Most organisations still rely on static, fragmented knowledge: documents spread across drives, Slack, inboxes, or internal wikis. Over time, this information decays, diverges, and loses context, while AI systems are expected to reason over it. The result is costly AI pipelines, unreliable insights, and decisions made on incomplete or outdated intelligence.

In this session, I will show how Spiintel transforms fragmented organisational knowledge into a living, evolving intelligence layer. Instead of treating knowledge as static documents, Spiintel models it as a temporal, ontology-driven graph that humans and AI collaboratively build, maintain, and reason from.

Built on Neo4j, Spiintel enables organisations to:
- Define and evolve their ontology through a dedicated META subgraph
- Enrich the knowledge graph using natural language and file ingestion
- Autonomously resolve duplicates while preserving full historical context
- Retrieve insights through hybrid search and Text2Cypher, grounded in structured intelligence rather than raw text

I will walk through Spiintel’s three core components: META Subgraph, Enrich, and Retrieve. I will demonstrate how they work together to solve real-world challenges such as schema evolution, entity resolution, temporal tracking, and inconsistent data structures. Using simple natural language, knowledge can be created, updated, merged, and queried while the system maintains a complete, auditable history.

By the end of the session, you’ll understand:
- How to design and evolve a temporal knowledge graph that mirrors how your organisation actually works
- How natural-language-driven CRUD and ingestion enable continuous intelligence building
- Why ontology-first, AI-native architectures reduce integration cost and improve model performance
- How graph-native retrieval enables fast, context-rich insights for both humans and AI

If your teams are struggling with siloed knowledge, brittle RAG pipelines, or AI systems that don’t scale with organisational complexity, this talk will show how to build a connected, self-evolving intelligence layer using graph technology.