Zero income loss. Zero downtime. Zero paperwork.
India's Q-Commerce delivery partners (Zepto, Blinkit, Swiggy Instamart) operate on brutal 10-minute SLAs. A single external disruption — heavy rain, a local curfew, elevated AQI — can wipe out an entire shift's earnings in minutes. These workers have no safety net. When disruptions hit, they bear 100% of the financial loss alone.
ZeroDown fixes that.
Segment: Grocery / Q-Commerce delivery partners (Zepto, Blinkit, Swiggy Instamart)
Why Q-Commerce specifically? Because a 10-minute delivery promise means any external disruption causes immediate, measurable income loss. The parametric trigger is tighter, the payout case is stronger, and the persona is underserved compared to food delivery workers.
Scenario 1 — Ravi, Zepto partner, Bengaluru It's a Tuesday evening. Ravi completes 4 deliveries/hour earning ≈ ₹80/hour. At 6 PM, heavy rain (>65mm/hr) begins. Orders drop 80%. Ravi earns ₹0 for the next 3 hours. ZeroDown detects the weather trigger, auto-initiates a claim, and credits ₹200 to his UPI in under 2 minutes. No forms. No calls.
Scenario 2 — Priya, Blinkit partner, Delhi AQI in her zone crosses 400 (Severe). The platform deprioritises outdoor deliveries. Her active hours drop from 8 to 3. ZeroDown's parametric trigger fires, covering 60% of her projected income loss for the day automatically.
Scenario 3 — Arun, Swiggy Instamart partner, Mumbai A sudden local curfew is announced in his delivery zone. He cannot access pickup/drop locations for 5 hours. ZeroDown detects the zone-level social disruption event, cross-validates with his GPS inactivity, and processes the claim without him lifting a finger.
Worker Onboards (Web + Mobile)
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Risk Profile Generated (AI/ML)
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Weekly Policy Purchased (₹25–₹60/week)
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Real-Time Disruption Monitoring (APIs)
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Parametric Trigger Fires
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Auto Claim Initiated (Zero Touch)
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Fraud Detection Check (AI)
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Instant UPI Payout
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Worker Notified (Push / SMS)
ZeroDown prices insurance on a weekly basis to align with the gig worker earnings cycle. Workers earn and spend week-to-week — so we insure week-to-week.
| Risk Tier | Weekly Premium | Max Weekly Coverage | Who Qualifies |
|---|---|---|---|
| Low Risk | ₹25 | ₹1,000 | Zones with low historical disruption, high Trust Score |
| Medium Risk | ₹38 | ₹1,500 | Zones with moderate weather/traffic disruption history |
| High Risk | ₹55 | ₹2,000 | Flood-prone zones, high AQI history, strike-prone areas |
The weekly premium is not fixed — it adjusts each week based on:
- Zone Risk Score — historical disruption frequency in the worker's primary delivery zone
- Worker Trust Score — based on GPS consistency, claim history, delivery activity
- Predictive Weather Forecast — upcoming week's weather risk (rainfall probability, AQI forecast)
- Seasonal Modifiers — monsoon season, Diwali traffic, harvest festivals
Example: A Zepto partner in Koramangala (low flood history, high trust score) pays ₹28/week. The same worker during monsoon season (June–September) pays ₹42/week because the predictive model flags elevated rainfall risk.
These are the 5 automated triggers that fire a claim — no manual filing required.
| # | Trigger | Condition | Data Source |
|---|---|---|---|
| 1 | Heavy Rainfall | Rainfall > 65mm/hr in delivery zone | OpenWeather API |
| 2 | Severe AQI | AQI > 300 (Very Poor / Severe) in zone | CPCB AQI API |
| 3 | Extreme Heat | Temperature > 44°C AND humidity > 60% | OpenWeather API |
| 4 | Zone Lockdown / Curfew | Government-issued curfew or Section 144 in zone | News API + manual override |
| 5 | Delivery Demand Collapse | Platform order volume drops > 70% in zone for > 45 min | Simulated platform API |
A trigger alone doesn't release payout. The system cross-validates:
- External signal confirmed (API data)
- Worker GPS shows they were in the affected zone during the event
- Worker activity dropped (delivery count fell during the window)
- No duplicate claim for the same event window
- Fraud score below threshold
All five checks pass → auto-approve → instant payout.
- Input features: Zone historical disruption rate, worker trust score, upcoming weather forecast, seasonal index, platform demand patterns
- Output: Personalised weekly premium (₹)
- Why Random Forest: Handles mixed feature types well, interpretable for regulators
- Input: Worker's average hourly earnings × disruption duration × demand drop %
- Output: Estimated income loss in ₹ (payout amount)
- Calibrated per zone — a worker in high-density Koramangala has a different baseline than Whitefield
- Signals monitored:
- GPS location inconsistency (claimed to be in zone but wasn't)
- Worker was already inactive before disruption (can't claim disruption caused inactivity)
- Duplicate claim attempt for same event
- Coordinated fraud (multiple workers filing for a non-existent event)
- Output: Fraud probability score (0–1). Score > 0.75 = flagged for review
- Computed weekly. Factors: claim history accuracy, GPS data quality, delivery activity consistency
- Higher trust → lower premium, faster payouts
- Forecasts zone disruption risk for the upcoming week
- Powers the pre-week premium adjustment and sends risk alerts to workers
Why both?
| Platform | Audience | Key Use Case |
|---|---|---|
| Web (Next.js) | Insurer admin, partner managers | Dashboard, analytics, fraud review, policy management |
| Mobile (React Native / Flutter) | Delivery workers | Policy purchase, claim status, payout notifications |
Workers interact on mobile — fast, thumb-friendly UI with minimal steps. Admins and insurers use the web dashboard for analytics and oversight. Both share the same FastAPI backend.
- Web: Next.js 14, Tailwind CSS, ShadCN UI
- Mobile: React Native (Expo) — targeting Android first (80%+ of delivery partners use Android)
- Primary API: FastAPI (Python) — handles claims, triggers, fraud detection, AI models
- Supporting services: Node.js — real-time WebSocket notifications, event streaming
- PostgreSQL — workers, policies, claims, payouts
- Redis — session management, real-time event cache, trigger state
- PostGIS extension — geo-spatial zone risk mapping
- Scikit-learn — Random Forest (pricing), Isolation Forest (fraud)
- Facebook Prophet — earnings forecasting, seasonal adjustments
- Pandas / NumPy — feature engineering
- OpenWeather API (weather triggers)
- CPCB AQI API (pollution triggers)
- News API (social disruption signals)
- Razorpay sandbox (mock UPI payouts)
- Firebase Cloud Messaging (push notifications)
- Docker + Docker Compose for local dev
- GitHub Actions for CI/CD
- Render / Railway for deployment (free tier)
- Persona definition and scenario research
- Weekly premium model design
- Parametric trigger specification
- Tech stack decision
- Basic project scaffolding (repo structure, CI setup)
- Static UI wireframes (worker onboarding + dashboard)
- Worker registration and onboarding flow
- Insurance policy creation and management
- Dynamic premium calculation (ML model v1)
- 3–5 parametric trigger integrations (mock APIs)
- Basic claims management UI
- Zero-touch claim initiation pipeline
- Advanced fraud detection (GPS spoofing, duplicate claim detection)
- Simulated instant payout via Razorpay sandbox
- Intelligent dashboard — worker view + insurer admin view
- Predictive analytics (next-week disruption risk)
- Final demo video + pitch deck
zerodown/
├── frontend/
│ ├── web/ # Next.js admin dashboard
│ └── mobile/ # React Native worker app
├── backend/
│ ├── api/ # FastAPI — core services
│ │ ├── claims/
│ │ ├── policies/
│ │ ├── triggers/
│ │ └── fraud/
│ ├── ml/ # ML models and feature pipelines
│ └── notifications/ # Node.js WebSocket + FCM service
├── data/
│ └── mock/ # Mock API responses for triggers
├── docker-compose.yml
└── README.md
- Tighter persona: Q-Commerce's 10-minute SLA makes income loss from disruption immediate and measurable — cleaner payout logic than food or e-commerce
- Zero-touch experience: Workers don't file claims. The system detects, validates, and pays. That's the product.
- AI at every layer: Pricing, fraud, and forecasting are all ML-driven, not rule-based hacks
- Weekly model is real: Aligns with how gig workers actually earn and plan — not monthly premiums they can't afford to front
Built for Guidewire DEVTrails 2026 — Unicorn Chase Team ZeroDown