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Structured and unstructured data – both or just structured?

 

Core strength is structured and semi-structured data across relational databases, warehouses, lakes, and platforms like Databricks, Snowflake, and Microsoft Fabric. For unstructured data, Timbr is the semantic backbone, not a document store or NLP extraction engine: the ontology provides the structured context that grounds retrieval over unstructured content. This is the GraphRAG story, and Timbr powers scalable GraphRAG and LangGraph agent workflows. Entities extracted from documents can be modeled as concepts and linked into the ontology.

In short: structured/semi-structured natively; unstructured handled through ontology-grounded context and GraphRAG, typically alongside vector/RAG components rather than replacing them.

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Model a Timbr SQL Knowledge Graph in just a few minutes and learn how easy it is to explore and query your data with the semantic graph

Model a Timbr SQL Knowledge Graph in just a few minutes and learn how easy it is to explore and query your data with the semantic graph

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