This topic examines how modern AI components are being combined to build Airbnb‑style search and itinerary features for travel and hospitality platforms. It covers the core technical patterns—vector/embedding search and retrieval-augmented generation (RAG) for natural‑language queries, real‑time web grounding and scraping for inventory and pricing, and agentic orchestration for multi‑step booking flows—and the enterprise considerations that determine production viability. Relevance in 2026: travel platforms need fast, personalized discovery and trustworthy, up‑to‑date answers as consumers expect conversational search, dynamic pricing, and on‑demand itineraries. Advances in managed ML/GaI platforms (e.g., Vertex AI for end‑to‑end model lifecycle), enterprise LLMs and embeddings (Cohere), web‑grounded answer engines (Perplexity), and low‑code/no‑code automation (Anakin.ai) make it easier to combine retrieval, generation, and workflow automation. Agentic infrastructure (Xilos, Adept) and orchestration platforms (Kore.ai) are emerging patterns for stitching search, booking, and external services while preserving governance and observability. Key tool roles: enterprise search systems provide vector indexing and relevance tuning; web scraping and synthesis tools keep availability and pricing current; grounded answer engines and citation-capable APIs improve provenance; conversational and voice agents (PolyAI) enable natural interactions; no‑code platforms speed integration. Practical considerations include data freshness and legality of scraping, evaluation and bias mitigation for recommendations, latency and cost tradeoffs with real‑time retrieval, and governance/observability for multi‑agent flows. Comparing these components across vendors helps teams design architectures that balance accuracy, safety, and operational complexity for scalable travel search and itinerary services.