About

In my 2nd year at NIT Kurukshetra, I started SynthAiLabs (OpenEdu) — part startup, part movement. I hand-selected 500 students from 2,000 and built an accelerated learning community. Students were completing 6 months of curriculum in just 2.

The revenue model was B2B: companies could distribute coupons to students, and I'd sell outsourced learning materials and software to companies training their employees. But funds were always the bottleneck.

What happened after I stopped was the real validation — juniors restarted and continued it on their own. It had given them something worth continuing.

I co-founded AuraX, a fashion-tech startup incubated at IIIT-Hyderabad's CIE. We researched diffusion models for virtual try-on and built a pipeline that, in our quality benchmarks, outperformed Google's and Stable Diffusion's published results — along with the Indian competitors we tested against.

We signed a deal with Aditya Birla Fashion Group and several brands under Mensa Brands. Real revenue. Real contracts.

In October 2025, Google DeepMind's vision team in Bangalore came to me for a research engineer role, the loop cut to one technical interview. I said no. We were mid-roadmap, and I would rather build my own thing than take a research seat in someone else's lab.

But the space moved fast. Larger players with deeper pockets entered. We ran out of runway. I made the call to close AuraX — not because the tech failed, but because the timing wasn't on our side.

After closing AuraX, I took intentional time off. I studied finance to understand the business mistakes I'd made — and started building on MQL5 along the way. I focused on health — something I'd ignored for two years of building.

For five months I was an SDE at Tejas AI (YC W25) — WhatsApp-based AI agents with cross-session memory, a scraping fleet indexing roughly 60 ATS platforms, and OpenSearch-backed hybrid retrieval doing the matching.

The lesson was that none of the hard parts were model problems. They were memory, deterministic routing, and knowing when a user's requirement is a filter rather than a similarity score.

I left in August 2026.

I'm looking for the next thing, and I'm deliberately open about the shape it takes: a full-time role in AI infrastructure, search, or quantitative research; contract work on inference cost, agent reliability, or retrieval that misses; or a research collaboration — I'm currently working on measurement studies in systems and finance.

If any of that is a thing you need, the fastest route is email. The productized versions of the contract work are on the hire page.