ArangoDB offers a graph‑native Contextual Data Platform that unifies graph, vector, document, key‑value, and search workloads in a single multimodel database, providing a persistent, queryable context layer for AI agents and applications. Built‑in tools such as AutoGraph and AutoRAG, along with enterprise features like high‑availability, RBAC, and elastic scaling, accelerate the creation of reliable, explainable AI pipelines and reduce time‑to‑production.
Funding
$27.8M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
2OFounders
Product
Problem
AI agents, assistants, and applications often fail to reach production because they lack unified, trusted business context, leading to unreliable decisions and untraceable outcomes. This stems from fragmented data architectures that bolt together separate graph, vector, document, key‑value, and search systems.
Solution
Arango provides a graph‑native Contextual Data Platform that unifies graph, vector, document, key‑value, and search workloads in a single multimodel database. The platform delivers a persistent contextual data layer that agents can query in real time, enabling accurate reasoning, explainable decisions, and end‑to‑end lineage. Built‑in features such as AutoGraph, AutoRAG, and pre‑configured integrations accelerate the creation and operation of AI data pipelines, reducing time‑to‑production. Enterprise‑grade capabilities—including HA/DR, RBAC, elastic scaling, and native governance—ensure that AI workloads remain secure, auditable, and scalable across large organizations.
Target Audience
Primary customers are enterprise teams building agentic AI systems—data engineers, ML engineers, and developers who need a governed, scalable data foundation for intelligent applications.
Features
- Multimodel native architecture that stores and queries graph, vector, document, key‑value, and full‑text data with a single query language (AQL)
- Graph‑native lineage provides automatic traceability of every AI decision back to its source data
- AutoGraph and AutoRAG tools generate context‑aware retrieval pipelines and graph‑based reasoning without custom code
- Integrated enterprise features: high‑availability, disaster recovery, role‑based access control, and elastic horizontal scaling
- Unified API and drivers for all major programming languages, enabling rapid development of agents and assistants
- Built‑in analytics and machine‑learning extensions (GraphML, Graph Analytics) for advanced insight generation