Go Beyond Vector Databases • Arango

Vector databases excel at similarity search and retrieval but fall short for enterprise AI that requires reasoning and action. The core limitation: vectors store proximity, not meaning—they can't manage relationships, time-based context, provenance, or trust. As systems scale, fixed-dimensional embeddings hit a representational ceiling where accuracy plateaus. Enterprise AI needs a contextual data layer that unifies structured, unstructured, and multimodal data with explicit relationships, temporal awareness, and governance—so agents and co-pilots can reason consistently, explain decisions, and act safely in production.