This topic covers the platforms, architectures and tools enterprises use to manage AI-ready data and deliver analytics-driven decisions. As organizations move from experimentation to production, they combine governed data warehouses, multimodal databases, vector stores, analytics/BI engines and agent frameworks to support retrieval-augmented generation (RAG), real-time dashboards, and ML pipelines. Snowflake, Anthropic and other LLM providers are often paired with pipeline and observability vendors to enable secure, scalable model access while preserving data lineage and access controls. Key categories include AI data platforms and data analytics tools. Examples from the current landscape: Domo — an enterprise data platform that automates data preparation, connects hundreds of sources and surfaces real-time insights; Sisense — an embedded BI and AI analytics platform with developer SDKs and marketplace capabilities; Alteryx One — a governance-first, no-code/low-code analytics suite for workflow automation and deployment; Activeloop Deep Lake — a multimodal database that versions and indexes unstructured data and embeddings for RAG workflows; LangChain and related engineering stacks that power stateful agent apps and LLM orchestration; and agent/automation platforms (AutoGPT, Agentverse) plus document-chat tools (ChatwithData) that expose natural-language access to datasets. Trends making this category timely in late 2025 include broader adoption of vector/RAG patterns, emphasis on governance and reproducibility, demand for multimodal and versioned data stores, and growing use of autonomous or agentic workflows. Buyers should evaluate platforms for ML-readiness (vector/embedding support, streaming and versioning), governance and auditability, developer tooling for LLM integration, and operational automation. The result is a heterogeneous stack where data management, model access and analytics converge to support practical, auditable AI applications.