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
•14m read time• From arango.ai
Table of contents
Why Unified, Current and Trusted Business Context is the Missing Layer in the AI StackTL;DREnterprise AI Failures Aren’t About the ModelWhat Vector Databases Do Well & When They Are EnoughWhy Context Reconstruction Fails at ScaleWhat’s Next?Sort: