Graph RAG Implementation

We implement graph RAG systems that pair a knowledge graph with vector retrieval so language models can reason across entities and relationships, not just isolated text chunks. Our team designs the schema, extracts entities and relations from your content, builds the graph, and connects it to a retrieval layer that combines structured and unstructured signals. This lets users ask multi-hop questions, such as how a customer, contract, and product issue relate, and receive an answer that walks the connections. We handle entity resolution, graph construction, query planning, and evaluation. We also build interfaces for visualizing reasoning paths so analysts and auditors can see how a conclusion was reached. The result is a retrieval system that handles complex business questions standard RAG cannot, with traceable logic at every step.