90%, alongside a 7% increase in first-three-month sales productivity. At Pronto, 10% of total partner leads are onboarded without manual intervention.

We see the opportunity extending to other markets with large, distributed frontline workforces, including Southeast Asia, the Middle East and Latin America.

We are excited to back Krishna and Shantanu again, having known them since their Locus days, as they build from India for the world.