From Static to Living: Building an AI-Ready Temporal Graph with Spiintel

Session Track: Knowledge Graphs & GraphRAG

Session Time: 11:00 am - 11:30 am April 15

Session description

Most organisations struggle with fragmented knowledge: documents spread across drives, Slack, inboxes, docs, etc. All disconnected and decaying as the time goes. As a result, teams make critical decisions on incomplete or inconsistent information.

In this session, I will show how Spiintel transforms static organisational data into a living, interconnected, AI-ready temporal graph. Using Neo4j under the hood, Spiintel collaboratively (human-machine) builds and evolves your ontology, enriches your knowledge base via natural language or file upload, and enables powerful retrieval through hybrid search and Text2Cypher.

You’ll learn how three key components (the META Subgraph, Enrich, and Retrieve plugins) work together to solve real-world challenges like schema definition, entity deduplication, temporal tracking, and inconsistent data structures. I will demonstrate how knowledge can be created, updated, merged, and queried using simple natural language while the system maintains complete historical context.

By the end of the session, you’ll understand:
– How to design and evolve a temporal knowledge graph that mirrors your organisation
– How natural-language-driven CRUD and file ingestion work in practice
– How to build AI-ready graph architectures that reduce integration cost and improve model performance
– How hybrid search and Text2Cypher enable fast, context-rich organisational insights

If your teams are struggling with siloed knowledge, inefficient AI pipelines, or decision-making bottlenecks, this talk will show you how to unlock a connected, self-evolving organisational intelligence layer using graph technology.