Project overview
Imagine a workforce that keeps an entire planet’s economy running: delivering food, moving people, shipping packages, freelancing skills; yet remains financially invisible because their earnings don’t follow Earth’s traditional “monthly paycheck” ritual.
HyperGig+ is the universal translator between this new workforce and the banking systems that can’t understand them.
We built a digital identity layer for gig workers; one that reads their earnings from multiple platforms, analyzes their work patterns, calculates their financial stability, and converts it all into a trustable credit score.
In simple terms: We taught banks how to understand the gig economy.
HyperGig+ turns chaotic, multi-source income into a clean financial fingerprint.
Once gig workers become visible; they become creditworthy.
Once they become creditworthy; they become empowered.
HyperGig+ a bridge between two species who till date never spoke the same language: gig workers and the financial system.
Inspiration
While studying gig workers closely; delivery riders, drivers, freelancers, we kept encountering the same story:
Working 10–12 hours a day, earning decently… but rejected by banks because “income is unstable.”
The truth is, the income wasn’t unstable.
The system that measured it was outdated.
We were inspired to fix that structural injustice.
HyperGig+ was born from the realization that gig workers don’t lack income, they lack recognition.
The financial world needed a new scoring language.
We decided to build it.
What it does
HyperGig+ unifies and analyzes gig workers’ financial lives.
1. Unified Income Dashboard: Consolidates income from Zomato, Swiggy, Uber, Dunzo, freelance platforms, and UPI.
2. Income Stability Index: Measures how consistent and dependable their income is.
3. Smart Risk Score: Converts work patterns, earnings, task behaviour, and delay frequency into a credit score.
4. Real-Time Credit Offers: Shows credit cards, micro-loans, BNPL, insurance, and savings plans based on live scoring.
5. Tax & Insurance Layer: Estimates taxes, flags insurance gaps, and recommends financial products.
HyperGig+ builds a clean financial identity for gig workers, one they can finally use to access fair credit.
How we built it
We built a two-part system:
1. MVP Layer (Frontend-Only):
(a) React + Vite
(b) Simulated multi-platform income
(c) A custom scoring engine.
(d) Componentized UI for dashboard, insights, scoring, offers, and tax
Local computation of all analytics
2. Production Architecture :
1. Microservices blueprint with Node.js + Python
2. Machine learning scoring model (LightGBM + logistic regression)
3. Kafka-based income stream ingestion
4. PostgreSQL financial storage
5. Offers engine + API specification
6. Risk modelling pipeline & scoring formula
7. Infrastructure design for scalable deployment
We built the MVP to demonstrate feasibility,
And a complete engineering blueprint to show readiness for real-world deployment.
Individual contributions
Aditya Kumar:
1. Designed full-system architecture (MVP + production-grade)
2. Built the Income Stability Index + Risk Scoring algorithm models.
Garav Goyal:
1. Built Frontend from scratch
2. Developed the Backend System of HyperGig+
Avish Gumber:
1. Crafted the business model & financial reasoning
2. Dockerization for final deployment
Aryan Pal:
1. Developed dashboards, charts, UI, and scoring flows
2. Engineered the conceptual ML framework for risk modeling
Japneet Kaur:
2. Wrote documentation, diagrams, README, and pitch components.
3. Created a professional narrative & product storyline
Challenges
1. Translating chaotic gig income into measurable stability: The biggest challenge was defining a mathematical structure that converts irregular, platform-diversified income into reliable financial signals.
2. Designing a scoring model that banks could trust
Traditional credit models collapse when income isn’t monthly. We built a multi-feature risk engine tailored for gig realities.
3. Creating a full product without backend support
We simulated platform integrations, scoring pipelines, and data patterns to create a realistic MVP that feels like a functional production prototype.
4. Representing a complex product through simple UI
Turning huge quantities of financial intelligence into clean, digestible screens was a UI/UX challenge we solved through intelligent segmentation and data summarization.
Accomplishments
1. Built a fully functional MVP showcasing the entire product journey
2. Created a financial identity model for gig workers
3. Designed an industry-ready risk scoring architecture
4. Developed the Income Stability Index formula
5. Produced a comprehensive production-ready architecture
6. Crafted a pitch and storyline that resonates with banks, investors, and users
7. Turned a systemic socio-economic problem into a working fintech product
Learnings
1. Gig income is predictable, if you look at the right signals: frequency, delays, tasks, volatility, platform shifts.
2. User-centric design matters: gig workers don’t need complexity; they need clarity.
3. Financial systems fail when frameworks don’t evolve with work culture.
4. Scalable engineering begins with a well-defined data model.
5.. The difference between a product and a solution is narrative clarity.
Next steps
1. Productionizing the scoring engine (Python-based FastAPI microservice)
2. Building real gig-platform integrations (Zomato, Swiggy, Uber APIs where possible)
3. Deploying a backend pipeline (Kafka + PostgreSQL + Redis)
4. Launching HyperGig+ mobile app
5. Partnering with NBFCs or banks to pilot lending experiments
6. Building tax-filing and insurance marketplaces for gig workers
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