ASPIRING PRODUCT MANAGER
I analyze products, make trade-off decisions, and ship the result end-to-end.
BTech IT ’27 · Former Product Management Intern @ Orderly · 3 live products on this domain
3 SHIPPED PRODUCTS · RESEARCH & GTM STRATEGY · AI INTEGRATIONS
Selected Work
01INDEPENDENT CASE STUDY
Project Adhikar
Consent & data-rights redesign for Zomato under India’s DPDP Act 2023.
Read the full case study ↗Problem
The DPDP Act 2023 makes Zomato’s existing consent patterns non-compliant — blanket permissions, no purpose limitation, no real withdrawal path. The product problem: redesign consent to be legally sound without wrecking order conversion.
Decisions
- Chose 6 targeted consent touchpoints over one blanket consent screen — purpose-level clarity over one-tap legality theatre.
- Prioritized fixes by legal-risk severity vs. user friction — highest-exposure, lowest-friction changes ship first.
- Made consent withdrawal a first-class flow, not a buried setting — the Act treats withdrawal as equal to consent.
ALTERNATIVES EXPLORED
For minors’ consent, evaluated DigiLocker-verified parental consent against ML-based age estimation — scoped accuracy, privacy exposure and rollout cost before recommending.
Shipped
Interactive prototype demonstrating the redesigned consent flows end-to-end.
Metrics
CONSENT COMPLETION · NOTICE DROP-OFF · GRIEVANCE TAT
02SHIPPED PRODUCT
ShipFlow AI
An AI-powered software delivery platform — planning, boards and automated code review in one pipeline.
Problem
Delivery work is fragmented across planning docs, boards and review tools — and code review is the bottleneck. Built the pipeline end-to-end, solo, to compress it.
Decisions
- Chose a dual-pass AI review engine over single-pass — single-pass missed context-dependent bugs; accepted higher cost for accuracy.
- Chose real GitHub PR diffs over simulated demo data — credibility of findings beats ease of demoing.
Shipped
Product Management planning portal, developer Kanban, and automated AI review running on real pull requests — live in production.
Metrics
REVIEW TURNAROUND · FINDINGS PER PR · FALSE-POSITIVE RATE
03SHIPPED PRODUCT
NEXUS
An AI workspace that reduces actions across email and calendar.
Problem
Knowledge work leaks time to context-switching between inbox, calendar and task tools. NEXUS pulls them into one command surface.
Decisions
- Chose a universal action-reduction tool over a niche, role-specific app — larger problem, simpler pitch, harder scope.
- Cut integrations to protect scope — shipped Gmail and Google Calendar deep instead of five tools shallow.
Shipped
Smart inbox, AI-drafted replies and a command-center dashboard — live in production.
Metrics
ACTIONS SAVED PER SESSION · TIME-TO-REPLY
04PRODUCT MANAGEMENT INTERNSHIP
Orderly — the 7 → 4 redesign
Reducing order-completion friction on an AI voice-ordering product.
Problem
Orderly (tryorderly.sh) lets restaurant guests order by voice via QR — no app download. Early usage showed guests starting orders but not finishing them.
WHAT I DID
Mapped the full onboarding and ordering journey, identified 3 friction points killing completion, and proposed a simplification that cut the flow from 7 steps to 4. Prioritized the changes with founders on user impact vs. engineering feasibility within voice-processing constraints.
Metrics
ORDER COMPLETION · STEPS TO ORDER: 7 → 4 · TIME TO FIRST ORDER
How I Work
01
Research
Map the journey. Find friction with evidence, not opinions.
02
Decide
Frame the trade-off. Choose X over Y with a reason attached.
03
Ship
Build or spec it end-to-end. Live beats slideware.
04
Measure
Define success metrics before launch, not after.
About
I’m a BTech IT student at JSS Noida (’27) focused on product. I analyze user journeys, improve product experiences, and ship AI products end-to-end — working across market research, go-to-market strategy, AI integrations, feature prioritization, and regulatory compliance (DPDP). I care about building user-centric solutions that scale. Based in Noida.
