In this lightning talk, we'll explore a graph-first approach to real-time decision intelligence. Rather than relying solely on ML predictions, we combine multiple contextual graph layers, capturing entity relationships, behavioral signals, and latent structures to generate fully explainable, adaptive decisions in production systems.

The architecture fuses Neo4j graphs, vector similarity search, and policy engines into a composable pipeline. We’ll highlight how topology itself, not just features, serves as a signal, enabling systems to optimize outcomes even in sparse or shifting data environments.