GenAI search and retrieval platforms combine large language models, embeddings-based retrieval, and indexed knowledge layers to deliver context-aware answers, document search, and agent workflows. As of mid‑2026 the field is defined by multimodal models, retrieval‑augmented generation (RAG), and stronger requirements for privacy, observability, and live web grounding. Google’s Intelligent Search direction pairs Gemini multimodal models with Vertex AI’s managed tooling for model discovery, fine‑tuning, deployment and monitoring — a unified path for enterprises that want integrated Gemini APIs and Google Cloud governance. Alternatives emphasize different tradeoffs: Cohere offers private, customizable LLMs, embeddings and retrieval services tailored to enterprise security and fine‑tuning; Perplexity AI focuses on real‑time, web‑grounded answers and citation tracking for research use cases; GPTGO provides a lightweight, free hybrid of Google results and chat‑style answers for exploratory searching. Open and integrative approaches include AI Knowledge Search by Amurex, which creates a unified open‑source search layer across personal and corporate data, and Notion, which embeds AI search inside a broader knowledge‑management workspace. Kore.ai targets orchestrated multi‑agent enterprise workflows with governance and observability, while no‑code platforms like Anakin.ai speed deployment of document search and automation. Supporting tooling such as Prmptless improves repeatability of prompts and RAG pipelines. Choosing between Google’s stack and alternatives hinges on priorities: multimodal model quality and cloud integration, on‑prem or private model needs, web grounding and citations, developer APIs and observability, or no‑code deployment. Understanding these tradeoffs is essential for selecting a GenAI search solution that fits enterprise scale, compliance, and knowledge‑management goals.