The tourism industry in high-altitude regions, specifically the Himalayas, faces two critical and distinct challenges: ensuring operational safety amidst volatile weather and traffic conditions, and overcoming commercial inefficiency caused by a lack of 24/7 customer support. Traditional solutions have largely failed to address these issues simultaneously, often relying on fragmented manual processes or static chatbots that lack real-time capabilities. This paper presents a unified Artificial Intelligence (AI) platform designed to address these distinct problems using a novel ”Hybrid AI” architecture. A ”Two-Brain” system is proposed that integrates Retrieval Augmented Generation (RAG) for static, knowledge-intensive customer queries and Tool-Using Large Language Model (LLM) Agents for dynamic, real-time logistical support. By leveraging open source technologies, specifically Django for the backend framework and PostgreSQL with pgvector for high-dimensional vector storage, and implementing semantic caching, a cost-effective, maintainable solution is demonstrated for Small and Medium Enterprises (SMEs) in developing economies. The design mitigates hallucination risks through strict context faithfulness protocols and ensures data sovereignty via a self-hosted infrastructure. Performance metrics regarding average latency, token cost efficiency, and data freshness are analyzed, showing that the dual-pathway approach significantly optimizes resource usage compared to traditional methods. Specifically, the semantic caching mechanism reduces API costs by approximately 60 percent for repetitive queries, while the real-time agent ensures critical safety data is retrieved with a freshness of under 10 seconds. This study concludes that such a hybrid architecture provides a scalable, safe, and economically viable model for modernizing tourism operations in low-resource environments.
Dhakal et al. (Fri,) studied this question.