Why Arango
Context is not an afterthought.
It’s the architecture.
Context that gets rebuilt for every project cannot be governed or trusted. Arango makes it something your agents inherit instead of assembling.
Enterprise AI is failing at the architecture layer
MIT found that 95% of enterprise GenAI pilots return nothing measurable.1 The models are not the constraint. The data underneath them is. One use case works in a pilot. The same architecture buckles when it has to carry a hundred AI agents in production.
1MIT Project NANDA, “The GenAI Divide: State of AI in Business 2025,” July 2025. Based on 52 executive interviews, surveys of 153 leaders, and analysis of 300 public AI deployments.
95%
of enterprise GenAI pilots return nothing measurable.
Your stack assumes
a person is watching
And they were. Someone ran the report, saw the number was wrong, and fixed it before anything happened. That person was the contextual data layer, which is why the systems underneath could stay fragmented. Agents remove the assumption.
Reads
A person opens a handful of records. An AI agent traverses thousands of entities in one reasoning step.
Conflicting data
A person knows which record is current. An AI agent picks one and acts.
Query pattern
A person queries occasionally. An AI agent queries continuously, in loops, across concurrent sessions.
Explainability
A person reconstructs their reasoning afterward. An AI agent can only do that if the sources were machine readable to begin with.
Nobody is left in the middle to catch the difference.
This is a systems problem, not a tools problem
The common response is to add tools. A vector database for semantic search. A graph database for relationships. An orchestration framework, a reranker, and pipelines to keep it all aligned. Each one solves a slice. Together they create a distributed system that no single team owns and every new application has to assemble again.
The failure modes are predictable.
Dual writes
One store commits, the other does not. Two systems now disagree about the same customer.
Embedding drift
The document changed. The embedding did not. Retrieval returns what used to be true.
Resync skew
The same question, asked forty seconds apart, comes back with a different answer.
Governance above the data
Entitlements live in the application, so an AI agent traversing relationships can walk past them.
Schema propagation.
One source change reaches every retrieval path you have built.
Matillion found that 64% of data teams spend more than half their time on repetitive manual work.2 Most of it goes into maintaining the connections between tools, which is work no customer ever sees.
2Matillion, “Data Integration and AI-Readiness Report,” 2025.
64%
of data teams spend more than half their time on repetitive manual work.
Connections belong in the data platform, not in the pipelines
Arango holds graph, vector, document, key-value, and full-text search in one data platform, with one query language and no synchronization layer between separate specialized stores.
That decision was the founding idea for Arango. In 2014, connected data meant running three databases, because no single system handled graphs, documents, and key-value pairs well. Most teams ran all three and wrote pipelines to reconcile them. We built one instead.
One question. Two paths.
Which open disputes involve this customer, and which policy governs them?
On Arango:
One request
The traversal, the similarity ranking, and filtering resolve in the same transactional foundation, and the result carries its own lineage.
On a stitched stack:
Seven requests
- Query the system of record
- Query the graph database
- Query the vector store
- Rerank in application code
- Join the results in application code
- Reconcile three consistency models
- Reconstruct the path for the audit log, because no single system saw the full traversal
The difference compounds with every agent loop:
more round trips, more context assembled, more tokens spent carrying it.
12 years of connected data in production
Arango began in Cologne in 2014. The question was not how to store enterprise data, which was already well solved. It was how to represent what connects it.
Storage kept getting cheaper. Understanding kept getting harder.
That architecture went into production where relationships were the whole problem: fraud detection, cybersecurity, digital twins, supply chain, network and asset management, investigations. More than 200 organizations run it today, including HPE, PSI, Zscaler, and the U.S. Air Force.
Nobody called it a contextual data layer then. It is what it was, and it is what agentic AI now depends on. Twelve years of commits, releases, and documentation are public, so the architecture can be read in the order we built it.
Business context built once, used everywhere
Most agentic workflows rebuild business context on every run, with a new pipeline, graph, and index for each use case. The Arango Contextual Data Platform maintains it as shared infrastructure instead.
AutoGraph
Automatically builds and maintains the graph from documents and unstructured data sources, so nobody hand-models a knowledge graph for every project.
Arango AutoRAG
Automatically adapts the retrieval strategy to each question across relationships, semantics, and documents, and returns lineage with every result.
ArangoDB
The data foundation. Graph, document, vector, key-value, and search in one graph-native platform.
Retrieval finds it.
Trusting it is a different problem.
Retrieval tells you where the information is. It doesn’t tell you how it fits together, or whether acting on it is safe.
Six things have to be true before an agent can act:
01
Semantic clarity
One customer, not five near-duplicates.
02
Explicit relationships
Across customers, contracts, assets, policies.
03
Freshness and temporal state
What is true now, and what was true at the moment it acted.
04
Provenance
Every result traces to a source.
05
AI-native retrieval
The right retrieval path for the question, with evidence attached.
06
Unified multimodel context.
Relationships, documents, semantics, and operational data resolved together.
And all of it has to stay continuously updated and governed at the platform layer, not in the application above it. Twelve years of production taught us that list. Not the last two.
The Contextual Data Layer for Enterprise AI
6 architectural requirements for building agentic-AI-ready systems
What Arango is Not
Not a data warehouse.
Snowflake, BigQuery, and Databricks handle analytical workloads well. Quarterly aggregation is not what this platform is for.
Not a BI layer.
Dashboards stay where they are.
Not a replacement for systems of record.
Arango runs alongside existing data infrastructure and brings the context it needs from it.
Unlike vendors that solve only one piece of the AI context problem—data storage, knowledge graphs, metadata, or retrieval—Arango unifies the entire contextual data lifecycle on one native platform.
What we own is the connected, governed, current business context an agent reasons over, sitting between the enterprise systems and the model.
If your AI workload is one model against one clean table, you do not need us.
The first ninety days
Arango deploys multi-cloud, self-managed, as a managed service, air-gapped, or embedded.
Weeks 1 to 3
Connect sources. AutoGraph builds the ontologies and the graph from documents and unstructured data sourcees. Nothing gets migrated.
Weeks 4 to 8
A first AI agent workload runs against the context layer in parallel with your existing retrieval path, so you can compare answer quality, latency, and lineage directly.
Weeks 9 to 12
Workloads move over as they earn it. The pipelines they replace retire at that point, not in advance.
Proof at Scale
More than 200 enterprises run on Arango, including NVIDIA, the U.S. Air Force, HPE, Zscaler, PSI, and Cloud Imperium Games. Arango was named a Strong Performer in The Forrester Wave: Multimodel Data Platforms, Q2 2026.
Where engineering effort belongs
A capable team can build a working AI use case in a few weeks. The architecture that ships the first one quickly is rarely the one that survives the hundredth, and early speed is usually borrowed against later scale.
Your advantage is your proprietary data. Your customers, your fraud patterns, your supply chain, whatever your team knows that nobody else does. It is almost never knowledge graph modeling, index synchronization, retrieval tuning, or entity resolution, and those are general problems a platform should solve once.
We have been solving them since 2014.
Bring us your hardest question.
Tell us what your agents cannot answer yet, and we will show you what a shared context layer looks like across your AI initiatives.