Asset Management Tool that is designed to optimize enterprise-level asset tracking and management. Built on a robust tech stack, the backend leverages Django for its scalability and reliability, while the frontend is powered by React to provide a dynamic and responsive user experience.
The system is built with REST APIs, ensuring seamless communication between the frontend and backend while enabling smooth integration with external services. We integrated AI into the tool to provide predictive analytics, enhancing decision-making capabilities regarding asset utilization and maintenance schedules.
To ensure optimal performance and scalability, Redis was used for caching, and Celery was implemented to handle asynchronous tasks, making background processes like report generation and notifications highly efficient. The platform supports three key roles: System Admin, Lead, and Manager, each with custom permissions, ensuring granular control over asset management processes.
A standout feature is the Advanced Query Search, which empowers users to perform complex asset searches with ease. Additionally, real-time notifications ensure that users stay informed about critical updates, and SSO (Single Sign-On) integration provides enhanced security and ease of access across platforms.
All services were fully dockerized, streamlining development, testing, and deployment while ensuring consistency across environments. We used NGINX as a reverse proxy for improved load balancing and security. A well-architected CI/CD pipeline was put in place to automate testing, integration, and deployment processes, ensuring rapid delivery of new features and updates.
This project showcases my ability to design and implement highly scalable, secure, and feature-rich systems tailored to enterprise needs, with a strong focus on efficiency, maintainability, and user experience.