Devtrails Hackathon (phase-1)

manoj D — Wed, 01 Apr 2026 05:48:49 +0000

At the end of Phase 1, Team Carbon secured 2nd place 🥈 in the Diamond Tier at the Guidewire DevTrails Hackathon! 🚀

This phase was intense and rewarding where we:
💻 Designed and built under tight timelines
⚙️ Solved real-world problems with practical approaches
🔁 Iterated fast, failed fast, and improved faster

👥 Huge shoutout to everyone in Team Carbon for the collaboration, ideas, and execution 🙌

⚡ Key takeaway:
Strong fundamentals + teamwork > everything else in high-pressure environments

Grateful for the opportunity and the learnings so far — on
to the next phase 🔥

Guidewire #DevTrails #Hackathon #TeamCarbon #Developers #TeamWork #BuildInPublic #Tech #Innovation

Devtrails Guidewire Hacakthon Blog-1

manoj D — Wed, 18 Mar 2026 10:22:23 +0000

#devtrails #guidewire

Carbon – Phase 1: Building Income Protection for Gig Workers

The Problem We Set Out to Solve

Gig delivery partners are the backbone of modern on-demand platforms such as food, grocery, and parcel services.

However, their income model is highly unstable:

No fixed salary
No guaranteed daily earnings
No protection during disruptions

A single external disruption — such as heavy rainfall, platform outages, or demand drops — can result in zero income for the day, while essential expenses continue.

This creates a critical financial vulnerability in the gig economy.

Why This Problem Matters

The gig economy is growing rapidly, but financial protection systems have not evolved alongside it.

Traditional insurance models do not fit gig workers because:

Income is variable
Work patterns are dynamic
Risk is short-term and frequent

This gap creates an opportunity for a new type of system designed specifically for gig workers.

Our Idea – Carbon

GigShield is a micro-contribution based income protection system.

The core model:

Drivers contribute a small percentage of their earnings
Contributions form a shared protection pool
Verified disruptions trigger automated compensation

This approach enables a lightweight, scalable alternative to traditional insurance.

Phase 1 Objective

The goal of Phase 1 was to validate feasibility by building a working prototype that can:

Detect disruptions using real-world data
Simulate contribution and payout mechanisms
Demonstrate automated compensation logic

System Design Overview

GigShield Phase 1 is built around three core components:

1. Data Collection Layer

Integrated external weather APIs
Captured environmental signals such as rainfall and temperature
Used these inputs as indicators for disruption detection

2. Disruption Detection Engine

Implemented a rule-based decision system
Example logic:
- If rainfall exceeds threshold
- AND delivery activity drops
- THEN trigger disruption event

This serves as a foundation for future machine learning models.

3. Contribution and Compensation Engine

Designed a percentage-based contribution model (1–3%)
Simulated accumulation of a shared financial pool
Triggered compensation during disruption scenarios

Technical Implementation

Architecture

The system follows a modular architecture:

Mobile Application Layer (Flutter)
Backend API Layer (FastAPI)
Data Layer (Firebase / Database)
External Data Integration (Weather APIs)

Backend Design

REST APIs for driver data, contributions, and payouts
Event-driven logic for disruption handling
Validation layer for eligibility checks

Data Flow

Weather data is fetched at scheduled intervals
Data is processed by the disruption engine
Events are generated when thresholds are met
Compensation logic is triggered automatically

Automation Strategy

The system minimizes manual intervention through:

Scheduled data fetching (weather APIs)
Rule-based event triggering
Automatic eligibility validation
Direct payout simulation

Key Achievements

Developed a functional prototype
Integrated real-time external data sources
Built an automated disruption detection system
Simulated income protection payouts
Established a scalable architecture

Challenges and Learnings

Limited availability of real gig worker datasets
Balancing fairness and sustainability in contribution models
Designing fraud-resistant automation mechanisms

These challenges influenced the design of validation and eligibility layers.

Future Roadmap (Phase 2)

Phase 2 will focus on intelligence and scalability:

Predictive risk modeling using machine learning
Fraud detection based on behavioral patterns
Region-based disruption analysis
Integration with real gig platforms
Dynamic contribution adjustment based on risk

Vision

GigShield aims to create a disruption-aware financial protection system tailored for gig workers.

By combining:

micro-contributions
real-time data analysis
automated compensation

the system provides a sustainable safety net for workers in the gig economy.

Conclusion

Carbon represents an early step toward redefining income protection for non-traditional workers.

Phase 1 validates the concept and demonstrates that automated, data-driven protection systems are both feasible and scalable.

Feedback and Collaboration

We welcome feedback, suggestions, and collaboration opportunities to further develop GigShield into a real-world solution.

Forem: manoj D