Data & AI

Discover how agentic RAG combines Retrieval-Augmented Generation with autonomous AI agents to deliver accurate, context-aware and trustworthy responses. Learn why this advanced architecture outperforms traditional RAG for enterprise AI by facilitating dynamic workflows, compliance and efficiency.

A strong data foundation is the offensive line of AI—rarely celebrated, but absolutely essential to protecting performance, ensuring consistency and powering every winning play.

Historically, when interacting with machines, humans had to adapt to the machine’s language to ask questions, typically using SQL queries, and then interpret the machine’s response. This response usually comes in a structured data format, such as a table or a JSON string, following a specific schema.

NucliaRAG (also known as NucliaDB) is a core component of Nuclia’s platform, designed to store all processed data uploaded by customers and to coordinate retrieval-augmented generation (RAG) queries efficiently across that data. One of the main components of NucliaRAG, and the subject of today’s article, is the indexing system. It indexes all text extracted from customer documents, as well as the inferred entities and semantic vectors in order to power the fast search capabilities that are the building blocks of the RAG features.