RAG at Machine Speed: Built for the Demands of Deep Research
Retrieval-Augmented Generation (RAG) is becoming foundational for enterprises adopting generative AI. By grounding large language models (LLMs) in private, structured, and unstructured data, RAG enables secure, context-aware responses tailored to real business needs—powering customer-facing applications like conversational support, intelligent search, and personalized self-service, as well as internal tools for research and decision automation.
But the demands on retrieval are changing. We are entering an era of deep research, where AI agents must perform multi-step reasoning, issuing many retrievals in sequence to explore, verify, and synthesize information. This shift requires retrieval systems that operate at machine speed and scale. Organizations still relying on systems optimized for human-speed analysis will struggle to keep up—and risk falling behind.