MeshForce: An AI-Assisted Real-Time Volunteer Dispatch System for Mass Gathering Events — A Case Study on Mahakumbh 2025

Mass religious gatherings such as the Mahakumbh attract tens of millions of pilgrims within a temporary, high-density event footprint, placing enormous coordination demands on the volunteer workforce responsible for medical, crowd-control, and lost-and-found response. Existing coordination at such events is largely manual, relying on radio calls and word-of-mouth routing, which delays the matching of an available, appropriately skilled volunteer to an emerging incident [1], [3]. This paper presents MeshForce, an AI-assisted, real-time volunteer dispatch system comprising a volunteer Progressive Web App (PWA), a FastAPI backend, a Supabase (PostgreSQL + Realtime) data layer, and an administrative command-center dashboard with a live map. Incoming incident reports, submitted in free-form natural language or via SMS in low-connectivity conditions, are parsed into structured metadata using a large language model, and a composite scoring function combining geodesic distance, skill overlap, language match, and volunteer exhaustion ranks and dispatches the best-available volunteers. We describe the system architecture, the dispatch scoring algorithm, and a cost-conscious mock/production LLM design that enables full-scale simulation without incurring inference cost. Simulated evaluation with 50 volunteers and 15 concurrent incidents is used to validate dispatch latency and correctness against the system's stated non-functional requirements. We discuss applicability of this architecture to other mass-gathering and disaster-response contexts and outline limitations, including the absence of a field trial.