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TRACK-1_MAVERICKS

🚀 Overview

TRACK-1_MAVERICKS is a TypeScript-based project built with Vite, Supabase, and modern tooling for rapid development.
This repository is part of the VectorFlow-vvce initiative and is designed to provide a scalable foundation for building web applications.


🌾 Benefit_Bridge: AI-Powered Financial Inclusion Engine

Eliminating the information gap between Indian farmers and ₹1.5 Lakh Crore in unclaimed welfare subsidies.


🎯 The Vision

Indian farmers lose thousands of crores annually to information asymmetry and middlemen exploitation. Kisan Saathi is a personalized "Welfare Intelligence Engine" that uses local AI and voice-recognition to match farmers directly to their eligible government subsidies—removing corruption and friction from the application process.


🔥 Key Innovations (Track 1 Highlights)

  • 🗣️ Voice-First Vernacular Interface: Farmers can describe their situation in Kannada, Hindi, or Tamil. Powered by OpenAI Whisper, ensuring accessibility for users with limited digital literacy.
  • 🛠️ Kiro-Driven Spec Architecture: Designed using Spec-Driven Development in Kiro to ensure mathematically accurate matching logic for complex eligibility criteria.
  • 🧠 Edge-AI Intelligence: Runs a local Ollama + Mistral 7B model. This provides expert financial advice completely offline, ensuring privacy and reliability in rural low-connectivity zones.
  • 🛡️ Anti-Bribe Roadmap: Generates a personalized "Document Checklist" and "Transparency Pipeline" so farmers know exactly what they need and where their application stands.

🏗️ System Architecture Workflow

The system utilizes a RAG (Retrieval-Augmented Generation) pipeline to ensure all advice is grounded in current government law.

  1. Ingestion: User inputs land size, crop type, and location via voice or text.
  2. Kiro Engine: Processes input into vector embeddings for precise matching.
  3. Knowledge Retrieval: Queries a verified database of 1500+ government scheme JSON files.
  4. Local LLM Analysis: Mistral 7B predicts eligibility and calculates the "Financial Opportunity" for the user.
  5. Multilingual Output: Results translated and displayed on a clean, high-fidelity Streamlit dashboard.

📂 Project Structure

  • src/ → Main application source code
  • supabase/ → Supabase configuration and migrations
  • .env → Environment variables (not committed)
  • package.json → Dependencies and scripts
  • vite.config.ts → Vite configuration
  • tsconfig.json → TypeScript configuration
  • eslint.config.js → Linting rules
  • wrangler.jsonc → Cloudflare Workers configuration
  • bun.lockb / bunfig.toml → Bun runtime configuration

🛠️ Tech Stack

  • TypeScript (98%)
  • CSS (1.7%)
  • JavaScript (0.3%)
  • Supabase for backend services
  • Vite for fast bundling
  • Bun as runtime and package manager
  • Cloudflare Workers for deployment

⚙️ Setup Instructions

  1. Clone the repository

Clone the repository

git clone https://github.com/VectorFlow-vvce/TRACK-1_MAVERICKS.git cd TRACK-1_MAVERICKS

Install dependencies

pip install -r requirements.txt

Run the platform

streamlit run app.py

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