Arango for Enterprise AI
Agentic AI Runs
on Business Context
LLMs generate answers. Without knowing how your data connects and whether it can be trusted, those answers aren’t safe to act on. Arango builds the foundation your agents actually need.
The Context Gap
AI Needs Context
Without shared business context, AI retrieves fragments. With it, agents reason across your entire data foundation — accurately, consistently, and without guessing.
Your model isn’t the problem.
Your data foundation is.
Most enterprise AI projects don’t fail because the model is wrong. They fail because the data underneath it is fragmented, disconnected, and ungoverned. LLMs can generate answers.
They can’t manufacture context that doesn’t exist.
AI insights are only relevant when grounded in unified, current, and trusted business context.
93%
of business leaders agree context is the missing layer
Without a Contextual Data Layer
What does this data actually mean in our business?
How are these entities related to each other?
Was this information current when the decision was made?
Where did this answer come from, and can we audit it?
With the Arango Contextual Data Platform
What things mean
Structured and unstructured data unified in a single, queryable foundation.
How they relate
Graph-native relationship modeling connects entities across systems. No ETL. No data sync.
When they were true
Time-aware data structures keep context current as your business changes.
Where they came from
Built-in lineage and governance. You can trace every answer back to the source.
Arango Agentic AI Suite
From fragmented data to a Contextual Data Layer — Automatically.
Most teams assemble context themselves and end up with a Frankenstack: vector search bolted to graph, bolted to search indexing, bolted to pipelines. Every new use case adds more wiring. Arango replaces all of it.
01
AutoGraph
Knowledge modeling layer
AutoGraph transforms your enterprise data into a governed knowledge graph. It discovers relationships, generates ontologies, and creates RAG partitions. Your agents start with structure, not scattered documents.
Capabilities
- Context creation
- Relationship discovery
- Knowledge graphs
- Semantic structure
- Context modeling
02
AutoRAG
Retrieval pipeline layer
AutoRAG picks the right retrieval strategy for every query. GraphRAG for relationship traversal. HybridRAG for semantic and structured retrieval. Deep Search for multi-hop queries across any data source.
Capabilities
- GraphRAG
- HybridRAG
- Semantic retrieval
- Relationship traversal
03
Contextual Data Access
Execution layer
Connect to your existing data platforms, LLMs, and MLOps pipelines via MCP tools, APIs, and native drivers. Not a replacement. A trusted data foundation that works with what you already have.
Capabilities
- Unified access interfaces
- Multi-consumer support
- Context-aware query execution
- LLM and agent integration
- Secure and governed access
Fully Integrated
AutoGraph, AutoRAG, and the Contextual Data Access Layer run on the same unified foundation — ArangoDB’s native multimodel platform. No syncing between layers. No duplicated governance. One system from ingestion to execution.
Data Sources
Databases
Stores
Databases
Warehouses
& Lakes
Applications
Content
Management
Unstructured
Data
Ingestion
Layer
Retrievers
The Arango Agentic AI Suite runs as a continuous system — AutoGraph models your enterprise knowledge and AutoRAG pulls the right context at the right moment. Data moves across all three layers without interruption, so your agents always have what they need to act with confidence.
Key Capabilities
What the Arango Agentic AI Suite does and how each layer works.
Five capabilities that span ingestion, modeling, retrieval, routing, and integration — built into the platform, not bolted on.
Automated Data Ingestion
Connect structured and unstructured data sources without building custom pipelines. Data flows in. Context builds automatically. No manual schema mapping. No one-off connectors.
- Structured and unstructured sources — databases, documents, APIs, and file stores handled natively.
- No custom pipelines — AutoGraph handles ingestion logic so engineering teams stay focused on product.
- Continuous context building — as new data arrives, the knowledge graph updates automatically.
AutoGraph Knowledge Modeling
AutoGraph generates ontologies and knowledge graph partitions automatically — at scale, across your full data estate. Drop in a million documents. AutoGraph discovers relationships and builds the structure AI agents need to reason from.
- Automated ontology generation — no data scientists required to define schemas by hand.
- Relationship discovery at scale — surfaces connections across systems that manual modeling would miss.
- GraphRAG partition management — creates the retrieval-ready structure AutoRAG queries at runtime.
Hybrid Retrieval Pipelines
Combine GraphRAG, VectorRAG, and HybridRAG in a single retrieval pipeline. No separate systems. No retrieval gaps. One query spans relationship traversal, semantic search, and structured filtering simultaneously.
- GraphRAG — traverses entity relationships for context that vector search can't surface.
- VectorRAG — semantic retrieval across unstructured content using vector embeddings.
- HybridRAG — combines both strategies in a single pipeline for queries that need both.
Adaptive Retrieval Strategies
Deep Search selects the right retrieval strategy per query — local, global, unified, or custom — routed by ToolGraph at runtime. No hardcoded pipelines. No retrieval logic to maintain by hand.
- ToolGraph routing — selects the retrieval strategy that fits each query automatically.
- Local and global retrieval — narrow entity queries and broad semantic queries handled by the same system.
- Custom strategies — define your own retrieval logic when standard patterns don't fit your data.
Contextual Data Layer Integration
Connect to your existing data platforms, LLMs, and MLOps pipelines via MCP tools, APIs, and native drivers. The Contextual Data Layer fits into the stack you already have. Extend without replacing.
- MCP tools — ArangoDB MCP and AutoGraph MCPs connect agents directly to your data foundation.
- LLM integrations — works with OpenAI, Anthropic, Mistral, and any model you already use.
- Any deployment model — self-service cloud, VPC, on-premises, or air-gapped.
Works with your existing data platforms, LLMs, and MLOps pipelines. Not a replacement. A trusted data foundation.
Deployment options
Self-service cloud
VPC
On-premeses
Air-gapped
Secure and observable across every deployment model.
Ready to eliminate the Frankenstack?
Give your AI the context it needs.
Context changes what's possible.
Give your AI agents, assistants and apps the business context they've been missing and see the impact they can have .
Support Engineering
Support Engineering
Customer Support AI Agents
Agents need to connect customer records, product history, known issues, and resolution policies across systems before they can give a useful answer. Without that connection, they surface fragments and escalate what they should resolve.
Suite capabilities
AutoGraph
GraphRAG
ContextRAG
Why it works
Relationship-aware retrieval traces a customer issue across every relevant system — ticket history, product logs, known defects, policy rules — in a single query.
Knowledge Management
Knowledge Management
Enterprise Knowledge Assistants
Agents need to know what your organization knows — not just what's in a document, but how concepts relate across teams, systems, and time. Static document search returns text. Contextual retrieval returns meaning.
Suite capabilities
Multimodel retrieval
AQLizer
Agent
Why it works
AQLizer translates natural language into queries that span structured and unstructured data, so knowledge assistants answer from the full picture, not the nearest chunk.
Developer Productivity
Developer Productivity
Product Engineering Agents
Agents need access to code, architecture decisions, runbooks, and engineering context — with enough relationship awareness to give answers that are actually applicable to the system in front of them.
Suite capabilities
MCP tools
ContextRAG
Deep Search
Why it works
Deep Search traces dependencies across codebases, docs, and incident history to surface engineering context that a simple semantic search would miss.
Risk and Security
Risk and Security
Fraud, Risk, and Compliance AI
Agents need to trace relationships across accounts, devices, identities, and events in real time, with full auditability and policy enforcement built in, not added after.
Suite capabilities
Graph Analytics
GraphML embeddings
RBAC governance
Why it works
Graph traversal across entity relationships surfaces fraud rings and anomaly patterns that vector-only retrieval misses entirely. RBAC ensures every query respects access policy.
Operations
Operations
Network, Asset, and Infrastructure Operations
Agents need a live, connected view of your infrastructure — assets, dependencies, alerts, and change history — to diagnose issues and recommend action without sending engineers on a manual search.
Suite capabilities
Agentic workflows
AQLizer
Graph Analytics
Why it works
A unified graph of your infrastructure topology means agents can trace a degraded service back to a configuration change three hops away and explain the path.
See it In Action
AutoGraph builds AI-ready knowledge graphs. Automatically.
See how AutoGraph automatically turns your enterprise data into a knowledge graph, so AI agents can reason across relationships, not just retrieve documents.
Enterprise AI Outcomes
What changes when your agents have context.
When AI agents run on a unified Contextual Data Layer, every decision is accurate, explainable and traceable.
Explainable AI Decisions
Agents grounded in a unified Contextual Data Layer produce decisions that are explainable.
Faster Time to Production
AutoGraph, AutoRAG, and pre-built MCP integrations cut the time to build, manage and operate a reliable AI data architecture.
Traceable Decisions
Graph-native lineage means you can trace every decision back to the source. Auditable end to end.
Simplified Data Architecture
One platform replaces the the need to glue together graph, vector, document, key-value, and search. Less to build, less to maintain.
Enterprise-Grade Infrastructure
HA/DR, RBAC, elastic scaling, and deployment flexibility built into the platform, not bolted on afterward.
Production-ready agents
run on trusted context.
Explore the architecture behind Arango Contextual Data Platform to see how it fits into your AI stack.