Graph Layer for AI
Most AI projects stall when they hit the data layer. Unstructured documents, siloed databases, no shared schema. Perseus turns that into a knowledge graph your AI can actually reason over.
Each capability is designed to work independently or together, so teams can ship quickly and scale with a reliable graph foundation.
Automatically generate a custom ontology from your source documents to serve as the blueprint for your knowledge graph. Tailor the schema by simply describing your specific business use case, or seamlessly upload and extend your existing ontologies.
Learn moreTransform raw documents into a structured semantic network of entities and relationships. Use automated Text-to-Graph pipelines to generate standardized, production-ready knowledge graphs built specifically to power your AI applications.
Learn moreExtract high-signal entities, relationships, and semantic paths directly from your knowledge graph. Empower your AI applications and agents to reason over deeply connected, structured context rather than isolated fragments of text.
Learn moreExtract entities and relationships from your data with a single call.
graphs = perseus_client.build_graph(
file_paths=["path/to/your/document.txt"],
)
Senior ontologist
Focuses on ontology governance, evolution, and semantic consistency.
Graph specialist
Leads ontology design and schema alignment for production knowledge graphs.
Graph researcher
Works on graph extraction quality, retrieval accuracy, and model-driven improvements.
Building Knowledge Graphs is hard.
Modeling them correctly is even harder: defining the right entities, structuring relationships, and evolving them as the domain changes.
That’s where our knowledge modeling and graph experts work side by side with your team.
From Fortune 500s to fast-moving startups, our customers are building the next generation of AI applications on a foundation of structured knowledge.
Perseus helped Alfa Laval process over 1,000 technical PDFs, structuring the content to improve answer quality and support various teams.
Perseus transformed 108 GB of biomedical text into structured graph knowledge for Wisecube, monitoring +500 classes and scaling integration.
