Why I repositioned Gita Chat from spiritual AI to mental wellness
On finding category-of-one positioning in a saturated market - and the one competitive teardown that changed the brief.
Read →Product Manager - Open to APM / PM / AI PM roles
PM with 3+ years across fintech, marketplace, and AI products. Currently building gitachat.life - 250+ users, 10+ paying, in week one.
I work on consumer AI for the Indian market - products that sit at the intersection of LLM capabilities and local context. Most recently: gitachat.life, a scripture-grounded emotional wellbeing companion I shipped in 24 hours. 250+ users and 10+ paying customers in the first week, with positioning refined over that same week based on competitive analysis.
Before this, I spent 3+ years across Barclays Investment Bank (Global Reconciliation Tech - enterprise fintech tooling across $150M+ daily flows) and Sevame (hyperlocal marketplace, 0-to-1 to 10K+ users as first PM). I also built an AI Compliance Readiness Tool - an LLM + RAG product that evaluates early-stage Indian startups on data protection and AI maturity. MSc Advanced Computer Science, University of Manchester. BTech Computer Engineering, NMIMS.
Consumer AI
LLM products built for Indian context and language.
Fintech Ops
Enterprise reconciliation tooling. $150M+ daily flows. Stakeholder delivery across eng, ops, product.
0-to-1 Marketplace
First PM at Sevame. 10K+ users, 60+ partners, 3 cities.
LLM Products
RAG pipelines, prompt design, output validation, evals.
Competitive Positioning
Found category-of-one positioning for Gita Chat in 7 days.
Safety-Critical Design
Curated retrieval over coverage. Hallucinations are a feature bug.
Fintech compliance work taught me constraints. Marketplace work taught me velocity. AI product work taught me that positioning beats features. Gita Chat is where all three converge.
The projects I'd defend in a room full of PMs.
On finding category-of-one positioning in a saturated market - and the one competitive teardown that changed the brief.
Read →Why 60 hand-picked verses outperformed full corpus retrieval - and what coverage actually costs in trust.
Read →On velocity, killing features fast, and why one product out of thirty was worth the other twenty-nine.
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