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Introducing the Arango Contextual Data Platform

The fastest path to production AI agents—without the Frankenstack.

One governed platform that automatically turns your enterprise data into the contextual data layer your agents need to reason and act.

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70%

less infrastructure to maintain

20+

AI services built in

Billions

of connected records at scale

Proven in 200+ production environments worldwide

  • Articul8
  • Cloudera
  • Cloud Imperium
  • Cycode
  • Dun and Bradstreet
  • Deloitte
  • Emerson
  • ESRI
  • French Defense Ministry
  • HPE
  • IC Manage
  • Johnson Controls
  • Johns Hopkins
  • Linx
  • Kaseware
  • London Stock Exchange
  • NIH
  • Mercedes Benz
  • Orange
  • NVIDIA
  • Stockbit
  • US Air Force
  • Telstra
  • US Federal Government

There’s a simpler way

Five systems. Twenty-three integration steps. One platform replaces them all.

Most teams building enterprise AI end up with a Frankenstack. A vector store bolted to a graph database bolted to a search index, held together by custom retrieval pipelines and a governance layer added as an afterthought.

93%

of business leaders say insights need business context

The Frankenstack

Separate vector store
Pinecone, Weaviate, or Qdrant bolted onto a database not built for it

Graph database bolt-on
Neo4j or Tigergraph without a shared query language

Separate search index
Elasticsearch or OpenSearch maintained independently

Custom RAG pipelines
LangChain or LlamaIndex stitching, brittle at scale

Governance bolted on
Access control, data privacy, and security  as afterthoughts

Arango Contextual Data Platform

Vector built-in
No separate vector database needed.

Graph built-in
Traverse relationships natively without a separate graph database.

ArangoSearch built in
Full-text search natively integrated. No extra cluster.

AutoRAG built in
GraphRAG, HybridRAG, VectorRAG selected automatically.

Governance built in
RBAC, lineage, observability from day one.

What’s new in version 4.0

Arango Contextual Data Platform.
Five new capabilities.

Arango Contextual Data Platform 4.0 adds natural language interaction, automatic graph building, intelligent retrieval, and visual exploration — delivering the context AI needs to reason, the trust your enterprise demands, and the scale to run it all in production.

Ada

AI digital assistant for natural language development

AQLizer

Natural language to optimized AQL queries

Graph Visualizer

See and explore contextual relationships visually

AutoGraph

Automatically build contextual knowledge graphs

AutoRAG

Intelligent retrieval across graph, vector, and document data

How it works

From raw data to production agents.
Automatically.

Data flows in from any source. AutoGraph builds the knowledge graph. AutoRAG picks the right retrieval strategy. Your agents get the business context that’s connected, governed, and traceable.

Multi-Model
Data Sources
Relational
Databases
Document
Stores
NoSQL
Databases
Data Platforms,
Warehouses
& Lakes
Enterprise
Applications
Enterprise
Content
Management
Files &
Unstructured
Data
Arango Contextual Data Platform
Arango Agentic AI SuiteContextual Data for AI
Auto Orchestration
Auto
Ingestion
Layer
AutoGraph
AutoRAG
Auto
Retrievers
Auto Optimization
Contextual Data Layer
Contextual Operations
(Platform Suite)
Contextual Data Foundation
(ArangoDB)
Query
MCP
API
NLP
AQL
Consumers
AI Agents, Assistants & Apps
Humans
Prompt Template
LLM

Contextual Data for AI

Agentic AI Suite

20+ AI services that turn raw data into production-ready context including AutoGraph, AutoRAG, ContextRAG.

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Contextual Operations

Platform Suite

Enterprise operations: security, governance, scaling, observability, and deployment tooling.

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Contextual Data Foundation

ArangoDB

Graph, vector, document, key-value, and search in one system, one query language .

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New in 4.0

Arango Agentic AI Suite

AutoGraph

AutoRAG

ContextRAG

Governance

+ more...

The Automation Layer

Each component handles a different stage of the pipeline. Data comes in, context gets built, the right retrieval strategy gets selected, and your agents connect to it all.

01

AutoGraph

Knowledge modeling

AutoGraph, Arango's automated knowledge graph builder, ingests your enterprise data and discovers entity relationships without manual ontology design. Drop in millions of documents—AutoGraph builds the retrieval-ready structure your agents need.

02

AutoRAG

Retrieval strategy

AutoRAG, Arango's adaptive retrieval layer, selects the right retrieval strategy for every query at runtime — GraphRAG, HybridRAG, or VectorRAG. Deep Search for multi-hop queries. No manual pipeline configuration.

03

ContextRAG

Knowledge organization

Organizes enterprise knowledge into a corpus graph and delivers the right context at the right time. Structured and unstructured data, unified. Hybrid retrieval that turns fragmented data into trusted context.

Built into every query

Your agents don't just retrieve data. They rank it, adapt to it, and prove where it came from.

Multimodal Retrieval

Retrieves signals across documents, embeddings, structured records, and graph relationships in a single pipeline.

Relationship Traversal

Navigates multi-hop connections between entities, events, and systems that vector search alone cannot reach.

Evidence Ranking

Evaluates and ranks sources by relevance, relationships, and provenance for grounded, explainable responses.

Query Adaptation

Continuously refines retrieval paths and reasoning steps as new signals emerge, improving accuracy over time.

Governance

Every query governed from the start. Not bolted on after.

Provenance

Every answer traceable to its source

Compliance

Full audit trail, every query logged

Temporal

Query data as it existed at any point in time

Security

Row-level access control at query time

Vector finds similar.
Graph finds what’s actually connected.

Standard RAG retrieves documents by semantic similarity. GraphRAG traverses entity relationships—multi-hop paths and business context that vector search alone can’t surface. AutoRAG selects between GraphRAG, HybridRAG, and VectorRAG at query time.

What GraphRAG delivers

  • Traverses entity relationships vector search can’t see
  • Multi-hop reasoning across documents, graphs, and operational signals
  • Every answer traceable—explainable, auditable, governed
  • Graph + vector in one query.

Context changes the outcome.

Every use case below has the same root problem: fragmented data across systems, no understanding of relationships, and unexplainable decisions. Arango solves all three.

Customer Support

Agents need to connect customer records, product history, known issues, and resolution policies across systems.

With Arango: Relationship-aware retrieval traces a customer issue across ticket history, product logs, known defects, and policy rules—in a single query.

Fraud Detection

Agents need to trace relationships across accounts, devices, identities, and events in real time, with full auditability.

With Arango: Graph traversal surfaces fraud rings and anomaly patterns that vector-only retrieval misses. RBAC ensures every query respects access policy.

Knowledge Assistants

Agents need to know what your organization knows—how concepts relate across teams, systems, and time.

With Arango: AQLizer translates natural language into queries spanning structured and unstructured data. Answers from the full picture.

AIOps & Root Cause

Agents need to correlate alerts, trace incidents across infrastructure dependencies, and recommend resolution paths in real time.

With Arango: Logs, topology, alerts, and change history queried together in one platform. Graph traversal traces a degraded service back through every dependency to the root cause.

Engineering AI

Agents need access to code, architecture decisions, runbooks, and engineering context with enough relationship awareness to give applicable answers.

With Arango: Deep Search traces dependencies across codebases, docs, and incident history to surface context semantic search would miss.

Infrastructure Graph

Agents need a live, connected view of infrastructure—assets, dependencies, alerts, and change history.

With Arango: A unified graph of infrastructure topology lets agents trace a degraded service back to a configuration change three hops away.

New in 4.0

Bring your LLM. Bring your framework.Arango connects to all of it.

LLM + Agentic Framework Integrations

OpenAI

Anthropic

Mistral

LiteLLM

LangChain

LlamaIndex

NVIDIA Triton

Any LLM

Driver Support

Python

JavaScript

Java

Go

.NET

Spring

Deployment options

Self-service cloud

VPC

On-premises

Air-gapped

Stop building Frankenstacks.
Start building with Arango.

Talk to our team and see how Arango gives your AI agents, assistants, and apps the trusted data foundation they need to work in production.

Book a Demo

Frequently Asked Questions

Arango AutoGraph™ is Arango's automated knowledge graph builder. It ingests enterprise data — structured, semi-structured, and unstructured — and discovers entity relationships without requiring manual ontology design. The result is a governed, retrieval-ready knowledge graph that AI agents, assistants, and applications can query immediately.

Arango AutoRAG™ is Arango's adaptive retrieval layer. It evaluates each query at runtime and selects the best retrieval strategy — GraphRAG for relationship traversal, HybridRAG for semantic plus structured retrieval, VectorRAG for embedding similarity, or Deep Search for multi-hop queries.

VectorRAG retrieves documents based on embedding similarity. GraphRAG traverses entity relationships in a knowledge graph — multi-hop connections that vector search cannot surface. Arango's AutoRAG selects between the two (and HybridRAG) automatically based on query type.

No. Arango integrates with LangChain, LlamaIndex, and other frameworks. It provides the governed data foundation and retrieval layer — AutoGraph, AutoRAG, and Agent Runtime — that sits underneath your LLM and orchestration framework. It replaces the Frankenstack of separate databases, not your AI framework.

Arango AQLizer translates natural language queries into AQL (ArangoDB Query Language). It allows AI agents, assistants, and applications to query graph, vector, and document data without requiring users or developers to write AQL directly. It helps simplify development and optimize query performance, making it easier to access and reason over connected enterprise data.

Arango includes built-in RBAC (role-based access control), audit trails, provenance tracking, and query logging. Every answer produced by an AI agent, assistant, or application can be traced back to its source data. Governance is built into the data layer, not added on top after the fact. It supports explainability and decision traceability requirements of the EU AI Act.

Arango works with any LLM. It has integrations for OpenAI, Anthropic, Mistral, LiteLLM, NVIDIA Triton, LangChain, and LlamaIndex. Because it operates as the data and retrieval layer, the choice of LLM stays entirely with the developer.