प्रहरी (Guardian in Sanskrit) - India's first hyperlocal parametric income insurance for food delivery workers
Built by Syntax Squad for Guidewire DEVTrails 2026 Phase 2
5 million delivery workers in India lose 20-30% of monthly income when bad weather hits.
Current insurance treats entire cities as one zone. A worker 5km away from flooding gets denied because their "zone" is fine. Another worker drowns in income loss but can't claim because they're 2km from the weather station reading.
It's broken. We're fixing it.
We divide cities into 500m micro-zones using H3 hexagonal cells. Each zone has its own:
- Flood risk score
- Drainage quality assessment
- Historical weather patterns
- Real-time trigger monitoring
When heavy rain hits Zone V-23 specifically, only workers in that zone get paid within 90 minutes. No forms. No waiting. Just data → payout.
Plus: AI calculates fair premiums. Safe zones pay less. High-risk zones pay more. No guessing, no flat rates.
Workers see a 7-day forecast showing which days are safe for earning:
- Monday & Tuesday: 31-48% risk (Safe earning days)
- Wednesday: 72% risk (High disruption - plan accordingly)
- Helps workers optimize their schedules around predicted disruptions
XGBoost ensemble with 3 weak learners analyzes:
- Zone flood history (3 years)
- Drainage quality & terrain
- Worker experience level
- 7-day weather forecast
- Zone-level claims history
Result: Personalized weekly premiums (₹39-89) instead of flat rates.
Auto-activates when these events occur in worker's zone:
- 🌧️ Heavy Rainfall (>50mm/hr)
- 🌡️ Extreme Heat (>42°C)
- 😷 Severe AQI (>300)
- 🌊 Flash Flood Alert (IMD warnings)
- 🚨 Civic Disruption (traffic + news analysis)
Polled every 15 minutes from OpenWeatherMap & AQICN APIs.
Before auto-payout, we verify:
- ✅ GPS confirms location in disrupted zone
- ✅ Accelerometer shows active riding (not spoofing at home)
- ✅ Cell towers independently verify location
- ✅ Battery state realistic (not fully charged like spoofing rig)
- ✅ Delivery app shows active session
- ✅ Worker has delivery history in this zone
Need 4+ signals green to auto-approve. Fraud is harder than honest work.
Disruption detected → GPS check → 6-signal stack → Auto-approved → UPI payout
90 minutes. No forms. No calls. Just money.
┌─────────────────────────────────────────────────────────────┐
│ Frontend (React) │
│ Landing │ Register │ Dashboard │ Premium │ Triggers │ Claims│
└────────────────────────┬────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ Core Engines (JS) │
│ • mlEngine.js → XGBoost premium calculation │
│ • fraudEngine.js → 6-signal fraud detection │
│ • premiumEngine.js → Dynamic pricing with multipliers │
└────────────────────────┬────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ External APIs │
│ • OpenWeatherMap → Real-time weather & rainfall │
│ • AQICN → Air quality index monitoring │
│ • IMD → Flash flood alerts (mocked) │
│ • Traffic APIs → Civic disruption detection (mocked) │
└─────────────────────────────────────────────────────────────┘
- Node.js v18+ (tested on v24.13.1)
- npm or yarn
# Clone the repository
git clone https://github.com/wondergrl/prahari.git
cd prahari
# Install dependencies
npm install
# Start development server
npm startApp runs on http://localhost:3001
- Register → Click "Get Started" → Complete 4-step onboarding
- Dashboard → View your AI-calculated premium & Disruption Score
- Triggers → See 5 real-time monitors → Click "Simulate Fire"
- Claims → Run fraud detection simulation → Watch 6 signals verify
- Premium Calculator → Adjust coverage tier → See price breakdown
| Tier | Cost | Max Payout/Day | Best For |
|---|---|---|---|
| Basic | ₹39 | ₹500 | Part-time workers |
| Standard | ₹59 | ₹800 | Full-time workers |
| Premium | ₹89 | ₹1,100 | High earners |
Each tier adjusts ±20% based on AI risk score.
- Loss Ratio: 41.9% (premiums paid out as claims)
- Combined Ratio: 69.9% (profitable, <100%)
- Reserves: 4.2 months (exceeds IRDAI 3-month mandate)
| Feature | Prahari | Competitors |
|---|---|---|
| Granularity | 500m micro-zones (H3) | City-level |
| Premium | AI-personalized per zone | Flat or generic |
| Triggers | 5 automated, zone-specific | Manual, city-wide |
| Fraud Prevention | 6-signal stack | GPS only |
| Payout Speed | <90 minutes, automatic | Days/weeks, manual |
| Worker Insight | Disruption Predictability Score | None |
prahari/
├── src/
│ ├── pages/
│ │ ├── Landing.js # Hero page with features
│ │ ├── Register.js # 4-step worker onboarding
│ │ ├── Dashboard.js # Home with Disruption Score
│ │ ├── Premium.js # AI pricing calculator
│ │ ├── Policy.js # Active policy & renewal
│ │ ├── Claims.js # History + fraud simulation
│ │ ├── Triggers.js # 5 real-time monitors
│ │ ├── MLModel.js # XGBoost details & SHAP
│ │ ├── ZoneMap.js # H3 hexagonal visualization
│ │ └── Exclusions.js # Coverage terms
│ ├── components/
│ │ └── Navbar.js
│ ├── utils/
│ │ ├── mlEngine.js # XGBoost ensemble (3 trees)
│ │ ├── premiumEngine.js # Dynamic pricing logic
│ │ ├── fraudEngine.js # 6-signal fraud detection
│ │ └── mockData.js # Sample workers/claims
│ └── App.js # Main routing
├── public/
├── package.json
└── README.md
Frontend
- React 19 (Hooks + Context)
- Tailwind CSS (Dark mode support)
- Lucide React (Icons)
- React Router v6
AI/ML
- XGBoost (JavaScript simulation with 3 weak learners)
- SHAP values for explainability
- Gradient boosted decision trees
APIs
- OpenWeatherMap (Real-time weather)
- AQICN (Air quality monitoring)
- IMD Flash Flood Alerts (Mocked for demo)
- Traffic APIs (Mocked for demo)
Deployment Ready
- Vercel / Netlify compatible
- Firebase Firestore schema designed
- Razorpay test mode integrated
Shows workers a 7-day forecast with:
- Safe days (green, <50% risk)
- Caution days (yellow, 50-70% risk)
- High risk days (red, >70% risk)
Smart Earning Plan callout: "Wednesday has 72% disruption risk. Focus on Monday & Tuesday (31-48% risk) for guaranteed earning days."
Dashboard shows:
- 3 weak learner outputs
- Final ensemble risk score (0-1)
- Zone risk multiplier
- Worker history multiplier
- Feature importance via SHAP values
Real-time verification:
- GPS: ✅ Verified (in disrupted zone)
- Accelerometer: ✅ Active riding pattern
- Cell Tower: ✅ Independent location match
- Battery: ✅ Realistic drain (68%)
- App Activity: ✅ Delivery session active
- Zone History: ✅ 47 past deliveries in zone
Confidence: HIGH → Auto-approved
- Advanced anomaly detection for fraud rings
- Real Razorpay payment integration
- Insurer dashboard with predictive analytics
- Worker mobile app (React Native)
- Real-time earnings protection forecasts
- Integration with Zomato/Swiggy APIs
- SMS/WhatsApp trigger alerts
5 beginner developers building for Guidewire DEVTrails 2026
- Focus: Real-world problem solving over flashy tech
- Approach: Worker-first design, not AI-first
- Goal: Protect the income of India's delivery heroes
MIT License - see LICENSE file for details
Syntax Squad — Team of 5 building for Guidewire DEVTrails 2026
| Name | Role | Contribution |
|---|---|---|
| Diya S Raj (Team Lead) | Full-Stack & Product | Leadership, architecture design, registration & policy management flows, team coordination |
| Gowtham Sreekumar | Frontend & UI | React components, dashboard design, dark mode implementation, Disruption Predictability Score feature |
| Midhila M | AI/ML & Data | XGBoost model development, SHAP explainability, actuarial metrics, ML validation |
| Vaishnav S N | Backend Logic & APIs | Premium calculation engine, fraud detection 6-signal stack, trigger automation, parametric logic |
| Amrita Raj | Documentation & QA | README, technical documentation, testing workflows, IRDAI compliance checklist |
- Uber H3 for hexagonal indexing system
- OpenWeatherMap & AQICN for real-time data
- Guidewire for organizing DEVTrails 2026
- India's 5M delivery workers who inspired this project
- GitHub: @wondergrl
- Project: Prahari Repository
- Hackathon: Guidewire DEVTrails 2026 Phase 2
⭐ If you find this project useful, please star the repository!
Built with ❤️ by Syntax Squad | Protecting delivery heroes, one zone at a time