Atlas
Built as a university project for the 'Introduction to AI' module, Atlas is an intelligent warehouse management system designed to optimize stock strategies and simulate real time inventory tracking. Using Tauri and PostgreSQL, it heavily incorporates AI pathfinding and optimization algorithms to dynamically organize layouts and route incoming packages. Despite a team member leaving mid-development, the prototype outperformed all 60 competing teams to secure the highest grade and remains a highly valued asset.
About This Project
Designing intelligent pathfinding and layout optimization algorithms for incoming package warehousing as part of a high pressure university AI module, compounded by losing a team member during development.
Engineered custom AI optimization models covering pathfinding and warehouse layout planning using Next.js/Three.js for the visual simulation, backed by a Tauri-desktop and PostgreSQL data pipeline.
Ranked 1st place out of 60 competing teams, earning the highest grade in the class and proving that a robust algorithmic foundation can outperform fully completed baseline systems.
Data Engineer
2025
Public
University
Technology Stack
Project Story
Designing intelligent pathfinding and layout optimization algorithms for incoming package warehousing as part of a high pressure university AI module, compounded by losing a team member during development.
Engineered custom AI optimization models covering pathfinding and warehouse layout planning using Next.js/Three.js for the visual simulation, backed by a Tauri-desktop and PostgreSQL data pipeline.
Ranked 1st place out of 60 competing teams, earning the highest grade in the class and proving that a robust algorithmic foundation can outperform fully completed baseline systems.
Insights & Takeaways
Highlights
- Developed as part of the 'Introduction to AI' university module, focusing on layout and pathfinding optimization.
- Achieved 1st place and the highest grade among 60 student groups.
- Successfully integrated a Tauri desktop wrapper, PostgreSQL database, and Three.js simulations.
Challenges
- Losing a key team member during active development, requiring rapid realignment and ownership of the AI and visualization modules.
- Implementing real time pathfinding and dynamic layout reorganization for new package arrivals.
Lessons Learned
- Learned to build resilient software architectures under team resource constraints, and proved that a well-designed AI prototype can easily outcompete complete but simpler systems.
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