Why We Built This
Gig workers operate in one of the most unpredictable environments. A delivery rider facing a 48°C heatwave or sudden flooding doesn’t just have a “bad day”—they lose their entire day’s income.
Existing insurance systems don’t address this problem well:
Claims take days or weeks
Policies are expensive and rigid
Micro-duration risks (like a single day of extreme weather) are ignored
We wanted to design something fundamentally different:
a real-time, automated, low-cost insurance system that reacts instantly to environmental risk.
That’s how Kavach was born.
*What is Kavach?
*
Kavach is a parametric insurance platform designed specifically for gig workers.
Instead of manual claims, payouts are triggered automatically when predefined conditions are met.
Key Design Goals
- Low cost: Affordable daily subscription model
- Instant payouts: No claim filing or manual approval
- Fraud-resistant: Hardware-backed verification
- Scalable: Built on a modular MERN + AI architecture
System Overview
At a high level, Kavach works through three tightly coupled layers:
Weather Data → Risk Model → Fraud Detection → Payout Engine
+-------------------+ +---------------------+
| Weather API | -----> | Climate Oracle AI |
| (Temperature, etc)| | (Random Forest) |
+-------------------+ +----------+----------+
|
v
+--------+--------+
| GDI Calculator |
+--------+--------+
|
GDI > 0.85 → | Trigger
v
+-------------------+ +---------------------+
| Mobile Sensors | -----> | Sentry-AI |
| (Motion, Temp) | | (Fraud Detection) |
+-------------------+ +----------+----------+
|
v
+--------+--------+
| Risk Controller |
| (Node.js Backend)|
+--------+--------+
|
v
+--------+--------+
| Payout Engine |
| (UPI / Wallet) |
+------------------+
We call this the “Sword & Shield” architecture:
- Sword: Detects real-world risk
-
Shield: Verifies the authenticity of the claim
⚔️ Sword (Risk Detection)
Weather Data → AI Model → GDI Score
|
v
Is Risk High?↓ YES 🛡️ Shield (Fraud Detection)
Sensor Data → Motion Check
→ Thermal Check
→ EV Filter
|
v
Is User Legit?↓ YES 💸 Payout Triggered Instantly
Layer 1: Climate Oracle (Risk Detection Engine)
We built a Random Forest model that processes real-time weather data from external APIs.
- Inputs
- Temperature
- Humidity
- Wind speed
- Output
A computed score called the Gig Disruption Index (GDI).
GDI > 0.85 → Red Alert
Automatically flags a high-risk event
Why Random Forest?
Handles nonlinear relationships well
Robust against noisy environmental data
Works efficiently with tabular inputs
This layer answers:
👉 “Is the environment actually dangerous enough to disrupt work?”
Layer 2: Sentry-AI (Fraud Detection via Sensor Fusion)
Parametric systems are vulnerable to exploitation if not validated.
We addressed this with a sensor-driven verification layer.
Core Idea
Don’t just trust external data—verify the user’s physical context.
Signals Used
- Accelerometer (Kinetic Jitter)
- Detects motion patterns consistent with riding
- Filters out idle or stationary devices
- Battery Temperature (Thermal Correlation)
- Compared with external temperature
- Detects “indoor spoofing” (e.g., AC room fraud)
- Edge Case Handling
- EV-specific logic to avoid false positives from charging heat
Outcome
Only users who are:Actually active
Physically exposed to conditions
…are eligible for payouts.
Layer 3: Risk Controller (Liquidity & Payout Engine)
The backend ensures the system remains financially stable while delivering instant payouts.
- Responsibilities
- Monitor liquidity pool in real time
- Prioritize high-risk users during peak events
- Prevent over-disbursement
- Implementation
- Built into a Node.js + Express service layer
- Uses MongoDB for:
- User risk profiles
- Subscription tracking
- Transaction logs Tech Stack Breakdown
| Layer | Technology |
|---|---|
| Frontend | React.js (Web Sensor APIs) |
| Backend | Node.js + Express |
| Database | MongoDB |
| AI Engine | Python (Scikit-learn, .joblib) |
Why This Stack?
MERN enables rapid prototyping and scalability
Python integrates seamlessly for ML inference
Web APIs allow direct hardware signal capture
Phase 1: What We Achieved
Built a working end-to-end MERN prototype
Implemented real-time sensor data ingestion
Integrated dual AI layers (risk + fraud detection)
Validated system behavior against spoofing scenarios
Key Engineering Challenges
1. Bridging Web Apps with Hardware Signals
Accessing reliable sensor data in a browser environment required careful handling of:
- Permissions
- Data sampling rates
- Noise filtering
2. Synchronizing AI Pipelines
We needed a clean handshake between:
- Weather-based risk scoring
- Sensor-based validation Ensuring both layers agreed before triggering payouts was critical.
3. Designing for Real-Time Decisions
The system had to:
- Process inputs quickly
- Avoid false positives
- Trigger payouts without delay
What’s Next (Phase 2)
We’re moving toward a mobile-first architecture.
Planned Improvements
- Native mobile app for better sensor fidelity
- Background telemetry collection
- One-tap UPI payouts (<60 seconds target)
This will significantly improve reliability and user experience.
Final Thoughts
Kavach is an attempt to rethink insurance from the ground up.
By combining:
- Real-time environmental data
- On-device sensor validation
- Automated payouts
…we’re building a system that aligns with how gig workers actually live and work.
The goal isn’t just innovation—it’s impact.
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