AI‑Powered Search & Recommendation Platforms cover the technologies and vendors that turn raw data and user signals into contextualized, personalized search results and ranked recommendations. As of 2026, enterprise teams are shifting from keyword lists to vector-based retrieval, multimodal models, and conversational interfaces that blend retrieval‑augmented generation (RAG), real‑time web grounding, and recommender systems to improve relevance and explainability. Key capabilities include embedding and vector search for semantic matching, LLMs and multimodal models for natural language understanding and answer generation, real‑time grounding and citation for provenance, and policy/monitoring tools for safety and compliance. Representative platforms: Perplexity AI (web‑grounded answers and developer APIs), Google Vertex AI (managed model training, deployment and Model Garden), Google Gemini (multimodal generative models), Cohere (private/customizable LLMs, embeddings and retrieval), Anthropic’s Claude family (conversational assistants), Microsoft 365 Copilot and IBM watsonx Assistant (application‑integrated assistants/agents), GPTGO (free hybrid search/answer interface), and StackAI (no‑code/low‑code agent and orchestration platform). Why this matters now: commercial providers have matured core building blocks—scalable vector stores, hosted LLMs, and operational tooling—making pilots feasible at product scale. Organizations face practical tradeoffs: accuracy vs. latency, cloud vs. private deployment for data governance, and the need for provenance and human‑in‑the‑loop evaluation. Consumer marketplaces and enterprise apps (for example, recent pilots from major platforms) are testing search and recommendation AI to improve discovery and conversion, which increases demand for robust enterprise search stacks. For procurement and architecture, prioritize solutions that combine reliable retrieval, model customization, measurable provenance, and governance to reduce risk while improving relevance and personalization.