Arango’s Contextual Data Layer sits between your enterprise data sources and your AI agents, apps, and LLMs. Fragmented data from dozens of systems flows in. The platform connects, understands, retrieves, governs, and persists it as a unified contextual data layer built on a graph-native multimodel data foundation.
What Arango’s contextual data layer delivers in production.
Explainable Answers
Agents grounded in a unified live contextual data layer produce outcomes that are explainable.
Faster Time to Production
AutoGraph, Auto Ingest and Retrieval, and pre-built MCP integrations cut the time to build and operate a reliable, scalable 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 need to glue together graph, vector, document, key-value, and search. Less to build, less to maintain.
Enterprise-Grade
HA/DR, RBAC, elastic scaling, and deployment flexibility built into the platform, not bolted on afterward.
Massive Scale. Made Easy.
Horizontal scale across graph, vector, document, key-value, and search. No rebuilds required.
PROVEN IN PRODUCTION
Performance, efficiency, andtrust.
2000x
Faster workloads
Query, index, and analyze enterprise data in real-time. Arango’s unified architecture eliminates the overhead of moving data between systems.
70%
Simpler Stack
Replace dozens of bolted-together components with one governed platform. Less infrastructure to build, maintain, and debug.
Arango is our AI data platform of choice because it delivers performance, scalability, and flexibility that others can’t. As our knowledge graphs grow past 100k+ nodes and edges, Arango scaled effortlessly.
Other platforms give you building blocks. Arango automates the hardest parts.
A Frankenstack makes your team do all the work: build the graph, tune the retrieval, write queries across multiple systems. Arango automates the three hardest parts.
Most knowledge graph projects spend months on ontology design before anything ships. Arango AutoGraph ingests your enterprise data and automatically builds a governed knowledge graph: the foundation of your live contextual data layer. Entities extracted. Relationships resolved. Schema maintained as your data changes. Production in weeks, not months.
AutoGraph ingests structured, semi-structured, and unstructured enterprise data and automatically builds a governed knowledge graph. Entities extracted. Relationships resolved. Schema maintained as your data changes.
Data that’s retrieval-ready before the first query
Most platforms store your data and hope retrieval sorts it out. It doesn’t. Arango determines the optimal processing depth for each data domain at ingestion: entity extraction, chunking, partitioning. Your contextual data layer is structured for accurate retrieval the moment data lands. No chunk-size experiments. No embedding model roulette. Quality is decided at ingestion, not at query time.
Arango analyzes the complexity of incoming data and selects the right ingestion strategy automatically. Complex data gets full entity extraction, relationship mapping, and ontology generation. Simpler data gets chunked, embedded, and indexed. Every data domain is retrieval-ready on the way in.
The right retrieval strategy for every query, chosen automatically
Most RAG setups pick one retrieval strategy and hope it works for every question. Arango Deep Search evaluates each query against your contextual data layer at runtime and selects the best approach: GraphRAG for multi-hop reasoning across your knowledge graph, VectorRAG for semantic search across embeddings, or both. Your context layer does the work. No retrieval pipelines to build or maintain.
Arango Deep Search evaluates each query at runtime and routes to the right retrieval strategy: graph traversal for multi-hop relationship walks, vector search for embedding similarity, or document lookup for full-text retrieval. Results are merged, ranked, and filtered into one contextual answer.
Ask in plain language, query across every data model
Arango AQLizer lets teams ask natural language questions against your contextual data layer and automatically generates optimized queries. Explore relationships across entities and systems, investigate operational patterns without writing complex queries. Natural language to graph-native querying, with the full analytical power of ArangoDB.
AQLizer translates natural language questions into optimized AQL queries across every data model: graph, vector, document, and key-value. One query language. One platform. No context switching between systems.
FOR DEVELOPERS
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Everything you need to go from zero to production.
Explore AutoGraph
Automatically build a knowledge graph with your own data.
