AI-Powered Mass Finder Dashboard

Mass schedules across the Archdiocese were distributed across multiple parishes and formatted inconsistently, making discovery inefficient for users. Traditional website navigation required manual browsing through individual parish pages, creating friction for users trying to locate nearby Mass services quickly. The lack of centralized data processing, geolocation awareness, and intelligent filtering made it difficult to surface the most relevant Mass options in real time.

I designed and implemented an AI-assisted discovery dashboard that aggregates parish Mass schedules through structured APIs and processes them through intelligent filtering and ranking logic. The system evaluates multiple contextual signals such as: • User location • Current date and time • Mass type availability • Parish proximity Using these signals, the platform dynamically ranks Mass options and presents the most relevant services first. The architecture also lays the groundwork for future conversational AI assistants that can recommend Mass services through natural language queries.