Future-proof your data foundation
Your data is everywhere.
Why isn’t it working together?
AI projects start with vector search, then bolt on databases for relationships, records and full-text search. Arango grows from vectors into graph, document and search on the same foundation. No rebuild.
Proven in 200+ production environments worldwide
One engine. Multiple data models.
Built for applications and AI.
Your next use case should not require another system.
Specialized stack
6 systems. 15 connections.
Every system connects to the others — and each one you add multiplies the integrations to build, secure and maintain.
Arango Contextual Data Platform
One governed data foundation
- Graph
- Vector
- Document
- Key-value
- Search
One query engine · one governance model
1 engine. 0 new systems.
Support the next use case without starting over — no new pipeline, no replatforming decision.
Why teams consolidate
One foundation, already running in production.
200+
production environments, proven across consequential enterprise workloads.
One
multimodel engine for graph, vector, documents, key-value and search.
Anywhere
cloud, private cloud, on premises or air-gapped.
The growing-stack problem
Each new requirement adds another system to build and maintain.
Vector search may solve the first retrieval problem. The next application may also need to follow relationships, access structured records, search exact terms or act on changing operational data.
01
More integrations
Every specialized database adds another connection that must be built, tested, secured and maintained.
02
More copies of data
Moving information between systems creates more places for data to become stale, inconsistent or inaccessible.
03
More operational complexity
Teams must monitor, scale and govern a growing collection of systems before the application reaches production.
We knew a knowledge graph could improve the accuracy and explainability of our AI application, but building one manually would have taken months and required expertise we didn’t have. AutoGraph gave our existing team a practical way to discover domains, extract entities and relationships, and connect them back to the source content — creating a graph we could use in production.
— Head of AI/ML Engineering, Global Cloud Technology Company
One engine. Multiple data models.
Use the data model each question requires.
Arango combines the models enterprise applications and AI need, so teams can expand the use case without replacing the foundation underneath it.
Graph
Follow relationships
Understand connections, paths, dependencies and impact.
Vector
Find what is similar
Retrieve semantically relevant content and concepts.
Document
Work with flexible records
Store rich application and enterprise data without rigid tables.
Key-Value
Access operational state
Retrieve fast-changing values required by applications.
Search
Find exact information
Use full-text and exact-match retrieval when precision matters.
Build for what comes next.
Vector search is often the start — not the entire data foundation.
The issue is not that vector stops being useful. New questions introduce additional requirements. Arango lets teams add those capabilities without turning each use case into another replatforming decision.
One team. One dataset. Four questions in a row.
Systems added0
- 01
“Find past reports similar to this defect.”
Find relevant information — semantic similarity and vector retrieval.
- Vector
- 02
“What else does this defect affect?”
Understand how it connects — relationships, paths and dependencies.
- Vector
- Graph
- 03
“What is the current state of what it touches?”
Include operational records — structured, document and key-value data.
- Vector
- Graph
- Document
- Key-value
- 04
“Where exactly does this error string appear?”
Search precisely — full-text and exact-match retrieval.
- Vector
- Graph
- Document
- Key-value
- Search
Four questions. One engine.
Each answer needed another data model — none of them needed another system.
What changes
Grow the use case — not the number of systems.
Specialized Stack
- Add a database for each new data model
- Build pipelines to move and synchronize data
- Query each system with different tools
- Operate and govern the systems separately
Arango
- Use multiple data models on one engine
- Keep related data together
- Query across models with one language
- Apply enterprise operations and governance consistently
Where the next use case leads
Different questions.
The same data foundation.
Engineering
What will this defect affect?
Combine bug records, product relationships, documents and release history.
Security
Which risk can reach production?
Connect vulnerabilities, identities, assets, events and threat indicators.
Customer Support
What applies to this environment?
Bring together customer records, configurations, cases, documents and known issues.
Life Sciences
Which evidence supports the decision?
Connect research, investigators, trials, institutions and source documents.
Intelligence
What supports this assessment?
Combine entities, events, claims, sources and changing operational state.
Infrastructure
What caused the failure?
Relate assets, telemetry, service records, dependencies and configuration changes.
Arango contextual data platform
A reusable foundation for applications and AI.
Bring the source data, relationships, semantic similarity, retrieval and enterprise operations together. Start with the requirement you have today and expand without redesigning the data foundation for every application that follows.
Graph + vector together
Relationships and similarity answered by one engine.
Documents + search
Rich records with full-text and exact-match retrieval.
Key-value access
Fast-changing operational state, read directly.
One query language
Query across every model without switching tools.
Transactional consistency
Related data stays correct as it changes.
Provenance + permissions
Source traceability and existing access rules carried through.
Enterprise scale
Proven in consequential production workloads.
Flexible deployment
Cloud, private cloud, on premises or air-gapped.
Complimentary working session
Is your data foundation ready for the next use case?
In an AI Data Architecture Workshop, Arango experts will help you map one priority application across its data, retrieval and production requirements — and identify where a unified platform could reduce complexity.
What you’ll leave with