Work
Products built, systems shipped, problems solved
Independent SaaS products and AI systems built from scratch, alongside enterprise-scale analytics platforms delivered at Amazon — a full picture of how I think about data, product, and systems.
Restaurant Intelligence Platform · B2B SaaS
Disha Analytics
Compresses 2 analyst-days of restaurant research into a 5-minute AI-powered intelligence report — deployed in production, with pilot reports run on live restaurants.
Problem
Independent restaurant owners across Europe make operational decisions without structured data. They know something is wrong — reviews mixed, margins thin — but cannot afford consultants and have no tool that synthesises insight into action.
What it does
An analyst enters a restaurant name and city. Disha scrapes 8 data sources, runs 6 specialist AI agents, and produces a ranked action plan, competitor benchmark, hypothesis checklist, and financial signal section — formatted as an internal report or owner-facing walkthrough.
Architecture
- Django 5 REST API + React 18 + Vite frontend
- Celery + Redis async pipeline for report generation
- 6 specialist AI agents — competitor, financial, review, menu, validation, synthesis
- Three delivery modes: Public Scan, Validated Report, Owner Walkthrough
- Three-tier ingestion layer: format adapters → validators → canonical models
Why it matters
This is a direct translation of the consulting instinct I built at Amazon — taking a complex data problem and compressing it into a decision-ready output. Disha does for restaurant analytics what I did for QBRs and Big Bet BRDs: turns raw data into a clear action plan under time pressure.
Community Trust Infrastructure · Deployed
Radius
Trust infrastructure for the informal local economy in residential societies — workers build verifiable trust scores, residents post jobs and get AI pricing, money flows directly peer-to-peer.
Problem
The informal labour market inside residential societies (plumbers, electricians, cleaners) operates entirely on word-of-mouth with no trust signals, no transparent pricing, and no accountability. Residents cannot find reliable workers; good workers cannot build a reputation that travels.
What it does
A resident posts a job via the web portal. An NLP parser extracts job details — skill, duration, headcount — and an AI pricing engine suggests a rate range. Matched workers receive alerts on Telegram, accept with one tap, complete the work, and build a verifiable trust score. All payments are peer-to-peer via UPI — Radius never touches money.
Architecture
- Django 5.1 backend with PostgreSQL on Railway
- Telegram bot for worker onboarding, job alerts, and confirmations
- Unified web portal — resident, worker, and admin views
- NLP job parser + AI pricing engine (Claude API) on every job
- Trust score built from completions and resident reviews
- Platform never handles money — all payments direct via UPI
Status
Platform built and deployed to Railway. Pilot setup complete at Blueberry Homes, Bangalore — worker onboarding and ops documentation ready. Full SOPs, dispute resolution, and field ops authored in collaboration with on-ground partner Alok.
Privacy-First · Local LLM · Live at soulspark.me
Soul Spark
A multi-agent AI life intelligence system that integrates finance, health, career, and growth signals into a unified conversational advisor — all inference local, no personal data leaves the device.
Problem
Most personal data systems are fragmented — health, money, tasks, and behavior live in separate apps, making it difficult to turn data into clear decisions. AI-assisted tools that handle personal data require trusting a cloud provider.
System Design
- Local Ollama LLM inference — all data stays on-device
- Packet-native multi-agent architecture with internal graph runtime
- Shared memory and pending-action loop across agents
- Finance, Health, Career, Social, Growth, Library domains unified
- Cloudflare tunnel for public access via soulspark.me
Why it matters
It reflects my interest in moving beyond analytics dashboards toward systems that actually help users make decisions. The shift from "here is your data" to "here is what you should do" is where AI creates real leverage — and where privacy-first design matters most.
Three independently deployed AI tools built on Python + Streamlit — each solving a real user problem with LLM reasoning layered on structured data. All live on Streamlit Cloud.
AirGap
Full-stack AI drone intelligence platform for the DJI Mini 5 Pro
Problem
Post-flight drone data is rich but inaccessible — footage, SRT telemetry, GPS tracks, and flight metrics sit in disconnected files with no tool to turn them into actionable insight.
What it does
A FastAPI backend + Expo React Native app that ingests DJI footage and SRT telemetry, runs YOLOv8 object detection on keyframes, scores flight quality (A–D grade), and surfaces pre-flight checklists with real-time weather flyability checks. WebSocket-driven live progress, JWT auth, SHA-256 dedup on ingest.
Why it matters
Built to validate full-stack AI product architecture — data pipeline to mobile UX — with local-first inference on real hardware. No footage or telemetry leaves the device.
Aerial photography portfolio and editorial platform — vyomanaerials.com
Problem
Drone photography deserves a home that goes beyond social media — one that handles editorial storytelling, gallery curation, and video showcasing without technical lock-in.
What it does
A cinematic aerial photography portfolio site for Luxembourg landscapes, powered by Sanity headless CMS. Features a full-viewport hero slideshow, photo gallery, "Stories from Above" editorial section with slug-based essays, and a films section — all CMS-driven with embedded Sanity Studio.
Why it matters
Built to own the full creative pipeline from flight to publish. The headless CMS approach separates content from code, allowing editorial depth and independent content updates without redeployment.
Vivre Ensemble
Luxembourg citizenship exam study platform and mock test engine
Problem
The Luxembourg "Vivre Ensemble" civic knowledge exam covers dense constitutional, historical, and institutional content — with no structured study tool or practice exam available.
What it does
A zero-dependency, offline-capable single-page web app with three modes: a structured study guide (40+ exam topics), a timed 40-question mock exam drawn from 80+ questions with instant explanations, and a civic history narrative for long-term Luxembourg residents.
Why it matters
Built for personal exam prep. The fastest path to a high-quality study tool was building it from scratch — a useful exercise in pure web performance without framework overhead.
Analytics systems, data products, and infrastructure delivered during 9 years at Amazon — spanning payments, private brands, and global marketplace expansion across India, Luxembourg, and multiple regions.
Selection Expansion Analytics Platform
Amazon · 2024 – 2026Amazon · Luxembourg · L6 · 17+ global stores
Problem
17+ global Amazon stores had fragmented, untrustworthy selection data. There was no reliable single view of what was expanding, what the funnel looked like, or how to frame it for leadership planning cycles.
What I built
- Redesigned the entire metric taxonomy — Platform GMS, offer breakdown, Contribution to 3P sales, selection quality — and rebuilt the QBR framework adopted across 4 senior leadership planning cycles.
- Rebuilt core selection identification logic, fixing long-standing data inaccuracies and restoring trust in 20+ downstream reports used daily by Business Managers and engineering teams.
- Delivered entitlement calculations for a $2.5B Seller growth Big Bet under strict timelines, forming the analytical foundation for 2026 annual operational planning.
Private Brands BI Platform
Amazon · 2021 – 2023Amazon · Luxembourg · L5→L6 · EU/NA/APAC
Problem
EU Private Brands had no analytics function. Product, Marketing, Supply Chain, and Finance were all making decisions without reliable shared data, tooling, or a common metrics language across regions.
What I built
- First BIE hire for EU Private Brands — built and led the BI function (3 BIEs + interns), owning the full analytics stack across four business domains.
- Delivered the APB-first Deals Performance Dashboard: 6 views, 20+ metrics, 90+ users across 10 countries and 12 job families. Global single source of truth, saving 350+ hours/year.
- Built Catalog Defects and products-with-a-sale bridge analysis uncovering ~€110M GMS opportunity — adopted by senior leadership for quarterly planning.
- Designed and led Inventory Tracker Engine with global supply chain teams, saving 1,000+ hours/year; extended to NA and APAC in V2.
- SQL cube innovation reduced the weekly business review codebase from 20K to 1.3K lines — adopted org-wide across APB BI.
Amazon Pay & HFC Analytics System
Amazon · 2017 – 2021Amazon · Bengaluru, India · L4→L5 · Payments & Commerce
Problem
Amazon Pay and High-Frequency Commerce (Recharges, Bill Payments, Flights) needed analytics infrastructure built from scratch — for product launches, marketing campaigns, fraud prevention, and Go/No-Go experiment decisions, all simultaneously.
What I built
- Built end-to-end data pipelines, 5 dashboards with 15+ views, and a 25+ query self-serve library supporting the launch of 8 HFC categories — foundational analytics for a $2B business driving 50+ launch campaigns.
- Designed the Downstream Impact methodology for product launch decisions — Go/No-Go framework for 5 Pay experiments at $30M GMV stakes, adopted as the PM standard for feature launches.
- Built a Python-based self-serve customer segmentation tool processing ~100 marketing campaigns/month, reducing campaign setup time by ~70% and saving ~3,000 hours/year.
- Designed abuse-prevention rules combining multiple data sources to auto-cancel ~100K abusive HFC orders/month (~2% of volume), significantly reducing incentive fraud with minimal false positives.