Building Trusted Business Context for Enterprise AI

Featuring New Research from Forrester The Forrester Wave^™: Multimodel Data Platforms, Q2 2026

Across the industry, organizations are recognizing that AI performance increasingly depends on business context. The challenge is that “business context” means different things to different people. Some define it as documents and retrieval. Others point to metadata, lineage, governance, knowledge graphs, operational state, or enterprise semantics.

In reality, these are all pieces of a larger challenge. Business context is the living representation of how an organization operates. It connects customers, products, policies, processes, relationships, governance, and operational events into a form that AI systems can understand, govern, and use.

As organizations move AI agents, assistants, and applications from experimentation into production, they are discovering that information and context are not the same thing. AI systems can retrieve information. The harder challenge is understanding how that information relates to customers, products, policies, operational processes, and business decisions.

This is why organizations are increasingly reevaluating AI architectures built around separate databases, vector stores, search engines, and integration layers. As AI adoption expands, many enterprises are looking for simpler ways to create, maintain, and operationalize trusted business context at scale.

This shift helps explain the growing momentum behind multimodel data platforms and contextual data architectures designed to support enterprise AI.

Forrester just released The Forrester Wave^™: Multimodel Data Platforms, Q2 2026, which evaluates the multimodel platform (MMDP) providers and identifies the most significant ones. The report shows how each provider measures up and helps leaders select the right one for their needs.  We are honored that Forrester recognized Arango as “well-suited to organizations seeking a contextual data foundation where multihop graph performance and verifiable reasoning are mission-critical for trusted AI.”

Why Enterprise AI Requires Business Context

AI systems must reason across documents, relationships, transactions, operational events, and business policies. As a result, technology leaders are looking for platforms that can unify this fragmented data into a shared contextual data foundation for AI:

Technology leaders are looking for platforms that can:

• Unify graph, vector, document, key-value, and search capabilities in one architecture
• Reduce the need to stitch together multiple databases, pipelines, and query layers
• Support explainability, provenance, governance, and operational control across AI workloads
• Help teams move faster from prototype to production

The shift is not about replacing one database with another. It is about creating a trusted foundation of business context that can be reused across AI agents, assistants, and applications.

What Forrester Recognized in Arango

The Forrester report stated that Arango’s differentiating native multimodel architecture delivers unified storage, execution, and schema propagation in a single engine. This helps enterprise AI applications work across relationships, documents, vectors, search results, and operational data without rebuilding context across disconnected systems.

Forrester also noted that Arango’s integrated AI capabilities fuse graph, vector, and document data in a single retrieval path with source citations, enabling cross-model reasoning across multiple retrieval modes. These capabilities help organizations provide AI applications, assistants, and agents with connected business context while maintaining explainability, traceability, and governance.

What This Means for Enterprise AI

This requires a data foundation that can support accuracy, explainability, governance, and scale without introducing additional architectural complexity. Increasingly, organizations are adopting contextual data platforms that help them create a shared, governed, and trusted representation of business context and make it continuously available across AI projects.

For Arango, this recognition reinforces the direction we have been building toward: helping enterprises create a live Contextual Data Layer that connects enterprise data, relationships, governance, lineage, and operational context so AI systems can reason, decide, and act more reliably at scale.

About Arango

Arango is pioneering the live Contextual Data Layer for enterprise AI, helping organizations transform fragmented enterprise data into trusted, reusable business context that enables AI agents, assistants, and applications to reason, decide, and act with greater accuracy, explainability, and trust at scale.

Built on the Arango Contextual Data Platform—a graph-native multimodel data foundation that unifies graph, vector, document, key-value, and full-text search capabilities with ACID guarantees—the live Contextual Data Layer enables organizations to build context once and reuse it across AI initiatives.

The platform includes more than 20 built-in AI services for contextual modeling, retrieval, orchestration, and enterprise AI development. The result is more accurate decisions, end-to-end traceability, faster deployment, and measurable business outcomes. 

Organizations including NVIDIA, HPE, Zscaler, London Stock Exchange Group, Siemens, the U.S. Air Force, NIH, Articul8, and others rely on Arango to power enterprise AI. Learn more at arango.ai.

Forrester does not endorse any company, product, brand, or service included in its research publications and does not advise any person to select the products or services of any company or brand based on the ratings included in such publications. Information is based on the best available resources. Opinions reflect judgment at the time and are subject to change. This report is part of a broader collection of Forrester resources, including interactive models, frameworks, tools, data, and access to analyst guidance. For more information, read about Forrester’s objectivity here .