Fragmented data and missing business context are making it difficult for AI systems to reason, decide, and act reliably in production.

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At NVIDIA GTC 2026, a clear trend emerged among enterprise teams: building and maintaining custom AI data architectures from scratch is unsustainable. Fragmented data across CRMs, ERPs, vector stores, and knowledge bases creates a 'context gap' that prevents AI agents from reasoning reliably in production. Stitched-together 'Frankenstacks' can retrieve data but fail to maintain a unified, trusted view of business context. In response, Arango launched its Contextual Data Platform 4.0, featuring AutoGraph for continuous context maintenance and AutoRAG for dynamic retrieval strategy selection, aiming to provide a persistent contextual data layer for enterprise AI systems.

At NVIDIA GTC, a Clear Shift Emerged: Teams Are Moving Beyond Building AI Data Architecture from Scratch • Arango

Why Enterprise AI Struggles in Production