Knowledge Graph-based RAG

We allow organizations to automatically convert their documents into a Neo4j knowledge graph, enabling a new, powerful way of querying information. Users can interact with the system in natural language: a human prompt is translated into iterative Cypher queries — Cypher being Neo4j’s graph query language, — that intelligently explore the graph until the relevant information is found. The system then provides an explanation in human language, ensuring the results are easily understandable.

By structuring knowledge into a graph rather than relying on unstructured text retrieval, KG-RAG (Knowledge Graph - Retrieval-Augmented Generation) significantly enhances factuality robustness and traceability. Instead of searching through dispersed text chunks, human language prompts are mapped to targeted graph-based searches, retrieving precise and verifiable facts. The structured and explicit nature of the graph also allows for seamless updates and maintains a clear, auditable trace back to the original data sources.

Compared to traditional Retrieval-Augmented Generation (RAG), this graph-based approach considerably reduces the risk of hallucinations, thereby strengthening trust in AI-generated answers. The precise, source-grounded retrieval enabled by the graph structure makes Graph-Based RAG particularly well-suited for critical applications that demand high standards of reliability, transparency, and accuracy.