ArangoDB

Why Graph?

Because connected data is how the real world works.

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Data is about relationships

Your business context isn’t flat, and yet we store data as rows and columns. Customers link to accounts, devices connect to networks, and suppliers impact supply chains. A graph database models those relationships natively, revealing patterns and context that other traditional databases overlook.

Graph powers
intelligence

Graphs don’t just store data. They show how everything is connected.

ArangoDB’s multi-model approach provided the answer to addressing both ease-of-deployment and the different data storage paradigms we need.

— Ross Mills, VP of Engineering

Build 360° views

of customers, patients, or assets across silos.

Optimize operations

by tracing dependencies in networks or supply chains.

Uncover fraud

map suspicious connections across accounts and devices.

Ground AI in context

so LLMs deliver accurate, trusted answers.

Graph and AI

Large language models hallucinate when they don’t have context. Graphs provide that missing context.

Knowledge graphs unify enterprise knowledge so co-pilots and chatbots can generate accurate results.

GraphRAG combines retrieval-augmented generation with graph queries for deeper, explainable AI.

Graphs let AI move beyond recall — inferring insights and explaining its reasoning.

Why Arango
for graph

Most graph databases stop at relationships.

Real-world problems don’t. You also need documents, vectors, and search alongside graphs.

With Arango, you don’t bolt databases together.

Native multi-model database unifying graph, document, key-value, vector, and search in one platform.

Single query language (AQL) to access and combine all data models.

Future-ready for AI with a governed data foundation that scales enterprise workloads.

Build what’s next

360° customer views. Fraud detection. Network optimization. AI co-pilots.

A graph database gives your applications the context they need to move from prototypes to production with speed, accuracy, and confidence.

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Frequently Asked Questions

Graph databases store relationships as first-class citizens. Instead of joins or nested queries, you can traverse connections natively. This makes it easier to analyze patterns across complex, connected data patterns with exceptional speed and scale

LLMs hallucinate when they lack context. Knowledge graphs provide structured, connected enterprise knowledge that grounds AI responses in facts.

GraphRAG (Graph Retrieval-Augmented Generation) combines graph queries with LLMs. It provides models with richer context and explainability than text-only RAG, leading to more accurate and trustworthy answers.

Most problems involve more than relationships. You also need documents, vectors, and search. Arango is a native multi-model database that combines all of these in one engine, so you don’t stitch multiple tools together.

Fraud detection, customer 360, supply chain optimization, network management, and healthcare all rely on connected data. Graph is the best way to model these real-world relationships.