The Brains Behind AI: Multimodel Platforms Emerge as Critical Layer

SAN FRANCISCO & COLOGNE, Germany – January 21, 2026 – As enterprises race to deploy sophisticated artificial intelligence, a fundamental bottleneck has emerged not in the AI models themselves, but in the fragmented data architectures that feed them. Addressing this challenge, a new report from the technology research firm Forrester has cast a spotlight on an emerging category of technology: Multimodel Data Platforms (MMDPs), positioning them as an essential component for building the next generation of trustworthy and scalable AI.

In its Q4 2025 report, “The Multimodel Data Platforms Landscape,” Forrester recognized Arango, a provider of contextual AI data infrastructure, among 18 key vendors shaping this critical market. The recognition underscores a broader industry shift away from siloed databases and toward unified platforms capable of handling the complex, real-time data demands of advanced AI systems, particularly autonomous or “agentic” AI.

The Missing Layer in the Modern AI Stack

For years, the standard approach for managing diverse data types involved “polyglot persistence”—stitching together separate databases for relational, document, graph, and key-value data. While functional for traditional applications, this approach has proven to be a significant hindrance for AI, which needs to retrieve and reason across these different data models simultaneously and in real time.

Forrester defines an MMDP as a common data platform that provides storage, processing, and access to any data—structured, unstructured, or semistructured—and supports multiple data models within a single engine. In a related report, Forrester analyst Indranil Bandyopadhyay identifies these platforms as “the missing layer in your AI stack,” arguing they form the “cognitive core of agentic AI by integrating reasoning and memory into a single platform.”

This architecture is purpose-built to function as the “brain” and “memory” of an AI agent. By natively fusing different data models, MMDPs allow AI systems to perform complex queries that combine relationship analysis (graph), semantic understanding (vector), unstructured content (document), and factual ground truth (key-value). This unified approach eliminates the latency and complexity inherent in navigating fragmented data silos, enabling the fast, accurate, and context-aware responses required for modern enterprise AI. Without such a foundation, AI initiatives often face challenges with performance, scalability, and the trustworthiness of their outputs.

Arango's Role in the New Data Paradigm

The Forrester report highlights Arango for its capabilities in powering AI, including agentic AI, as well as for enabling operational 360-degree views and digital twin modeling. The firm’s platform is designed as a native multi-model database, integrating graph, vector, document, key-value, and search capabilities from the ground up. This is a key distinction from other platforms that may have started as a single-model database before adding other functionalities.

This native integration is managed through a single, declarative query language, AQL (ArangoDB Query Language), which allows developers to construct sophisticated queries that traverse different data models seamlessly. The report also notes Arango’s flexible deployment options, which include on-premises, hosted private SaaS, and multitenant SaaS, catering to diverse enterprise security and infrastructure requirements.

“Companies are under pressure to deliver better business outcomes from AI projects that drive trust and adoption, including building AI they can trust, running it at scale, and achieving better economics by reducing data-stack complexity,” said Shekhar Iyer, CEO of Arango. “We believe Arango’s inclusion in Forrester’s Multimodel Data Platforms Landscape reflects the increasing demand for platforms that unify data models to support real-time, multimodal, and agentic AI workloads.”

From Fragmented Silos to a Unified System of Context

The move toward MMDPs represents a strategic pivot for many organizations. The primary goal is to create a unified “System of Context” that bridges disconnected enterprise data with AI systems. This is crucial for building what has been termed “Contextual AI”—intelligent systems that understand relationships, meaning, and intent across vast and varied datasets.

One of the most significant benefits of this approach is the ability to build more trustworthy and explainable AI. By leveraging knowledge graphs—a core strength of graph-enabled platforms like Arango—enterprises can ground Large Language Models (LLMs) in verified company data. This technique, known as Retrieval-Augmented Generation (RAG) and its more advanced variant, GraphRAG, helps mitigate the risk of AI “hallucinations” by forcing the model to retrieve answers from a curated knowledge base. Furthermore, the graph structure allows the AI to show the “path to the answer,” providing a transparent and auditable reasoning process.

This unification also yields significant operational benefits. By consolidating the data stack, enterprises can enhance developer agility, reduce the overhead of managing multiple systems, and improve data governance. Instead of spending resources on complex data integration pipelines, teams can focus on innovation and building value-adding AI applications.

Strategic Partnerships and Market Positioning

As the MMDP market matures, vendor strategy and ecosystem partnerships are becoming key differentiators. Arango has cultivated a particularly strong relationship with NVIDIA, becoming a member of its Inception Program. The company’s platform is the only graph database natively integrated for GPU acceleration through NVIDIA’s cuGraph library, a collaboration that dramatically boosts performance for large-scale graph analytics and complex AI workloads.

This partnership has produced tangible solutions, such as a Video Search & Summarization application that combines graph, vector, and document search to generate explainable intelligence from video streams. This deep technical integration validates the platform’s performance credentials for demanding, real-time AI use cases.

Beyond its work with NVIDIA, Arango’s inclusion in the AWS ISV Accelerate Program signals a strong cloud strategy, making its platform more accessible to organizations building on Amazon Web Services. With a client roster that includes the London Stock Exchange, Siemens, the U.S. Air Force, and HPE, the company has demonstrated its ability to power mission-critical, enterprise-grade AI solutions. This combination of a forward-looking data architecture, strong market validation from analysts like Forrester, and strategic technology partnerships positions such platforms as a foundational element for the next wave of enterprise innovation.