AI‑powered search features in consumer apps bring semantic, conversational discovery to complex product catalogs — think natural‑language “Airbnb‑style” queries that surface relevant listings, photos, reviews, availability and policy documents. These capabilities combine embedding‑based retrieval, retrieval‑augmented generation (RAG) and multimodal models to match user intent rather than keywords, provide sourced answers, and let users refine searches conversationally. This topic is timely as of 2026 because LLM and multimodal model families and managed platforms have matured into production APIs, raising expectations for grounded answers, low latency, privacy controls, and multimodal indexing. Key building blocks include high‑quality embeddings and vector stores (for semantic match), model inference and grounding (for accurate, cited responses), and document/asset ingestion pipelines (for photos, PDFs, and listings). Representative tools: Perplexity AI — consumer answer engine that returns web‑grounded answers and citations; GPTGO — a free interface combining web search results with ChatGPT‑style responses; Cohere — enterprise LLMs, embeddings and retrieval services for private customization; Vertex AI and Google Gemini — Google Cloud’s managed training/inference and multimodal model family; Claude — Anthropic’s conversational models for analysis and assistant workflows; Notion — an AI‑enabled knowledge workspace that embeds search into content; PDF.ai and ChatPDF — document chat tools for extracting answers from PDFs and uploads; IBM watsonx Assistant — enterprise virtual agents and orchestrations. For product teams building Airbnb‑style search, the tradeoffs are accuracy vs. latency, freshness vs. cost, and openness vs. data privacy. Practical implementations blend vector retrieval, signal fusion (filters, geolocation, pricing), provenance/citation, and operational tooling from enterprise search platforms and LLM APIs.