This paper introduces the Safe-Cities Chatbot, a novel conversational AI system designed to support urban security planning and emergency management, with a focus on complex cross-border scenarios. The system integrates Retrieval-Augmented Generation (RAG) techniques with domain-specific knowledge from the Security Vulnerability Assessment (SVA) framework, enabling context-aware assistance for security professionals. Unlike existing solutions, the chatbot was developed and evaluated using a real-world test — Piazza Transalpina, a public square on the Italian-Slovenian border — incorporating local regulatory constraints, spatial dynamics, and multi-agency coordination challenges. The chatbot supports three core functions: guiding users through structured security assessments, offering event-specific planning advice, and simulating emergency scenarios tailored to the location. We detail the system’s architecture, its contextual reasoning strategies, and the simulation engine design. Our results demonstrate the potential of AI-driven conversational tools to enhance situational awareness, improve preparedness, and facilitate security planning across jurisdictions — a growing need in the face of new urban threats and regulatory requirements such as the NIS2 directive.