Freelance Product Analysts in Kharagpur

Freelance Product Analysts in KharagpurFreelance Product Analysts in Kharagpur

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Durgesh Kumar

Kharagpur, India

Full-stack developer and founder building real-world product

New to Contra

Full-stack developer and founder building real-world product

Cover image for During my internship at Neosophical

During my internship at Neosophical Labs, I single-handedly built Sabse Bada Fan (https://sbf.ahoum.com/)(SBF), a live multiplayer quiz game, taking it entirely from an initial idea to a fully launched application. I designed the system to handle over thousands of people playing at the exact same time without slowing down, and I made sure the live leaderboards update instantly for everyone. To make the game accessible everywhere, I integrated Capacitor to seamlessly package the web platform into native Android and iOS mobile apps. Finally, I built an automated background system that perfectly runs our scheduled live quizzes and handles all the scoring completely on its own without any human effort.

This one started from a very ordinary campus problem: every semester, thousands of IIT Kharagpur students either haul stuff home they'll never use again, or buy new when a senior literally has the same thing sitting unused two hostels away. There was no trusted way to buy and sell second-hand on campus, just scattered WhatsApp groups where you can't verify who you're actually dealing with. I built ThriftKGP to fix that, a campus-only marketplace where every listing is tied to a verified institute identity, so you're never buying from a stranger off the internet, you're buying from another IITKGP student. Beyond the basic buy/sell flow, I built out four core modules that make it actually usable at scale: request boards (post what you're looking for instead of only browsing what's listed), wishlists, a review system so reputation carries across transactions, and an admin dashboard for oversight. I also automated listing moderation and buyer-request matching with event-driven background jobs, which cut down manual moderation work substantially, important once you're serving 16,000+ potential users and can't manually vet every post. Stack: React, Node.js, Express, MongoDB, Tailwind CSS It's since been recognized with a SELF RISE Innovation & Prototype Grant as a campus-wide second-hand marketplace concept. If you're building a marketplace, community platform, or anything that needs trust and verification baked into peer-to-peer transactions, that's squarely in my wheelhouse.

Cover image for Built for the Accenture Innovation

Built for the Accenture Innovation Challenge (Round 2), this one tackles a problem most companies rolling out AI don't think about until it bites them: if you're running several different LLM-powered features at once — a customer support bot, an internal knowledge assistant, a tool inside a regulated decision workflow — each one needs a completely different level of scrutiny. A support bot can tolerate more risk and needs to respond fast; a regulated decision-support tool needs to be slow, careful, and willing to hold a response for human review rather than guess. AuRizz is a middleware layer that sits between an enterprise's LLM provider and its users and decides, per use case and in real time, whether a generated response should go through as-is, get redacted, get held for a human, or get blocked outright. What made this a genuinely hard build, not just an API wrapper: Policy-driven, not hardcoded. Every behavioral difference between use cases — which checks run, their thresholds, what happens on failure — lives in YAML config, not in application code. Switching a deployment's risk posture is a config edit, not a redeploy. Confidence as a first-class signal. Every check returns both a risk score and a confidence in that score — a high-risk-but-unsure reading routes to human review instead of auto-blocking, which is the actual fix for alert fatigue. Multi-provider by design. Works with Anthropic, OpenAI, Gemini, or Groq behind one interface, with automatic runtime fallback if a provider fails — switching providers is a one-line config change, not a rewrite. A real audit trail. Every decision is written to a hash-chained, tamper-evident log, and human overrides are appended as new records rather than edits — so the fact that the system said block and a human said allow is preserved, not erased. Built to be tested. 104 tests run the full pipeline — detectors, aggregation, decision rules, the audit chain — with no network calls or API keys required, because everything is built against interfaces, not vendor SDKs directly. I worked on this with a teammate — I focused on architecture, pipeline, backend, and we split the detector logic, policy engine, and audit system between us. Stack: Python, FastAPI, React, TypeScript, Pydantic, SQLite, multi-provider LLM integration (Anthropic/OpenAI/Gemini/Groq) Status: this is a working prototype, not a deployed product — the source is public and the full pipeline runs locally with real API calls or offline in the test suite, but it hasn't been hosted or put in front of real traffic yet. If you're building anything where AI output needs a guardrail — content moderation, compliance-sensitive automation, multi-agent systems that need oversight — this is exactly the kind of system design problem I enjoy.

I built this because I was annoyed. I was paying monthly for a study library seat, and I still couldn't reliably tell if a seat would be free when I showed up. No way to check online, no way to book ahead just calling around or walking in and hoping. I figured if this was frustrating me, it was frustrating thousands of students across India who rent library/reading-room seats the same way. So I built Studoo (https://studoo.aithro.in/)and Padhloo (https://padhloo.aithro.in/)two connected products at Aithro (https://aithro.in/products)- Padhloo (student side): search study spaces by city/price/amenities, see a live seat map, request a specific seat for 1-12 months, and track your bookings, dues, and payments in one place. It also gamifies actually showing up — QR check-in/out, study streaks, points, badges, a leaderboard — so students stay consistent, not just book-and-forget. Studoo (owner side): the dashboard library/reading-room owners use to list their space, manage seat inventory and pricing per slot, approve or reject booking requests, chase due payments automatically, and see real analytics (occupancy, revenue, which slots to reprice). Free tier + two paid plans. You can see product demo video here - https://youtu.be/9Yex73MIYqQ?si=jfWqLxisRTM9N_28 I designed and built the full product end-to-end — the booking/approval workflow, the seat-and-slot inventory model, the QR-based geofenced attendance system, the payments/dues tracking, the subscription tiers, and the whole gamification layer (points, streaks, badges) on the student side. Where it stands: Got our first paying customer, a real library owner in Patna paying for the Growth plan, which is the moment this stopped being a side project and started being a business. Reached the Grand Finale of Empresario 2026 (IIT Kharagpur E-Cell), selected out of 3,000+ global registrations. Won the Platinum Jubilee Innovation Challenge (Deep Tech) at IIT Kharagpur's 75th anniversary event. What I'm good at, based on building this: turning a messy real-world workflow (owner approves seat → student pays offline → both sides need to trust the same numbers) into a system that's simple enough for a small business owner with no tech background to actually use — while still handling the edge cases (partial payments, overlapping slots, expiring bookings, geofenced check-ins) correctly under the hood. If you're a small business or founder who needs a web app or an AI-automation workflow built and shipped fast, not just prototyped that's exactly the kind of problem I like solving. Happy to talk about your project.

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