QwikAid – AI-Driven Roadside Assistance & Emergency Support Platform

Picture a comprehensive roadside assistance system that jumps into action when you break down on a remote national highway as dusk falls and your phone signal fades. It consists of an intelligent, agentic AI that evaluates your situation and arranges for assistance, along with live tracking to determine your location, an SOS button for immediate rescue, and offline capabilities to keep you covered even in signal-dead zones, ensuring that every commuter feels safe and supported no matter how far they roam.

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Project Overview

QwikAid is a modern, AI-enhanced platform designed to provide instant roadside help in emergency situations. In the event of a medical emergency or a car breakdown, QwikAid links users with local service providers, guarantees real-time updates, and offers offline and multilingual access for broad utility.

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Video Link: Link

Key Features

Feature	Description
Agentic AI Support	AI-powered assistant for navigating emergencies and coordinating help.
Live Map Tracking	Real-time tracking of both users and responders on interactive maps.
SOS Mode	Three-shake emergency broadcasting with live location and condition broadcasting.
Offline Support	Works seamlessly in areas with poor or no network coverage.
Nearby Assistance	Auto-connects to verified nearby service centers/mechanics.
Multilingual Support	Available in multiple Indian languages for maximum accessibility.
Payment Gateway	Integrated payment support for seamless transactions.
User Reviews	Rate and review services for improved reliability and quality assurance.

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Tech Stack

Layer	Technology Used
Frontend	React Native, Tailwind CSS, React Query
AI	MCP (Model Context Protocol), Groq (AI Inference Engine), Langgraph (Agentic AI Builder)
Backend	NestJS, Prisma, Apache Kafka, Socket.io
Database	MongoDB, SQlite
Security	JWT, Secure Auth, Real-time Monitoring

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App Screenshots

User App

Service App

User Flow

Architecture Overview

Agentic AI Flow

• User sends a query to the Backend via API.
• Backend forwards it to the MCP Client, which prompts the LLM.
• LLM processes the prompt, interacts with the MCP Server, and uses tools (e.g., GPS Tracking, SOS Emergency).
• LLM returns a JSON response to the MCP Client.
• Backend delivers the response to the User.

Real-time assistance matching

• The client app captures the user's location and sends it to the backend via HTTP POST.
• The backend queries a geo-indexed MongoDB to find the nearest garage.
• Garage details are returned in JSON and displayed on a map using React Native Maps.
• Assistance vehicles send live location via WebSocket for real-time tracking.
• The backend relays vehicle location updates to the client app.

Offline Support Architecture

• App uses geo-hashing to encode driver's GPS location for efficient proximity searches.
• Ride start triggers continuous location tracking and geo-hash storage in local DB.
• Nearby service center data (geo-hash + contact) is cached locally and refreshed online.
• In offline mode, app compares current geo-hash with cached data to find nearby centers.

Getting Started

Follow these steps to set up and run the app locally.

Prerequisites

Before you start, make sure you have the following installed:

• Node.js (LTS version recommended)
• Python (3.12 or later)

1. Clone the Repository

git clone https://github.com/jayesh9747/QuickAid.git
cd QwikAid

2. Set Up Environment Variables

Create a .env file in the root of the project and add the necessary environment variables:

touch .env

Edit .env and add the environment variables provided in .env.sample file

3. Install Dependencies

Navigate to the required directories and install dependencies:

Frontend:

cd user-app-v3
npm install

Backend:

cd ../backend
npm install

AI :

cd ../AI
pip install -r requirements.txt

4. Start the FastAPI AI Backend

Navigate to the AI directory and run:

python main.py

5. Start the Backend Server

Navigate to the backend directory and run:

npm run start

6. Start the Frontend Server

Navigate to the frontend directory and run:

npx expo start

Team Details

Team Name: Drishtikon

Team Members

• Jayesh Savaliya { Team Lead }
• Vaishnavi Mandhane
• Luv Kansal
• Kunj Vipul Goyal