AWS AI Hackathon Hong Kong 2025
HAECO Bay Management System
Built in 14 days โข 130+ shortlisted teams โข Hong Kong's first and largest AWS AI Hackathon
Built in 14 days โข 130+ shortlisted teams โข Hong Kong's first and largest AWS AI Hackathon
HAECO operates in a large-scale aircraft base maintenance environment, delivering maintenance services under complex operational, regulatory, and resource constraints. Effective bay assignment requires balancing aircraft compatibility, task urgency, and dynamic operational changes.
Bay assignment is done manually with time pressures, leading to errors and potential regulation breaches
Each bay has constraints on aircraft models and maintenance task types, plus geographical dependencies
Frequent task reprioritization due to unpredictable external factors creates planning bottlenecks
Automated bay assignment using AI that considers work type compatibility, urgency, bay dependencies, and operational factors
Interactive dashboard showing bay utilization, aircraft schedules, and system statistics at a glance
Web-based system that opens directly in browser - fast, secure, and accessible anywhere
Built-in chatbot for instant help, conflict detection, and operational insights
Zero-install web app opens directly in browser. Planners instantly see upcoming aircraft, bay utilization, and system statisticsโall in one place.
Download CSV template, fill in aircraft registration and task details, then import. System validates and processes everything locallyโfast and secure.
Bay assignments generated automatically. System considers aircraft model constraints, bay dependencies, and task urgency. Fewer errors, reduced unnecessary movements, operational efficiency insights.
Real-time bay availability and aircraft assignments. Digital replica of HAECO hangar layout with daily view.
Clear insights into bay utilization, cost reports, and operational trends. Managers make confident, data-driven decisions.
Built-in chatbot responds instantly to questions like: "How do I assign an aircraft to a bay?" "What are today's conflicts?" "Show me optimization statistics"
Used Kiro to document system requirements through vibe coding. Validated and clarified specifications to ensure robust, logically consistent design.
Built web-based solution with HTML, CSS, JavaScript. Leveraged Kiro's support to streamline development and focus on core logic and UX.
Integrated AWS Q Developer for intelligent bay assignment. Implemented optimization engine with comprehensive rule-based planning.
Created 3-minute demo video, prepared presentation materials, and delivered system walkthrough with UI design showcase.
Developer & Demo Presenter
Led the technical direction of the hackathon build, connecting solution design, implementation, and the final live presentation into one coherent story.
Transformation & Technology
Technology Innovation Team
Developer โ Testing & Documentation
Strengthened the build through validation, debugging, and documentation support so the prototype stayed stable, explainable, and presentation-ready.
Transformation & Technology
Technology Innovation Team
Business Analyst
Grounded the project in business requirements and workflow logic, helping the team shape a solution that matched real operational use cases.
Transformation & Technology
Subject Matter Expert (SME)
Brought real maintenance knowledge into the build so the concept stayed connected to actual bay operations, data realities, and on-the-ground constraints.
Base Maintenance
Developer & Demo Presenter
Developer โ Testing & Documentation
Business Analyst
Subject Matter Expert (SME)
Developer & Demo Presenter
Developer โ Testing & Documentation
Business Analyst
Subject Matter Expert (SME)
"Build Tomorrow's Hong Kong with AI"
Official Website aws-hong-kong-hackathon.devpost.comRepresented HAECO at AWS re:Invent (Las Vegas), interviewed on-site and featured in unwire.hk covering the HAECO Bay Management System.
Demonstrated ability to explain complex operational systems to a global audience.
โถ๏ธ YouTube InterviewThe prototype outlines a clear digitalization direction, demonstrating how data-driven approaches and system design principles can inform future workflow innovation.
A short reflection on what this project taught me and how it shaped the way I approach building.
This hackathon was more than a competition for me. It was the first time I experienced how quickly an idea could move from concept to a working prototype when the goal was clear and the team was fully committed. Winning the project gave me confidence, but more importantly, it showed me that I enjoy building solutions that respond to real operational needs.
The biggest challenge was working under a very short timeline while still trying to make the project practical, presentable, and meaningful. It was not only about building a system, but also about explaining the logic clearly, shaping the story of the project, and making the final output understandable to others.
After this experience, I want to keep developing projects that combine operations, data, and AI in a practical way. I also want to keep improving how I communicate ideas, present systems, and turn technical work into something that creates visible value for real users.