Data & AI

NucliaDB’s indexing system is the backbone of its Retrieval-Augmented Generation capabilities, organizing extracted text, inferred entities and semantic vectors from customer documents. This design enables lightning-fast, context-aware searches that power Nuclia’s advanced data retrieval features.

Designed for Nuclia’s AI Search platform, NucliaDB is a multilayered, dynamically sharded storage engine that unifies blob storage, key-value metadata, and vector indexing to scale semantic search over unstructured data. Its cloud-native architecture, powered by NATS and gRPC, enables fault-tolerant distributed search while still offering a lightweight standalone mode.

LLM-powered agents (or AI copilots) take generative AI beyond basic Q&A by combining conversational ability with goal-driven behavior, and Nuclia makes it easy to build these agents by pairing RAG-powered context with tools for ingestion, prompting and deployment. This enables highly tailored, domain-specific assistants—from city guides to enterprise copilots—without requiring deep ML expertise.

This article explores how Nuclia streamlines research by transforming scattered AI-related resources into direct, actionable answers—eliminating manual search, reducing noise and ensuring accuracy without hallucinations.