Arango has launched Contextual Data Platform 4.0, introducing a 'Contextual Data Layer' architecture for enterprise AI. The platform unifies graph relationships, vector embeddings, document knowledge, and operational data into a single multi-model foundation built on ArangoDB. Key new capabilities include AutoGraph (automated knowledge graph generation), AutoRAG (adaptive retrieval combining GraphRAG, vector search, and hybrid retrieval), AQLizer (natural language to AQL query translation), Arango Ada (AI digital assistant for developers), and a Visualizer for graph exploration. The platform targets enterprises moving AI from experimentation to production, promising 30–50% reduction in integration complexity and 2–4× faster AI development cycles. It supports Kubernetes-native deployment, RBAC, BYOC, and air-gapped environments.

10m read timeFrom arango.ai
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Table of contents
Why Enterprise AI Needs a Contextual Data LayerSimplifying Enterprise AI ArchitectureIntroducing the Arango Agentic AI SuiteWorking with Context: Developer and Data InteractionArango Ada TM : The AI Digital AssistantExpanding the Value of Existing ArangoDB DeploymentsArango AutoGraph TM : Automating Context CreationArango AutoRAG TM : Automated Retrieval for Contextual AIArango AQLizer: Natural Language to Graph QueriesArango Visualizer: Exploring Enterprise ContextEnterprise-Ready Platform OperationsReal-World AI ApplicationsScaling AI Across the EnterpriseThe Future of Enterprise AIBuild AI Systems That Reason with Context

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