Design and Validation of a Multimodal AI Conversational System for Automated Travel Itinerary Generation and Promotional Video Synthesis

Abstract

The manual creation of personalized travel itineraries remains a labor-intensive process that requires travel agents to consolidate heterogeneous information from multiple sources, including natural language interactions, booking confirmations, screenshots, and reservation documents. Although recent advances in multimodal artificial intelligence have significantly improved language understanding and content generation, existing solutions typically address isolated tasks rather than providing an integrated workflow capable of automating the complete travel planning process. This paper presents the development and validation of an AI-powered conversational agent designed to automate itinerary generation and generative video synthesis through natural language processing (NLP) and multimodal data extraction. The system, evaluated at Technology Readiness Level 4 (TRL4), employs a Multi-Agent System (MAS) architecture, integrating specialized large language models (Gemini 2.5/2.0 Flash, GPT-4o-mini) with optical character recognition (OCR) and Latent Diffusion Models to interpret user requests, extract structured data from images and PDFs, and produce comprehensive travel packages formatted as professional PDF deliverables alongside AI-synthesized promotional videos. Validation in a controlled laboratory environment demonstrated over 90% intent recognition accuracy, successful data extraction from non-standard formats, and the automated production of both client-ready documents and coherent visual narratives from static itinerary data. The system achieved an average video generation latency of 4.2±0.8 minutes, while maintaining structural consistency in the generated PDF itineraries. The system represents a viable proof-of-concept for intelligent travel planning automation, with implications for enhancing operational efficiency in the tourism industry and reducing cognitive load on human agents. Future work will advance the prototype to TRL5 through integration with external booking APIs and real-user testing scenarios.