About
I'm an engineer-turned-PM. I started in computer science, and I still work close to the systems — reading API docs, slicing funnel data in SQL, and arguing architecture trade-offs with engineering as a partner, not a client.
My work lives where fintech is hardest: regulated lending, where every product decision sits at the intersection of growth, risk, compliance, and partner constraints. I resolve those tensions the same way every time — get everyone onto the same data, make the trade-offs explicit, and ship.
I write about product, lending, and AI at Talkbeats — mostly things I've actually shipped, not theory.
Work
Insta EMI Card & Digital Gold Loan
I own the co-branded Insta EMI Card end-to-end — acquisition, KYC, activation, and retention — with Bajaj Finance as NBFC partner, inside Airtel's 250M+ app ecosystem. I designed the ETC/NTC acquisition journeys and own the KYC and wallet integration directly, keeping onboarding compliant and fraud-resistant without bleeding activation.
I also launched a fully digital gold-loan product 0-to-1 with the same NBFC partner, and lead a cross-functional team of 10+ across tech, QA, design, and analytics, including 2 Product Managers as direct reports.
XLR8 — Loan Origination Platform
Built and owned XLR8 from its early days to scale: an API-led, multi-tenant origination platform powering ₹250Cr+ in monthly disbursals across 5,000+ channel partners. I built the API suite and configurable onboarding layer that cut partner integration from weeks to days, and owned the payment-rails and reconciliation layer across multiple gateways — improving payout success and cutting reconciliation turnaround by ~30%. Directly led 2 Product Managers.
Consumer fintech, payments & platforms
Earlier roles across consumer payments at national scale (Paytm), digital gold (Safegold), D2C growth (Mamaearth), and ad-tech platforms (Affle) — the foundation for how I think about funnels, platforms, and scale.
Case Study
Risk-based onboarding: which checks, for which user
Insta EMI Card · Airtel Financial Services
Onboarding drop-off was concentrated in the verification steps, and the obvious read was a UX problem. Cutting the data by segment told a different story: new-to-credit and thin-file users were failing for different reasons, at different points — a one-size-fits-all KYC flow mismatched against customers with very different risk profiles.
Risk and our NBFC partner wanted more checks; growth wanted fewer. Both were right. So I instrumented the funnel to quantify what each incremental check cost in activation versus caught in fraud, and used that evidence to align everyone around a risk-based step-up flow — lighter verification where risk was low, stricter where it spiked.
How I Work With AI
I treat AI as a force-multiplier on judgement, not a replacement for it. It compresses the production work — research, analysis, prototyping, documentation — so my time goes to the decisions.
Research, analysis & PRDs
Market and user research, competitive analysis, funnel and cohort work, PRD drafting, and pressure-testing trade-offs before I commit to a direction.
Prototype before spec
Working prototypes and MVPs instead of documents — discovery conversations happen around something clickable, which removes the mental block on prioritisation.
Data, hands-on
I slice the data myself — drop-off by cohort, failure codes by gateway, experiment readouts — rather than waiting on a data team.
AI doesn't make the call
What to build, what to kill, which trade-off to take when both sides are right — that's judgement, and it's the part of the job that's grown, not shrunk.
Toolkit
Domain
- Lending & credit platforms
- Cards (EMI / co-branded)
- Payments & reconciliation
- KYC & onboarding journeys
- NACH · UPI · IMPS · RTGS rails
Craft
- Platform & API product strategy
- 0→1 launches & scale
- Experimentation & A/B testing
- Stakeholder alignment via data
- Agile delivery · Jira · Confluence
Stack
- Claude (daily)
- Emergent · Lovable
- SQL · Tableau · Superset
- Notion AI · Confluence AI
- Figma · Miro