Arango, the company pioneering the live Contextual Data Layer for enterprise AI, announced it has been named a Strong Performer in The Forrester Wave™: Multimodel Data Platforms, Q2 2026. According to the report, Arango is “well-suited to organizations seeking a contextual data foundation where multihop graph performance and verifiable reasoning are mission-critical for trusted AI.”

According to the report, Arango is “well-suited to organizations seeking a contextual data foundation where multihop graph performance and verifiable reasoning are mission-critical for trusted AI.”

The recognition comes at a time when enterprises are increasingly focused on how to create and operationalize business context for AI. As organizations move beyond experimentation and into production deployments of AI agents, assistants, and applications, many are reevaluating architectures built from separate databases, vector stores, search engines, and integration layers in favor of platforms that simplify how business context is connected, governed, and made available to AI systems.

Arango believes the recognition reflects growing enterprise demand for a unified approach to multimodel data management. According to the evaluation, Arango received the highest possible scores in the criteria of adoption and unified multimodel architecture.

“As organizations move AI initiatives into production, many are discovering that the challenge is no longer simply connecting data. The challenge is creating trusted business context that AI systems can reason over consistently,” said Ravi Marwaha, Chief Operating Officer and Chief Product & Technology Officer, Arango. “Enterprises increasingly want a simpler way to build, govern, and operationalize business context across their data landscape. We believe this recognition reflects growing demand for unified platforms that help organizations create a trusted foundation for enterprise AI.”

Why It Matters

At scale, enterprise AI is fundamentally a trusted business context challenge. AI agents, assistants, and applications must understand how customers, products, policies, processes, and operational events relate to one another. Organizations are increasingly looking for ways to create this context once, govern it centrally, and make it available across AI initiatives rather than rebuilding it repeatedly.

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Agentic AI systems increasingly require access to multiple forms of data, including relationships, documents, vectors, search results, and operational records. As a result, technology leaders are seeking platforms that can:

• Unify graph, vector, document, key-value, and search capabilities within a single architecture
• Reduce the need for multiple databases, synchronization pipelines, and query layers
• Support governance, lineage, provenance, and explainability across connected data
• Scale transactional, analytical, and AI workloads with greater operational control
• Accelerate the path from AI prototype to production deployment

Rather than managing separate systems for each workload, organizations are increasingly seeking a simpler foundation for intelligent applications, assistants, and AI agents.

Recognition for a Contextual Data Foundation

In its evaluation, Forrester cited Arango’s native multimodel architecture, which combines unified storage, execution, and schema propagation within a single engine. The report also noted Arango’s integrated AI capabilities, which combine graph, vector, and document data in a single retrieval path with source citations.

Arango believes these capabilities are increasingly important as organizations seek to build AI systems capable of reasoning across connected enterprise data while maintaining transparency, governance, explainablity and trust.

Built on a graph-native multimodel foundation, the Arango Contextual Data Platform unifies graph, vector, document, key-value, and search capabilities into a single distributed engine. The platform enables organizations to create a live Contextual Data Layer, a persistent, governed representation of business context that can be reused across AI systems across the enterprise.

Building Trusted AI Starts with Trusted Business Context

As enterprises expand AI initiatives across products, workflows, and business functions, data foundations must support more than performance. They must also provide explainability, governance, traceability, and operational scalability.

Arango believes multimodel data platforms play an increasingly important role in enabling organizations to build context once and reuse it across AI systems, helping reduce duplication, improve consistency, and accelerate deployment.