Turn enterprise data into a knowledge graph

Text-to-Graph is the process of transforming unstructured information into structured entities, relationships, and properties represented in a knowledge graph.

Perseus automates this process by mapping information from enterprise data to an ontology and building a connected graph that can be queried by AI applications and agents.

Perseus connects to your source data, extracts entities and relationships, maps them to your ontology, resolves entities across sources, and assembles the results into a structured knowledge graph.

As source data changes, affected parts of the graph can be refreshed without rebuilding the entire knowledge base.

You need a schema that defines the concepts and relationships your knowledge graph should contain.

You can bring an existing ontology or use Perseus to generate one from your source data and business use case before building the graph.

Perseus can connect multiple enterprise sources, including PDFs, databases, APIs, and raw files. This makes it possible to bring structured and unstructured knowledge into the same graph.

Perseus performs entity resolution across connected sources. It identifies records or mentions that refer to the same entity and reconciles them within the knowledge graph.

This helps connect information that would otherwise remain fragmented across documents and systems.

Perseus builds and maintains the knowledge graph by transforming source data into structured entities and relationships.

A graph database stores and queries the resulting graph. Perseus therefore complements graph database infrastructure rather than replacing it.

Yes. The graph can provide structured context for GraphRAG systems and AI agents.

Perseus Graph Retrieval combines graph traversal and semantic retrieval to return entities, relationships, paths, and source references rather than relying only on isolated text chunks.