As businesses move beyond experimental AI projects into production-grade agents and assistants, they are increasingly abandoning fragmented stacks of separate databases and vector stores. Instead, the market is pivoting toward unified platforms capable of connecting, governing, and serving business context to AI systems from a single source. Arango received top marks in the evaluation for its adoption metrics and its ability to integrate graph, vector, and document data into one retrieval path.

Forrester noted that Arango is particularly well-suited for enterprises where multihop graph performance and data provenance are non-negotiable for trusted AI. By unifying storage and execution within a single distributed engine, the platform allows developers to build context once and reuse it across multiple AI initiatives. According to Ravi Marwaha, COO and Chief Product & Technology Officer at Arango, the primary hurdle for organizations is no longer just data connectivity, but the ability to create a consistent, explainable business context that AI can reliably reason over at scale.