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Knowledge graphs represent information as a network of entities (people, products, concepts) connected by relationships (works at, is a type of, located in). Google, Amazon, and LinkedIn all use knowledge graphs to power their products.
In the LLM era, knowledge graphs complement RAG systems. While RAG retrieves relevant text chunks, knowledge graphs provide structured relationships that help LLMs reason more accurately — especially for complex queries involving multiple entities.
Use cases: enterprise search, customer 360 views, recommendation engines, fraud detection (relationship analysis), and AI assistants that need to understand domain ontologies.
We integrate knowledge graphs with LLM systems for enterprise clients who need structured reasoning — combining the power of graph databases (Neo4j) with natural language understanding.
Our AI-First engineers build production systems using Knowledge Graph technology. Talk to us.
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