Databases, documents, APIs, notes—your data is everywhere, but your AI agent only understands a fraction of it.
Most agents can query structured data, yet struggle to make sense of unstructured content and the relationships within it.
But what if you could turn that raw content into a knowledge graph—and plug it directly into your agent?
In this session, we’ll build an Aura Agent in starting with natural language interaction over structured data. We’ll then use LLMs to extract entities and relationships from unstructured content, generate a graph schema, and build a knowledge graph. Finally, we connect it back to the agent—enabling richer, context-aware responses powered by both structured and unstructured data.