Projects that
ship & scale.

Healthcare AI is bottlenecked by data scarcity and privacy constraints. I wanted to prove that synthetic, privacy-preserving patient data could unlock the next generation of medical ML models, without compromising a single real patient record.

The healthcare industry generates enormous amounts of EHR data but processing it at scale remains painfully slow. I built SynMedix to demonstrate that with the right distributed architecture, medical AI teams can iterate 3x faster on realistic, privacy-safe datasets.

Financial regulators produce vast amounts of structured data that remains trapped in siloed, poorly formatted sources. I wanted to build a pipeline that turns raw AMFI data into a reliable, analysis-ready dataset that financial engineers can actually trust.

Early-stage fundraising is unnecessarily opaque. I wanted to build a clean, structured platform where the information asymmetry between founders and angels is reduced, making the matching process feel less like luck and more like informed decision-making.

Energy waste in homes is invisible. Nobody knows they are leaving devices running until the bill arrives. I wanted to build a low-cost IoT system that makes residential energy use transparent and actionable in real time, not a month later.