The Anjali Sardana story is one of the most absurd startup trajectories I’ve seen this year. She’s 23. Georgetown biology grad. Worked at Bain Capital and 8VC as a private equity investor. Could have stayed on the guaranteed path to seven figures by 30. Instead she flew to India in early 2025 and noticed something: 190 million Indian households need domestic help. Somewhere between 20 and 90 million people work as house cleaners, cooks, and laundry workers. And the entire market runs on word of mouth, building guards, and WhatsApp groups. Zero infrastructure. Zero quality control. Zero income stability for workers. She launched Pronto in Gurugram with a single hub in Sector 56. She and her team literally slept on the office floor to make sure the first 170 daily bookings got fulfilled. Workers arrive within 10 minutes. Every “Pro” goes through a 5-day in-person training program, background checks, and a final exam. For every 300 applicants, 50 make the cut. Nine months later: 18,000 bookings per day. Over 3,000 active Pros. 10+ cities. The top 1% of customers use Pronto 23+ times per month. Median time between first and second booking: two days. The funding trajectory tells the whole story. $2M seed at $12.5M valuation. $11M Series A at $45M three months later. $25M Series B at $100M six months after that. $40M total raised. Sardana still owns 40%. The market math is what makes investors salivate. India’s domestic help sector generates tens of billions in annual wages, almost entirely in cash, with no formal contracts, no labor protections, and no platform taking a cut. General Catalyst’s Rahul Garg sized it at a $35B wage pool across 35 million semi-skilled workers. Pronto’s customer acquisition cost: Rs 400 (about $5). And she runs a largely variable-cost model. No dark stores. No massive capex. Referrals are her biggest growth channel because her workers actually like working there enough to recruit their friends. She left the guaranteed path to wealth to go sleep on an office floor in Gurgaon and solve a matching problem in a $35B informal labor market. That’s what actual conviction looks like.