Kiran
Wondering about life on Mars while improving the one on Earth
I'm based in Bengaluru, India's Silicon Valley, where the best minds converge to tackle humanity's hardest problems. I come from a small village in Tumkur, about 180km from Bengaluru. As a kid, I was always fascinated by engineering and how the universe works - planets to stars to entire galaxies. That curiosity shaped the engineer I became.
I travel frequently, often alone or with groups of strangers. I love riding my bike on off-trails where Google Maps pretends navigation doesn't exist. Traveling sharpened my judgment and weakened assumptions. It reinforced a simple reality: systems that ignore local constraints fail quietly, while those designed to adapt endure.
I'm drawn to work at the intersection of autonomy, institutional trust, and long-term sustainability, whether on Earth or beyond. I focus on Earth-intelligent systems that preserve context over time, absorb uncertainty without losing accountability, and translate real conditions into outcomes institutions can rely on.
When I saw that satellites aren't built for a sustainable economy, I founded Quanmo, was part of Antler's AIR-6 cohort. Analogy was that we repair $30K cars but burn $2-5M satellites when the mission ends or something goes wrong. Quanmo built autonomous space systems to provide context from space to Earth & vice versa, to spot issues early, improve space awareness, and guide policy. The company's paused now, but those lessons still shape how I think about building reliable systems.
At Pixxel, I created Aurora, the company's flagship Earth Observation platform. The work reinforced a core lesson: data becomes valuable only when aligned with how decisions are actually made.
At Varaha, I am leading efforts on systems tied to climate outcomes, verification, and finance. The work operates close to institutional requirements and on-ground constraints, where sustainability depended on aligning incentives, trust, and economic outcomes.
At Mitti Labs, I led teams in fragmented, high-variance environments. Execution favored robustness, adaptability, and systems that could sustain impact beyond initial deployment.