Vahan.ai

Vahan is an AI-first blue collar recruitment marketplace, building an aggregating layer across India’s highly fragmented blue collar worker supply value chain.

I was brought in to open categories that did not exist yet. Two of them, Warehousing and Manufacturing, went from nothing to roughly ₹1Cr ARR each at about 1,000 placements a month.

Building the categories

I did the first transactions myself in each category before hiring anyone. That is the part people skip. You cannot design an operating model for blue collar hiring from a spreadsheet; you have to sit with the logistics manager who needs 40 loaders by Monday and find out what actually stops him from saying yes.

Once the model held, I hired and placed the first team, then owned the full partner lifecycle: acquisition, onboarding, retention, and engagement.

The supply side is the business

In a two-sided marketplace for labour, demand is comparatively easy to find. Supply stickiness is the real liquidity metric, so that is where I spent most of my time.

Three things moved the number:

• Lead generation partnerships with JobHai, Apna, and WorkIndia to feed the supply pipeline for categories that had no organic funnel yet.
• Vahan Leader acquisition, recruiting and onboarding recruitment agencies as vendors. I designed a referral channel that sourced high-intent vendor leads from ex-supply operators sitting outside any conflict of interest. Month-3 partner retention went from roughly 50% on other channels to 65-70%.
• Worker lifecycle interventions to lift 30-day retention in Warehousing from 40% to 55%: pre-onboarding tele-calling nudges, same-day query resolution after onboarding, AI chat for routine queries, early salary access, and in-app community building through the AHA app.

I ran cohort and retention analysis on the supply base continuously, and built the dashboards, workflow tooling, and operations instrumentation the new categories needed to be run and monitored at all.

AI product work

I built and piloted product prototypes: one aggregating hiring demand from small manufacturers, another giving bulk recruiters a way to manage and amass candidate leads. Several pivots before the manufacturing segment showed real pull.

Also

Led a Human Egocentric Data collection project, 500 hours of egocentric data gathered for AI training.