Solution Brief
Arango for Partners
The Contextual Data Layer for Enterprise AI
Company Overview
Arango in 60 Seconds
Who we are. Arango began in 2014 in Cologne, Germany, as ArangoDB (DB – database) when the founders set out to solve a problem every data engineering team runs into: juggling separate databases for graphs, documents, and key-value data because no single engine handled all three well. Rather than accept that tradeoff, they built ArangoDB from the ground up as a native multimodel database — one engine, one query language, instead of a stitched-together stack.
That founding decision to unify the data models instead of assembling them has defined every major step since. ArangoDB proved the architecture could scale at enterprise level, and it went on to run in production at organizations including HPE, PSI, US Air Force, Zscaler, and approximately 200 organizations.
What we do. The Arango Contextual Data Platform turns fragmented enterprise data into business context AI can act on. At its foundation, ArangoDB stores graph, document, key-value, vector, and full-text search data in one graph-native engine. Built on top, Arango AutoGraph and AutoRAG turn that connected data into a persistent, governed Contextual Data Layer — exactly what agentic AI needs to reason, decide, and act reliably at scale.
Why it matters. Enterprise teams choose Arango because it answers what a stitched-together stack cannot.
Are the AI answers grounded in real data, not guesses? Yes, every response is grounded in your own governed data.
Can we explain and audit how the AI got there? Yes, every answer traces back to its source, with full lineage built in.
Can we stop stitching five systems together to get there? Yes, graph, vector, document, key-value, and search all live in one platform, built in rather than bolted on.
Proof at scale. More than 200 enterprises now run on Arango, including NVIDIA, the U.S. Air Force, HPE, Zscaler, and Cloud Imperium Games — deployed multi-cloud, self-managed, as a managed SaaS, air-gapped, or embedded/OEM.
Strong Performer
Forrester Wave™, Q2 2026
4.6 / 5 on G2
115 verified reviews
~200
enterprises worldwide
Our Story
Over a Decade of Innovation
From a graph-first multimodel database to a Contextual Data Platform trusted by ~200 organizations, Arango’s evolution reflects a decade of solving one problem: helping enterprises understand connected data. Today, that same foundation enables contextual AI.
2014
Distributed & Dual-HQ
ArangoDB becomes a distributed system with fault tolerance built in. The company establishes headquarters in Cologne, Germany and San Francisco, USA.
2019
Search Joins the Engine
Integrated full-text search (ArangoSearch) is added. One engine now spans graph, document, key-value, and search.
2024
Vector Indexes Added
Native vector search joins the multi-model core, positioning ArangoDB for the AI and GraphRAG era.
2026
Arango 4.0 Contextual Data Platform
ArangoDB evolves into Arango: AutoGraph, Deep Search, and the Agentic AI Suite arrive, turning connected data into a governed Contextual Data Layer for enterprise AI.
Enterprise AI’s real obstacle isn’t data volume — it’s fragmentation. Enterprises are forced to stitch together separate vector stores, graph databases, and search indexes, leaving AI to piece relationships together at query time.
Arango’s Contextual Data Platform unifies graph, vector, document, key-value, and search into one system. Arango AutoGraph automatically discovers entities and relationships across structured and unstructured data to build and continuously update an enterprise context graph. AutoRAG analyzes that graph to generate domain-aware GraphRAG pipelines with optimized retrieval, scalable partitioning, and multimodal support.
These innovations turn fragmented data into unified, trusted context — powering AI that’s accurate, explainable, and production-ready.
The Problem We Solve
Built for Humans.
Now Built for Agents.
Salesforce, Snowflake, and BI dashboards were designed for people to read reports and decide. Now AI agents are the ones reading the data — reasoning, deciding, and acting on it directly. When the consumer of data changes, the architecture underneath it has to change too.
Built for Humans
- Dashboards, reports & BI
- Search & documents
- A person interprets the data
- Reads, then decides
Built for Agents
- Connected, machine-ready context
- Relationships, meaning & history
- The agent reasons over the data
- Reasons, decides — and acts
The question: as agents become the consumers of your data, is your architecture keeping pace?
The Simplified Architecture
Arango answers that question with one added layer, not a rebuild. You keep your existing compute, models, and applications — Arango adds a single Contextual Data Layer on top: one multimodel platform spanning graph, vector, document, key-value, and search, so agents reason over connected, governed context instead of disconnected systems stitched together with fragile pipelines.
| Stitched Stack | Arango |
|---|---|
| 4–6 separate databases to license & run | One platform, one engine to operate |
| ETL pipelines and constant syncing | No ETL, no sync — single source of truth |
| Duplicated data that drifts out of sync | Consistent data across every model |
| Multiple query languages & skill sets | One query language (AQL) & one team |
| Weeks to ship a cross-system feature | Ship cross-model features in days |
| AI projects stall on data plumbing | AI-ready context layer, built in |
Lower TCO
Faster time-to-value
Less operational risk
A foundation for AI
Platform Portfolio
Arango Contextual Data Platform
Arango delivers the contextual data foundation for enterprise AI transforming fragmented enterprise data into a persistent, governed contextual data layer that enables agentic AI to reason, decide, and act at scale.
With the graph-native ArangoDB as the data foundation, unifying graph, vector, document, and key-value — the Arango Contextual Data Platform gives developers one environment to build AI agents and applications without assembling a Frankenstack of separate systems.
Arango AutoGraph automatically builds knowledge graphs from enterprise data — discovering entities, mapping relationships, and setting the optimal ingestion and graph structure. No ontology design required.
Also included: AutoRAG for retrieval-ready ingestion, Deep Search for multi-retriever query intelligence, and a Context Harness Layer, which includes, AQLizer for natural language to AQL, Graph Visualizer, and Ada.
Context is persisted once and reused everywhere. That moves AI from pilot to production.
Trusted by NVIDIA, HPE, Siemens, Zscaler, and leading enterprises worldwide.
Integrated • Agentic AI Suite
With over 20 AI service embedded in the platform innovations such as Arango AutoGraph build connected context automatically at ingestion; Deep Search retrieves it at query time — turning connected data into a live Contextual Data Layer that AI agents and copilots reason over: accurate, explainable, and auditable.
In the enterprise AI stack, Arango owns the middle layer — Applications & Agents → Connected Data (Arango) → Models & Frameworks → Compute — making every layer above it trustworthy.
The Data Foundation • ArangoDB
One graph-native multimodel database — five data models, one engine.
Graph: Native property-graph traversals, shortest-path, pattern matching.
Document: Schema-flexible JSON — each record carries its own structure.
Key-Value: High-performance lookups for speed-critical access.
Vector: Native semantic search alongside every other model.
Full-Text Search: Integrated search & retrieval (ArangoSearch).
Technology
20+ Built-In AI Services
The Agentic AI Suite runs natively on the platform — no bolt-on services to license, host, or keep in sync.
Arango AutoGraph
Automatically discovers knowledge domains across enterprise data and builds a per-domain contextual knowledge graph, with AutoRAG assigning each domain the right processing depth.
GraphRAG
Turn-key graph-grounded retrieval-augmented generation — builds a knowledge graph from raw text and answers questions grounded in real relationships, reducing hallucinations.
Deep Search
Multi-step reasoning over the knowledge graph at query time — agents traverse relationships instead of matching the nearest text chunk.
GraphML & Graph Analytics
Link prediction, node classification, and embeddings for fraud, churn, and anomaly detection, plus built-in algorithms — PageRank, centrality, community detection — GPU-accelerated.
Governance
Lineage, RBAC, and audit logging built into the platform, so every answer is traceable and every access is controlled.
Ada + MCP / AQLizer
Ada is an AI assistant for building queries and exploring graphs; MCP connects agents directly to the platform; AQLizer turns natural language into AQL across every data model.
Use Cases
Where Customers Win With Arango
Repeatable plays that map to a clear budget owner and a services attach.
Agentic AI &
GraphRAG Agents
Ground LLM-powered agents and assistants in governed enterprise context — the flagship use case for nearly every AI initiative.
Fraud, Risk & Compliance
GraphML and graph analytics surface hidden rings, anomalies, and relationships traditional ML misses, traced in real time.
Customer 360
One connected view of a customer across every system of record, resolving the identity-fragmentation problem.
Cybersecurity &
Supply-Chain Risk
Connect entities, ownership, suppliers, and vulnerabilities into one graph to expose concealed risk.
Enterprise Knowledge
& Search
Turn wikis, drives, and doc repositories into a domain-aware knowledge base queryable in natural language.
Customer Support Automation
Production-grade retrieval over runbooks, account, and policy context for support agents at scale.
Public Sector,
Defense & Intelligence
Air-gapped and on-prem deployment, explainability and provenance for mission assurance.
Chip & Hardware Design
Trace dependencies across massive engineering graphs — the NVIDIA-class use case.
Network & Asset Intelligence
A live, connected view of infrastructure at carrier scale across millions of devices.
Service, Support & Training
Enablement That Gets You to Production
Training, hands-on technical support, and services resources for teams evaluating or building on Arango.
Training & Certification
Role-based curriculum covering graph data modeling, AQL, and multi-model design, plus a GraphRAG / Agentic AI Suite track leading to Arango Certified Engineer status. Arango Academy and full documentation are open to every team.
POC & Demo Support
Sandbox environments, reference architectures, ready-to-run demo scripts (AutoGraph and AutoRAG), and solutions-engineering assistance on strategic proofs of concept.
Solution Architecture
Design reviews, sizing guidance, and deployment patterns for on-prem, cloud, embedded, and air-gapped environments — including the Kubernetes Operator for automated deployment.
Technical Support & Escalation
Direct access to Arango engineering for blocking issues on active evaluations and deployments, backed by NFR / Arango Managed Platform developer licenses for labs and skills-building.
Market Recognition
Independently Recognized
Strong Performer
The Forrester Wave™: Multimodel Data Platforms, Q2 2026
“Arango is well-suited to organizations seeking a contextual data foundation where multihop graph performance and verifiable reasoning are mission-critical for trusted AI.”
— The Forrester Wave™: Multimodel Data Platforms, Q2 2026
Arango received the highest possible scores in the criteria of adoption and unified multimodel architecture.
4.6 / 5 on G2
115 Verified Reviews
Leader in the G2 Grid® and Momentum Grid® Reports for Graph Databases, with the highest Satisfaction Score in the category. Reviewers consistently cite the flexibility of Arango’s multi-model approach, the intuitive AQL query language, and responsive customer support.
Why the Market Is Moving Now
5.5×
Growth in search interest for context engineering & GraphRAG, Jan 2024 → Jun 2026
50%+
AI agent systems will use context graphs by 2028 (Gartner)
95%
Of enterprise AI pilots deliver zero ROI — root cause: lack of context (MIT/Snowflake)
Customer Validation
In Our Customers’ & Analysts’ Words
“Arango is our GenAI data platform of choice — performance, scalability, and flexibility that others can’t match.”
— Joe Eaton, Ph.D., Distinguished Engineer, NVIDIA
“We no longer worry about data infrastructure. Arango scales and performs when it matters, freeing us to focus on building features.”
— Cloud Imperium Games, CTO, Star Citizen
“Our AI agent doesn’t just recommend trial sites — it explains the reasoning, grounded in a unified, current, and trusted business context.”
— Andrei Seryi, Director of Knowledge Management, PSI
“We retired six databases and now run global networking on one platform — simpler operations, fewer incidents, and faster answers.”
— HPE Aruba Networking, Engineering Team
“If one database could support document, key-value, and graph models, that was the ideal.”
— Scott Baugher, Co-founder & CEO, Kaseware
“Stitching together relational databases, warehouses, and lakes might have worked for yesterday’s analytics, but it’s a brittle foundation for AI systems that demand real-time, multimodal context.”
— Indranil Bandyopadhyay, Principal Analyst, Forrester
Trusted in ~200 Production Environments Worldwide
Four Ways to Partner With Arango
Resellers & VARs
Transact Arango licenses and subscriptions; lead with deal registration and margin.
Systems Integrators
Design, deploy, and operate Arango solutions; lead with services and delivery capacity.
Technology, ISV & OEM
Embed or integrate Arango into a product or platform; co-sell joint solutions.
Cloud & MSPs
Deliver Arango via Arango Managed Platform or managed offerings; recurring-revenue motion.
Ready to get started?
Apply to the Arango Partner Program, or book a demo to see the platform on your own data.