FleetIQ AI is a web application that helps users search and browse auction vehicles using simple filters such as make, model, year, and mileage.
The goal of the project is to build a clean, fast, and easy-to-use vehicle search experience while learning modern full-stack software engineering practices.
Buying vehicles at auction can take a lot of time because buyers often have to search through hundreds or thousands of listings to find vehicles that meet their needs.
FleetIQ AI aims to simplify that process by allowing users to quickly search and filter auction vehicles.
The first version of FleetIQ AI focuses on one feature:
- Search auction vehicles using filters.
Users will be able to:
- Search by make
- Search by model
- Filter by year
- Filter by maximum mileage
- View matching vehicle results
The MVP will not include:
- User accounts
- AI recommendations
- Bidding
- Buying or selling vehicles
- Favorites or watchlists
- Backend APIs
- Database integration
Sample vehicle data will be stored locally in the frontend so the focus remains on learning React and TypeScript.
- React
- TypeScript
- Vite
- HTML
- CSS
- Python
- FastAPI
- PostgreSQL
- Docker
This project is designed to help me learn:
- React
- TypeScript
- Component-based architecture
- State management
- Routing
- Search and filtering
- Git and GitHub workflow
- Professional software development practices
After the MVP is complete, future versions may include:
- Backend API
- PostgreSQL database
- Real auction data
- AI-powered vehicle analysis
- Saved vehicles
- Price comparisons
- Vehicle recommendations
🚧 In Development — MVP Planning Complete