ArchyKnowledgebase: AI agents that understand your knowledge

The Archy AI Platform's built-in RAG and knowledge management stores information and retrieves it with simple RAG. ArchyKnowledgebase is a separate, more advanced Data and Knowledge Platform: it gathers knowledge from many systems, maps how the concepts in it relate, and makes it available to Archy agents through advanced RAG pipelines.

No. Qdrant handles vector search, which finds passages by meaning. A Neo4j knowledge graph keeps the relationships between concepts, and conceptual snapshots keep point-in-time views. Ontology and taxonomy pipelines give the knowledge its structure, so agents work with concepts and relationships, not just similar text.

Files, databases, object storage and other systems. ArchyKnowledgebase is built on Dagster, whose broad integration ecosystem lets it gather knowledge from many sources. Tell us which systems you use and we will confirm how they connect. You can also connect your own RAG systems or vector databases.

No. It works alongside your files and systems and turns their content into structured knowledge your agents can use.

Yes. Connect one or several of them to ArchyKnowledgebase. Because ArchyKnowledgebase integrates with the Archy AI Platform, your agents can use them alongside the knowledge ArchyKnowledgebase prepares.

Dagster for data orchestration, Qdrant for vector search, Redis for fast caching and session data, and Neo4j for the knowledge graph. On top, our ontology and taxonomy pipelines map the concepts in your knowledge and how they relate.

Similar text is not always the right context. A knowledge graph records how concepts relate, so agents can follow those relationships to context that similarity search alone would miss, and you can see the connections behind an answer.