An "at-risk customer" means one thing to Finance and something else to Sales, and both can be right, until an AI application quietly picks one definition and applies it everywhere. Stop by the Neo4j booth to see how a knowledge graph makes that business context explicit, so AI uses the right meaning for the right question, every time.
We'll have live demos of Neo4j in action, including GraphRAG, plus our team on hand to talk through what grounding AI in the actual structure of your business looks like for your data.
Trusted data gets AI to the starting line, but it doesn't tell an AI which business meaning applies. An "at-risk customer" may mean one thing in Finance and another in Sales, and both definitions may be right. When that context lives in people's heads, spreadsheets, or code, each AI application is left to reconstruct the business for itself — and a confident, accurate-looking answer can still be the wrong one.
Nyah will walk through what AI is missing, and how a graph connects business definitions to the people and data behind them — making the relationships an answer depends on explicit. The goal isn't to force every team onto one definition. It's a shared, governed knowledge layer that lets AI use the right meaning for the right task.
You'll leave with five questions for making your business meaning explicit before your AI invents its own.
