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# Arango

## Sitemaps
[XML Sitemap](https://arango.ai/sitemap_index.xml): Includes all crawlable and indexable pages.

## Posts
- [Context Is an Architectural Imperative](https://arango.ai/blog/context-is-an-architectural-imperative/): Why the enterprise data stack we built for people has to be re-architected for AI agents
- [Graph Databases + LLMs: Why Connected Data Makes AI Smarter](https://arango.ai/blog/graph-databases-llms-why-connected-data-makes-ai-smarter/): This month's Arango community meetup tackled a question more teams are asking as they move AI from pilots into production: why does connected data make AI smarter? Mark Milinkovich, Director of Product Marketing at Arango, opened the session, and Daniel Morris, Arango's Lead Solutions Architect based in the UK, walked the group through both the thinking behind graph-native context and a live demo of Contextus, an application he built to show what's possible on top of the Arango Contextual Data Platform.
- [Building Trusted Business Context for Enterprise AI](https://arango.ai/blog/building-trusted-business-context-for-enterprise-ai/): Featuring New Research from Forrester The Forrester Wave™: Multimodel Data Platforms, Q2 2026
- [Contextus: The AI that finds what you didn’t know to look for](https://arango.ai/blog/contextus-the-ai-that-finds-what-you-didnt-know-to-look-for/): Your business data already contains the answers. The problem is that no search, dashboard, or AI tool was built to find them — because they're hidden in the connections between things, not in the things themselves.
- [What Is the Best Architecture for Agentic AI?](https://arango.ai/blog/what-is-the-best-architecture-for-agentic-ai/): 6 Architectural Requirements for Production-Ready AI Systems
- [Tracing Hidden Risk: How a Transitive-Risk Knowledge Graph Catches What Sanctions Screening Misses](https://arango.ai/blog/tracing-hidden-risk-how-a-transitive-risk-knowledge-graph-catches-what-sanctions-screening-misses/): This post summarizes Arthur Keen's technical deep-dive on the Sentries risk-intelligence system. Read the full article on Medium →
- [Designing at Speed: Graph Intelligence for IC Hardware Design](https://arango.ai/blog/designing-at-speed-graph-intelligence-for-ic-hardware-design/): Standard vector RAG fails in IC design because silicon is a graph, not a list of documents. The Arango Contextual Data Platform fuses graph traversal, vector search, and document storage in a single engine — replacing fragmented stacks with one deterministic pipeline. The result: 92% fewer LLM tokens, 5x lower query latency, zero hallucinated module links, and automated spec-to-silicon traceability across multi-repo hardware datasets.
- [The Billion-Token Problem and the Architecture Built to Solve It](https://arango.ai/blog/the-billion-token-problem-and-the-architecture-built-to-solve-it/): Multi-agent AI is consuming tokens at a pace that is breaking enterprise budgets. This brief shows how combining Arango's Contextual Data Layer with NVIDIA's KV-cache infrastructure cuts costs by 66% — without reducing model quality or agent capability.
- [Agentic AI in Clinical Trials: The Real Challenge Isn’t Automation — It’s Trust](https://arango.ai/blog/agentic-ai-in-clinical-trials-the-real-challenge-isnt-automation-its-trust/): Key takeaways from the Swiss Biotech Day panel discussion on Agentic AI in Clinical Trials, featuring leaders from PSI CRO, Basilea Pharmaceutica, Luzsana Biotechnology Europe AG, DataArt, and Arango.
- [The 3 Agentic AI Gaps: What CDAOs Must Do Now](https://arango.ai/blog/the-3-agentic-ai-gaps-what-cdaos-must-do-now/): Observations from the Gartner CDAO Summit, New York
- [Deploy Your Code, Your Way — Introducing Arango BYOC](https://arango.ai/blog/deploy-your-code-your-way-introducing-arango-byoc/): Arango's BYOC (Bring Your Own Code/Container) feature lets you run custom services — APIs, ML models, data processors — directly inside the Arango Contextual Data Platform, right next to your data. Upload your code as a .tar.gz (the platform builds and secures the container for you) or point to your own Docker image. Either way, authentication, routing, and network isolation are handled automatically via the Arango Control Plane. Services can be scoped globally or to a single database, and deploy in minutes through the Container Manager UI. Watch out for two gotchas: ARM/amd64 build mismatches on M-series Macs, and optional dependencies that need to be flattened into your pyproject.toml before packaging.
- [Metadata Helps You Understand Data. AI Needs a Contextual Data Layer.](https://arango.ai/blog/metadata-helps-you-understand-data-ai-needs-a-contextual-data-layer/): Metadata helps you understand and govern your data. But AI systems—especially agents and assistants—don’t fail because they lack understanding. They fail because they lack a stable way to make decisions on data at scale – consistently reasoning, deciding, and acting on the same business context. When context has to be rebuilt every time, systems drift. What AI needs is a contextual data layer where meaning, relationships, state are continuously maintained and directly usable.
- [The 80% Problem in AI Agent Architecture: Context Must Be Persistent, Not a Pipeline](https://arango.ai/blog/the-80-problem-in-ai-agent-architecture-context-must-be-persistent-not-a-pipeline/): Why AI agent and chatbot programs turn into data plumbing—and the architectural choice that determines whether you ship solutions or infrastructure.
- [What Is a Frankenstack—and Why It’s Breaking Enterprise AI](https://arango.ai/blog/what-is-a-frankenstack-and-why-its-breaking-enterprise-ai/): A Frankenstack is the result of a stitching together different systems without an overall architecture perspective. It’s composed of multiple disconnected systems—databases, pipelines, and services—stitched together to support modern applications.
- [Why Graph Databases Alone Don’t Win Enterprise AI (And What Actually Does)](https://arango.ai/blog/why-graph-databases-alone-dont-win-enterprise-ai-and-what-actually-does/): Jakki Geiger, CMO at Arango in conversation with Ravi Marwaha, Chief Product & Technology Officer at Arango
- [The Missing Layer in OpenClaw Agent Architectures: Contextual Data](https://arango.ai/blog/the-missing-layer-in-openclaw-agent-architectures-contextual-data/): Most AI agents rely on fragmented memory and retrieval, limiting their ability to reason and act. A contextual data layer connects relationships, semantics, and real-time state—enabling agents to move from retrieval to understanding, decision-making, and action at scale.
- [How PSI Reduced Clinical Trial Site Identification From Weeks to Minutes With a Unified, Trusted Context Layer](https://arango.ai/blog/how-psi-reduced-clinical-trial-site-identification-from-weeks-to-minutes-with-a-unified-trusted-context-layer/): Problem: Clinical trial site identification is slow and costly, and 30-40% trial sites under-enroll patients, wasting time and millions of dollars per study.
- [At NVIDIA GTC, a Clear Shift Emerged: Teams Are Moving Beyond Building AI Data Architecture from Scratch](https://arango.ai/blog/at-nvidia-gtc-a-clear-shift-emerged-teams-are-moving-beyond-building-ai-data-architecture-from-scratch/): Fragmented data and missing business context are making it difficult for AI systems to reason, decide, and act reliably in production.
- [Arango Contextual Data Platform 4.0 Is Now Generally Available (GA)](https://arango.ai/blog/arango-introduces-contextual-data-platform-4-0/): The Contextual Data Layer for Enterprise AI
- [Go Beyond Vector Databases](https://arango.ai/blog/go-beyond-vector-databases/): Jakki Geiger, CMO at Arango in conversation with Ravi Marwaha, Chief Product & Technology Officer at Arango
- [The Missing Layer in the AI Stack: Why Agentic AI Demands a Contextual Data Layer](https://arango.ai/blog/the-missing-layer-in-the-ai-stack-why-agentic-ai-demands-a-contextual-data-layer/): Enterprise AI is entering a new phase.
- [The Hidden Data Architecture Problem Blocking Enterprise AI](https://arango.ai/blog/the-hidden-data-architecture-problem-blocking-enterprise-ai/): Why AI Pilots Fail to Scale—and How AI-Ready Data Architecture Fixes It
- [Multimodel Data Platforms: The Next Evolution Beyond Vector & Graph Silos](https://arango.ai/blog/multimodel-data-platforms-the-next-evolution-beyond-vector-graph-silos/): Jakki Geiger, CMO at Arango in conversation with Ravi Marwaha, Chief Product & Technology Officer at Arango
- [The Missing Layer in Enterprise AI](https://arango.ai/blog/the-missing-layer-in-enterprise-ai/): Jakki Geiger, CMO at Arango in conversation with Ravi Marwaha, Chief Product & Technology Officer at Arango
- [How Arango Evolved in 2025—and What It Enables Next](https://arango.ai/blog/how-arango-evolved-in-2025-and-what-it-enables-next/): As we head into 2026, it’s a good moment to reflect on how Arango evolved over the past year. In 2025, our focus was twofold: 
- [Why AI Fails Without Context — And Why Multimodel Data Platforms Are Becoming the New Enterprise Standard](https://arango.ai/blog/why-ai-fails-without-context-and-why-multimodel-data-platforms-are-becoming-the-new-enterprise-standard/): Every enterprise AI project has the same failure point — and it’s not the model.
- [What NVIDIA GTC Made Clear: The Future of AI Is Context-Driven, And It Requires More Than a Single-Model Database](https://arango.ai/blog/what-nvidia-gtc-made-clear-the-future-of-ai-is-context-driven-and-it-requires-more-than-a-single-model-database/): Last week at NVIDIA GTC in Washington, DC, that message came through in every keynote, demo, and hallway conversation. We’ve entered a new phase of AI that understands rather than just predicts.
- [The Next Evolution of Arango: Powering the Age of Contextual AI](https://arango.ai/blog/the-next-evolution-of-arango-powering-the-age-of-contextual-ai/): At Arango, every new chapter begins with our community. Chief Product and Technology Officers, Heads of Engineering, Developers, Architects, and leaders like you have pushed us to think bigger – to make data not only connected, but intelligent.
- [From Prototype to Production: Why It’s Time to Move to ArangoDB Enterprise Edition](https://arango.ai/blog/why-its-time-to-move-to-arangodb-enterprise-edition/): TL;DR:Teams outgrow "good enough" faster than they think. ArangoDB Enterprise Edition isn't a feature upgrade-it's the foundation for scale, security, and future-ready workloads. 
- [Generate a Video Knowledge Graph: NVIDIA VSS Blueprint with GraphRAG on ArangoDB](https://arango.ai/blog/generate-a-video-knowledge-graph-nvidia-vss-blueprint-with-graphrag-on-arangodb/): NVIDIA Blueprint for video search and summarization (VSS) provides a sample architecture to develop visually perceptive and interactive video analytics visual AI agents. VSS Blueprint from Metropolis combines generative AI, VLMs, LLMs, RAG, and media management services. These AI agents can be deployed throughout factories, warehouses, retail stores, airports, traffic intersections, and more - helping streamlining operations. The VSS 2.4 release makes it easy to enhance vision AI applications with generative AI through a VLM, enabling powerful new features for smart infrastructure. 
- [From Native Multi-Model Graph Database to AI Data Platform: Arango’s Next Chapter](https://arango.ai/blog/multi-model-graph-database-to-genai-data-platform/): In this webinar we explored Arango's next chapter-evolving from a multi-model database into a AI data platform built for speed, scale, and flexibility.
- [Deploying and Securing an ArangoDB Cluster Using Docker](https://arango.ai/blog/deploying-and-securing-an-arangodb-cluster-using-docker/): Deploying a distributed database securely can be challenging, but Docker simplifies the process. This guide walks you through setting up a three-node ArangoDB cluster using Docker and Docker Compose. I'll also cover essential security measures, including authentication, TLS encryption, and automated backups.
- [Legal and Compliance challenges in modern finance and how ArangoDB plus GraphRAG solves them](https://arango.ai/blog/legal-and-compliance-challenges-in-modern-finance-and-how-arangodb-plus-graphrag-solves-them/): In today's financial sector, staying compliant isn't just about keeping up - it's about keeping your organization safe, agile, and ahead of the competition. Every week new sanction headlines, revised regulations, and internal policy updates flood your compliance teams. These aren't just bulletins; they're pressure points. Buried inside government websites, legal PDFs, third-party data feeds, and dusty internal repositories are potential threats and obligations. And somewhere in those scattered silos, an analyst - likely under time pressure - is trying to connect the dots.
- [Deploying ArangoDB on Kubernetes and customizing settings](https://arango.ai/blog/deploying-arangodb-on-kubernetes-and-customizing/): Deploying an ArangoDB cluster on Kubernetes is a straightforward process thanks to the ArangoDB Operator, a powerful tool that simplifies the deployment, management, and scaling of ArangoDB clusters in a Kubernetes environment. 
- [From Sensor Noise to Strategic Insights: How ArangoDB and GraphRAG are Reinventing IoT for Smart Manufacturing](https://arango.ai/blog/from-sensor-noise-to-strategic-insights-how-arangodb-and-graphrag-are-reinventing-iot-for-smart-manufacturing/): The modern manufacturing floor hums not just with machines, but with data as well. From predictive maintenance and energy monitoring to quality control and supply chain automation, IoT devices now generate terabytes of time-series and event data on a daily basis.
- [Integrating ArangoDB with Kubernetes for Seamless Deployment](https://arango.ai/blog/integrating-arangodb-with-kubernetes-for-seamless-deployment/): Are you a database architect or DevOps architect tasked with deploying modern databases like ArangoDB on Kubernetes? Kubernetes, with its robust orchestration capabilities, provides a solid foundation for managing containerized workloads, ensuring reliability and adaptability for database deployments.
- [ArangoDB’s GraphRAG Transforms Healthcare Data Management](https://arango.ai/blog/arangodbs-graphrag-transforms-healthcare-data-management/): Healthcare organizations across the spectrum—from large hospital systems to payers to individual providers—face unprecedented IT challenges. These include siloed information systems, complex regulatory requirements, and rapidly evolving clinical research. Add fast-growing patient expectations for personalized care, and one can relate to how challenging all this is. Traditional databases or data lakes have rigid schemas and limited modeling capabilities. They often fail to address these complex needs.
- [Benchmark Results – ArangoDB vs. Neo4j : ArangoDB up to 8x faster than Neo4j](https://arango.ai/blog/benchmark-results-arangodb-vs-neo4j-arangodb-up-to-8x-faster-than-neo4j/): This document presents the benchmark results comparing the ArangoDB's Graph Analytics Engine (GAE) against Neo4j. The GAE is just one component of ArangoDB's Data Science Suite. 
- [Vector Search in ArangoDB: Practical Insights and Hands-On Examples](https://arango.ai/blog/vector-search-in-arangodb-practical-insights-and-hands-on-examples/): Vector search is gaining traction as a go-to tool for handling large, unstructured datasets like text, images, and audio. It works by comparing vector embeddings, numerical representations generated by machine learning models, to find items with similar properties. With the integration of Facebook's FAISS library, ArangoDB brings scalable, high-performance vector search directly into its core, accessible via AQL (ArangoDB Query Language). Vector Search is now just another, fully-integrated data type/model in ArangoDB's multi-model approach.
- [Some Perspectives on HybridRAG in an ArangoDB World](https://arango.ai/blog/some-perspectives-on-hybridrag-in-an-arangodb-world/): Graph databases continue to gain momentum, thanks to their knack for handling intricate relationships and context. Developers and tech leaders are seeing the potential of pairing them with the creative strength of large language models (LLMs). This combination is opening the door to more precise, context-aware answers to natural language prompts. That’s where RAG comes in—it pulls in useful information, whether from raw text (VectorRAG) or a structured knowledge graph (GraphRAG), and feeds it into the LLM. The result? Smarter, more relevant responses that are grounded in actual data.
- [ArangoDB vs. Neo4J](https://arango.ai/blog/arangodb-vs-neo4j/): Update: Evolving ArangoDB's Licensing Model for a Sustainable Future
- [ArangoDB 3.12 – Performance for all Your Data Models](https://arango.ai/blog/arangodb-3-12-performance-for-all-your-data-models/): Congrats to the team and community for the latest ArangoDB release 3.12! ArangoDB 3.12 is focused on greatly improving performance and observability both for the core database and our search offering. In this blog post, we will go through some of the most important changes to ArangoDB and give you an idea of how this can be utilized in your products.
- [Advanced Fraud Detection in Financial Services with ArangoDB and AQL](https://arango.ai/blog/advanced-fraud-detection-in-financial-services-with-arangodb-and-aql/): In the realm of financial services, where fraud detection is both critical and complex, the choice of database and query language can impact the efficiency and effectiveness of fraud detection systems. Let’s explore how ArangoDB – a multi-model graph database – is powered by AQL (ArangoDB Query Language) to handle multiple, real-world fraud detection scenarios in a much more seamless and powerful way compared to traditional Relational Database Management Systems (RDBMS).
- [Update: Evolving ArangoDB’s Licensing Model for a Sustainable Future](https://arango.ai/blog/update-evolving-arangodbs-licensing-model-for-a-sustainable-future/): Updated 3/28/25 for accuracy.
- [The world is a graph: How Fix reimagines cloud security using a graph in ArangoDB](https://arango.ai/blog/the-world-is-a-graph-how-fix-reimagines-cloud-security-using-a-graph-in-arangodb/): In 2015, John Lambers, a Corporate Vice President and Security Fellow at Microsoft wrote "Defenders think in lists. Attackers think in graphs. As long as this is true, attackers win.ˮ
- [Reintroducing the ArangoDB-RDF Adapter](https://arango.ai/blog/reintroducing-the-arangodb-rdf-adapter/): ArangoRDF allows you to export Graphs from ArangoDB into RDFLib, the standard library for working with Resource Description Framework (RDF) in Python, and vice-versa.
- [Introducing ArangoDB’s Data Loader: Revolutionizing Your Data Migration Experience](https://arango.ai/blog/introducing-arangodbs-data-loader-revolutionizing-your-data-migration-experience/): At ArangoDB, our commitment to empowering companies, developers, and data enthusiasts with cutting edge tools and resources remains unwavering. Today, we're thrilled to unveil our latest innovation, the Data Loader, a game-changing feature designed to simplify and streamline the migration of relational databases to ArangoGraph. Let's dive into what makes Data Loader a must-have tool for your data migration needs.
- [How ArangGraphML Leverages Intel’s PyG Optimizations](https://arango.ai/blog/how-aranggraphml-leverages-intels-pyg-optimizations/): ArangoDB and Intel have announced a groundbreaking partnership to enhance Graph Machine Learning (GraphML) using Intel's high-performance processors. This collaboration, part of the Intel Disruptor Program, will seek to integrate ArangoDB's graph database solutions with Intel's Xeon CPU. This synergy promises to revolutionize data analytics and pattern recognition in complex graph structures, marking a new era in database technology and GraphML advancements.
- [ArangoDB’s Exciting Updates: Introducing Our Developer Hub and GenAI Bots!](https://arango.ai/blog/arangodbs-exciting-updates-introducing-our-developer-hub-and-genai-bots/): We’ve always believed in the power of community-driven knowledge sharing, and we are proud to present our brand-new Developer Hub, accessible at developer.arangodb.com. This hub is a testament to our dedication to creating an ecosystem that empowers you with the knowledge and resources you need.
- [Evolving ArangoDB’s Licensing Model for a Sustainable Future](https://arango.ai/blog/evolving-arangodbs-licensing-model-for-a-sustainable-future/): ArangoDB as a company is firmly grounded in Open Source. The first commit was made in October 2011, and today, we are very proud of having over 13,000 stargazers on GitHub. We believe that the ArangoDB community should be able to enjoy all of the benefits of using ArangoDB, and we have always offered a completely free community edition in addition to our paid enterprise offering.
- [ArangoGraph Now Available on AWS Marketplace](https://arango.ai/blog/arangograph-now-available-on-aws-marketplace/): Estimated reading time: 1 minute
- [Bridging Knowledge and Language: ArangoDB Empowers Large Language Models for Real-World Applications](https://arango.ai/blog/bridging-knowledge-and-language-arangodb-empowers-large-language-models-for-real-world-applications/): Today, two very different technology concepts have become prominent in data analysis and predictive analytics: Knowledge Graphs and Large Language Models (LLMs). These domains each have their unique benefits, and influence the ways that we engage with and derive meaningful insights from constantly expanding and complex datasets.  They are like the Odd Couple - better together than on their own!
- [Three Ways to Scale your Graph](https://arango.ai/blog/three-ways-to-scale-your-graph/): As businesses grow and their data needs increase, they often face the challenge of scaling their database systems to keep up with the increasing demand.
- [Graph and Entity Resolution Against Cyber Fraud](https://arango.ai/blog/graph-and-entity-resolution-against-cyber-fraud/): Entity resolution can be used for a variety of purposes, such as identifying duplicate customer records in a marketing database, matching medical records to patients, detecting fraudulent activities by identifying multiple identities of the same person, or linking social media profiles to a single individual. Entity resolution can improve the accuracy and completeness of data by reducing redundancy, eliminating errors, and creating a unified view of the data. It can also be used to facilitate data integration and analysis, as well as support various applications such as recommendation systems, personalized marketing, and fraud detection.
- [Combat Fraud with Graph](https://arango.ai/blog/combat-fraud-with-graph/): One technology that is increasingly being used to detect and prevent fraud is graph databases. This blog post will explain graph databases and how they can help with fraud detection.
- [Why Should You Care About SOC 2?](https://arango.ai/blog/why-should-you-care-about-soc-2/): While driving along California's Highway 101 and its billboards, compliance and SOC 2 seem to be an omnipresent – yet challenging – topic. But is it really? And if so, why? In this blog post, we want to share why and how ArangoDB has become SOC 2 compliant.
- [Introducing the ArangoDB-PyG Adapter](https://arango.ai/blog/introducing-the-arangodb-pyg-adapter/): We are proud to announce the GA 1.0 release of the ArangoDB-PyG Adapter!
- [What is a Graph Database?](https://arango.ai/blog/what-is-a-graph-database/): Graphs occur everywhere in everyday life: your network of friends, the network of roads you drive on, and the supply chain of factories, ships, and roads that brought you the device you're reading this on. While it might be easy to connect the dots on how most things can be shown as a graph, what makes a database a graph database? That is the question you will have the answer to in this blog post, but to put it simply: a graph consists of nodes, edges, and properties representing the relationships within data.
- [Who’s Who in Data Science](https://arango.ai/blog/whos-who-in-data-science/): Multiple data science personas participate in the daily operations of data logistics and intelligent business applications. Management and employees need to understand the big picture of data science to maximize collaboration efforts for these operations. This article will highlight the specialized roles and skillsets needed for the different data science tasks and the best tools to empower data-driven teams. You will come away from this article with a better understanding of how to support your own data science teams, and it is valuable for both managers and team members alike.
- [Introducing the new ArangoDB Datasource for Apache Spark](https://arango.ai/blog/introducing-the-new-arangodb-datasource-for-apache-spark/): We are proud to announce the general availability of ArangoDB Datasource for Apache Spark: a new generation Spark connector for ArangoDB.
- [Introducing the ArangoDB-DGL Adapter](https://arango.ai/blog/introducing-the-arangodb-dgl-adapter/): We are proud to announce the GA 1.0 release of the ArangoDB-DGL Adapter!
- [Introducing the ArangoDB-NetworkX Adapter](https://arango.ai/blog/introducing-the-arangodb-networkx-adapter/): We are proud to announce the GA 3.0 release of the ArangoDB-NetworkX Adapter!
- [A Guide to Putting Together a Virtual Conference](https://arango.ai/blog/a-guide-to-putting-together-a-virtual-conference/): Hello! I'm Cris Miranda, the community manager at ArangoDB, and I make sure ArangoDB has a vibrant, wholesome, and ever-growing community of amazing people. I want to share some tips and advice based on valuable lessons we've learned from our first-ever virtual developers' conference. 
- [ArangoSync: A Recipe for Reliability](https://arango.ai/blog/arangosync-a-recipe-for-reliability/): The Datacenter-to-Datacenter Replication (DC2DC) for clusters including the arangosync tool is no longer supported from v3.12 onward.
- [A Comprehensive Case-Study of GraphSage using PyTorchGeometric and Open-Graph-Benchmark](https://arango.ai/blog/a-comprehensive-case-study-of-graphsage-using-pytorchgeometric/): This blog post provides a comprehensive study on the theoretical and practical understanding of GraphSage, this notebook will cover:
- [Entity Resolution in ArangoDB](https://arango.ai/blog/entity-resolution-in-arangodb/): This post will dive into the world of Entity Resolution in ArangoDB.  This is a companion piece for our Lunch and Learn session, Graph & Beyond Lunch Break #15: Entity Resolution.
- [Inside the Avocado Grove: From Canada to Germany and the Digital Marketing of Avocados](https://arango.ai/blog/inside-the-avocado-grove-how-i-went-from-canada-to-germany-and-digital-marketing-of-avocados/): My name is Laura, and I am responsible for digital marketing here at ArangoDB. 
- [Word Embeddings in ArangoDB](https://arango.ai/blog/word-embeddings-in-arangodb/): This post will dive into the world of Natural Language Processing by using word embeddings to search movie descriptions in ArangoDB.
- [Introducing Developer Deployments on ArangoDB ArangoGraph](https://arango.ai/blog/introducing-developer-deployments-on-arangodb-oasis/): Today we're announcing the introduction of Developer deployments as a beta feature on the Oasis platform.
- [ArangoBNB Data Preparation Case Study: Optimizing for Efficiency](https://arango.ai/blog/arangobnb-data-preparation-case-study/): This case study covers a data exploration and analysis scenario about modeling data when migrating to ArangoDB. The topics covered in this case study include:
- [C++ Memory Model: Migrating from x86 to ARM](https://arango.ai/blog/cpp-memory-model-migrating-from-x86-to-arm/): Over the past years, we have seen a shift in processors from the previously dominant x86 architecture to the more energy-efficient (and often cheaper) ARM architecture. This trend is true for both consumer hardware, e.g., Apple's M1 SoC and also server hardware, e.g. AWS's Gravitron processor. Given that the ARM architecture has a more relaxed memory model, this might have some subtle impact on C++ programs…
- [ArangoML Series: Multi-Model Collaboration](https://arango.ai/blog/arangoml-series-multi-model-collaboration/): Multi-Model Machine Learning
- [Fixing a Memory Leak in Go: Understanding time.After](https://arango.ai/blog/fixing-a-memory-leak-in-go-understanding-time-after/): Recently, we decided to investigate why our application ARANGOSYNC for synchronizing two ArangoDB clusters across data centers used up a lot of memory – around 2GB in certain cases. The environment contained ~1500 shards with 5000 GOroutines. Thanks to tools like pprof (to profile CPU and memory usage) it was very easy to identify the issue. The GO profiler showed us that memory was allocated in the function `time.After()` and it accumulated up to nearly 1GB. The memory was not released so it was clear that we had a memory leak. We will explain how memory leaks can occur using the `time.After()` function through three examples.
- [Building HIPAA Compliant Applications With ArangoDB](https://arango.ai/blog/building-hipaa-compliant-applications-with-arangodb/): Protecting personal data is already quite high on (hopefully) all priority lists. Protecting personal healthcare information is even more important. If you are a company or organization in the health services industry and you work with personal patient data, then you have most probably heard about HIPAA compliance. The Health Insurance Portability and Accountability Act (HIPAA) is a law passed in 1996 by US Congress that sets standards and protocols for the protection of patient data in the healthcare industry.
- [Time traveling with graph databases](https://arango.ai/blog/time-traveling-with-graph-databases/): Graph databases are often used to analyze relations within highly interconnected datasets. Social networks, recommendation engines, corporate hierarchies, fraud detection or querying a bill of materials are common use cases. But these datasets change over time and you as a developer or data scientist may want to time travel and analyze these changes.
- [NoSQL Performance Benchmark 2018 – MongoDB, PostgreSQL, OrientDB, Neo4j and ArangoDB](https://arango.ai/blog/nosql-performance-benchmark-2018-mongodb-postgresql-orientdb-neo4j-arangodb/): ArangoDB, as a native multi-model database, competes with many single-model storage technologies. When we started the ArangoDB project, one of the key design goals was and still is to at least be competitive with the leading single-model vendors on their home turf. Only then does a native multi-model database make sense. To prove that we are meeting our goals and are competitive, we run and publish occasionally an update to the benchmark series. This time we included MongoDB, PostgreSQL (tabular & JSONB), OrientDB and Neo4j.

## Pages
- [Demo – Federal Team](https://arango.ai/demo-federal-government/): Discover how Arango helps U.S. Federal Government organizations connect data, improve information sharing, accelerate analysis, and support mission-critical decisions. Schedule a personalized demonstration focused on your mission requirements.
- [Talk with our Team – Federal Government](https://arango.ai/contact-us-federal-government/): Arango helps U.S. Federal Government organizations modernize data environments, improve information sharing, and support mission-critical decision-making. Tell us about your goals, and we'll connect you with the right member of our Federal Team.
- [Protected: Brand Assets](https://arango.ai/brand-assets/): Download logos and see brand color codes.
- [Protected: Customer Support](https://arango.ai/customer-support/): Deep expertise, fast responses, and a team invested in your outcomes.
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- [ArangoDB Product Support End-of-life Announcements](https://arango.ai/arangodb-product-support-end-of-life-announcements/): ArangoDB 3.11 was released for General Availability on May 30, 2023. As of May 30, 2025, support for ArangoDB 3.11 will end. Current ArangoDB 3.11 users are encouraged to upgrade to the latest release of ArangoDB.
- [ArangoDB Subprocessors](https://arango.ai/arangodb-subprocessors/): ArangoDB may engage the following entities to process personal data that you include in your use of ArangoGraph Insights Platform:
- [Newsletter Signup](https://arango.ai/newsletter-signup/): We’re excited to connect with you. Related Resources See All Resources
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- [Arango for AI](https://arango.ai/arango-for-ai/): Arango for Enterprise AI
- [Request Pricing](https://arango.ai/request-pricing/): Share your environment and scale requirements, and we’ll provide transparent pricing for managed or self-managed deployments.
- [Demo](https://arango.ai/demo/): Meet with our team and see how organizations are turning fragmented enterprise data into connected context for AI agents, assistants, and apps.
- [Customer Stories](https://arango.ai/customer-stories/): Explore how leading organizations use Arango to simplify complexity, scale without limits, and deliver accurate AI with context across industries and applications.
- [Partners](https://arango.ai/partners/): Arango partners with leading technology providers, system integrators, and solution experts to deliver a stronger data foundation for enterprises worldwide. Together, we help organizations simplify complexity, scale without limits, and power accurate GenAI.
- [Why AI](https://arango.ai/why-ai/): Because Artificial IntellegenceI is redefining how every enterprise uses data.
- [Why Graph](https://arango.ai/why-graph/): Because connected data is how the real world works.
- [Hub](https://arango.ai/hub/): Latest Blogs See All Blogs Latest Resources See All Resources Latest News See All News
- [Arango Data Platform](https://arango.ai/products/arango-data-platform/): Arango Data Platform
- [Resources](https://arango.ai/resources/)
- [Use Cases](https://arango.ai/use-cases/): Explore how leading organizations use Arango to simplify complexity, scale without limits, and deliver accurate AI with context across industries and applications.
- [Developers](https://arango.ai/developers/): Documentation, training, code samples, and downloads to help you build apps faster and smarter.
- [Contextual Data Platform](https://arango.ai/products/contextual-data-platform/): Introducing the Arango Contextual Data Platform
- [Pricing](https://arango.ai/pricing/): Choose the product that fits your use case, then choose the deployment option that fits your environment.
- [Downloads](https://arango.ai/downloads/): Starting with version 3.12.5, the Community Edition includes all Enterprise features with no time limits and is free for non-commercial use and datasets up to 100 GB.
- [ArangoDB Enterprise](https://arango.ai/products/arangodb/): ArangoDB
- [Why Arango](https://arango.ai/why-arango/): Because one unified data foundation powers everything—from apps to AI.
- [News & Press](https://arango.ai/news-press/): Contact press@arango.ai
- [Contact Us](https://arango.ai/contact-us/): From everyday applications to AI breakthroughs, Arango helps teams build faster on a unified data foundation. Tell us your goals, and we’ll help you get there.
- [Events](https://arango.ai/events/): Upcoming Events On-Demand Webinars and Virtual Meetups Past Events
- [Careers](https://arango.ai/careers/): If you’re driven by solving hard problems, owning your outcomes, and shaping the data stack of the future, you’re in the right place.
- [About Us](https://arango.ai/about-us/): Arango provides the trusted data foundation for the next wave of AI.
- [Theme](https://arango.ai/theme/): This is an example page. It's different from a blog post because it will stay in one place and will show up in your site navigation (in most themes). Most people start with an About page that introduces them to potential site visitors. It might say something like this:
- [Blog](https://arango.ai/blog/)
- [Home](https://arango.ai/): Give every agent, app, and AI workload the same live view of your business. Consistent answers. Explainable decisions. Production-ready from day one.

## Landing Pages
- [[Test] Sequel](https://arango.ai/lp/test-sequel/): 0bbb6486-b6ba-4ff7-81f1-e38c277b5cfd
- [Enterprise Knowledge Graphs](https://arango.ai/lp/enterprise-knowledge-graphs/): So we automated the whole thing.
- [[TY] Snowflake Summit](https://arango.ai/lp/ty-snowflake-summit/): From ArangoDB to the full Contextual Data Platform, you'll see how everything works together in one unified foundation. We’ll be in touch soon to set up your demo.
- [Snowflake Summit](https://arango.ai/lp/snowflake-summit/): Snowflake Summit
- [Arango Academy](https://arango.ai/lp/arango-academy/): Coming Soon
- [Nvidia GTC](https://arango.ai/lp/nvidia-gtc/): NVIDIA GTC Conference
- [[TY] Definitive Guide to Agentic AI-Ready Data Architecture](https://arango.ai/lp/ty-definitive-guide-to-ai-ready-data-architecture/): Thank you for your interested in The Definitive Guide to Agentic AI-Ready Data Architecture.
- [[TY] The Missing Layer in the AI Stack: Why Data Architecture Determines AI Success or Failure](https://arango.ai/lp/ty-the-missing-link-in-the-ai-stack-why-data-architecture-determines-ai-success-or-failure/): Thank you for registering for the webinar, The Missing Layer in the AI Stack: Why Data Architecture Determines AI Success or Failure.
- [The Missing Layer in the AI Stack: Why Data Architecture Determines AI Success or Failure](https://arango.ai/lp/the-missing-layer-in-the-ai-stack-why-data-architecture-determines-ai-success-or-failure/): Webinar
- [Nvidia – GTC DC](https://arango.ai/lp/nvidia-gtc-dc/): NVIDIA GTC AI Conference

## Resources
- [Why Autonomous AI Requires a New Operational Foundation Grounded In Trust](https://arango.ai/resources/why-autonomous-ai-requires-a-new-operational-foundation-grounded-in-trust/): Many leaders, I’ve observed, are making a consequential transition: delegating operational authority to autonomous AI systems before building the controls required to govern them safely.
- [How to Build Reliable, Context-Aware AI at Enterprise Scale](https://arango.ai/resources/how-to-build-reliable-context-aware-ai-at-enterprise-scale/): Discover how leading organizations are creating AI systems that combine real-time enterprise knowledge, governance, and connected data to support intelligent decision-making and action.
- [Graph Databases + LLMs: Why Connected Data Makes AI Smarter](https://arango.ai/resources/graph-databases-llms-why-connected-data-makes-ai-smarter/)
- [The Forrester Wave: Multimodel Data Platforms, Q2 2026](https://arango.ai/resources/the-forrester-wave-multimodel-data-platforms-q2-2026/): Report
- [At-a-Glance: Arango Managed Platform](https://arango.ai/resources/at-a-glance-arango-managed-platform/): At-a-Glance
- [At-a-Glance: Find What You Didn’t Know to Look For](https://arango.ai/resources/at-a-glance-find-what-you-didnt-know-to-look-for/): At-A-Glance
- [At-a-Glance: The Contextual Data Platform for U.S. Federal Missions](https://arango.ai/resources/at-a-glance-the-contextual-data-platform-for-u-s-federal-missions/): At-A-Glance
- [At-a-Glance: From Blind Spots to Full Supply-Chain Visibility](https://arango.ai/resources/at-a-glance-from-blind-spots-to-full-supply-chain-visibility/): At-A-Glance
- [At-a-Glance: Preserving Trust, Provenance & Traceability for Zero Trust](https://arango.ai/resources/at-a-glance-preserving-trust-provenance-traceability-for-zero-trust/): At-A-Glance
- [Building the Contextual Data Layer for Enterprise AI](https://arango.ai/resources/building-the-contextual-data-layer-for-enterprise-ai/): Your AI agents are answering questions. But are they answering them correctly?
- [The Hidden Cost Of Agentic AI? Misallocated Effort](https://arango.ai/resources/the-hidden-cost-of-agentic-ai-misallocated-effort/): Originally published in Forbes Technology Council
- [How Leaders Can Make Their Data Agentic-AI-Ready](https://arango.ai/resources/how-leaders-can-make-their-data-agentic-ai-ready/): Originally published in Forbes Technology Council
- [The Contextual Data Layer for Enterprise AI](https://arango.ai/resources/the-contextual-data-layer-for-enterprise-ai/): 6 architectural requirements for building agentic-AI-ready systems.
- [The Business Context Gap Undermining Enterprise AI](https://arango.ai/resources/the-business-context-gap-undermining-enterprise-ai/): Originally published in Forbes Technology CouncilArango CEO Shekhar Iyer shares his perspective on why enterprise AI initiatives struggle—not because of model limitations, but due to missing business context.
- [Competing on Trust: What Every Company Can Learn From the EU AI Act](https://arango.ai/resources/competing-on-trust-what-every-company-can-learn-from-the-eu-ai-act/): Originally published in Forbes Technology Council
- [Avoiding the AI Failure Zone: Why Context and a Unified Data Layer Matter](https://arango.ai/resources/avoiding-the-ai-failure-zone-why-context-and-a-unified-data-layer-matter/): Originally published in Forbes Technology Council
- [The Context Gap: Why Frankenstacks can’t solve it and how Arango does](https://arango.ai/resources/the-context-gap-why-frankenstacks-cant-solve-it-and-how-arango-does/)
- [At-a-Glance: Unlock AI Value from Your Existing ArangoDB Deployment](https://arango.ai/resources/at-a-glance-unlock-ai-value-from-your-existing-arangodb-deployment/): At-a-Glance
- [At-a-Glance: Arango Contextual Data Platform](https://arango.ai/resources/at-a-glance-arango-contextual-data-platform/): At-a-Glance
- [At-a-Glance: Arango Platform Suite](https://arango.ai/resources/at-a-glance-arango-platform-suite/): At-a-Glance
- [At-a-Glance: ArangoDB](https://arango.ai/resources/at-a-glance-arangodb/): At-a-Glance
- [At-a-Glance: Arango Agentic AI Suite](https://arango.ai/resources/at-a-glance-arango-agentic-ai-suite/): At-a-Glance
- [At-a-Glance: Arango AutoGraph](https://arango.ai/resources/at-a-glance-arango-autograph/): At-a-Glance
- [The Missing Layer in the AI Stack: Why Data Architecture Determines AI Success or Failure](https://arango.ai/resources/the-missing-layer-in-the-ai-stack-why-data-architecture-determines-ai-success-or-failure/): Enterprises are investing heavily in AI agents, chatbots, and copilots—yet many initiatives stall before reaching production. The limiting factor is rarely the model. Instead, it’s the underlying data architecture’s ability to deliver contextual, connected, and governed data at scale.
- [The Definitive Guide  to AI-Ready Data Architecture](https://arango.ai/resources/agentic-ai-ready-data-architecture/): Resource
- [Fireside Chat: Arango AI Capabilities](https://arango.ai/resources/fireside-chat-with-chief-product-and-technology-officer/): The AI revolution is transforming how we think about technology — and the catalyst for your enterprise’s leap forward lies in your data and your AI/Agentic platform strategy.
- [Fireside Chat – Powering GenAI: The Critical Foundations for Scale](https://arango.ai/resources/fireside-chat-powering-genai-the-critical-foundations-for-scale/): Join Shekhar Iyer, CEO of ArangoDB, as he hosts a dynamic fireside chat with Dr. Arun Subramaniyan, CEO of Articul8, a cutting-edge GenAI startup.
- [The Modern Graph Database Buyer’s Guide](https://arango.ai/resources/the-modern-graph-database-buyers-guide/): A technical guide for teams evaluating graph databases built for scale, performance, and AI-driven workloads.
- [AQL on ArangoDB and Cypher on Neo4J](https://arango.ai/resources/aql-on-arangodb-and-cypher-on-neo4j/): Technical Comparison - Multi-Model Data Handling And Performance with Distributed Datasets
- [Taming Your Semantic Dragon: Operationalizing Knowledge Graphs with Multi-Model](https://arango.ai/resources/taming-your-semantic-dragon-operationalizing-knowledge-graphs-with-multi-model/): Enterprise Knowledge Graphs (EKGs) have been on the rise and are incredibly valuable tools for harmonizing internal and external data relevant to an organization into a common semantic model to improve operational efficiency for the enterprise and competitive advantage for the business units. On the other hand, EKGs can be difficult to develop and sustain, suffer from scalability issues, and can be difficult for business units to consume.
- [Graph Done Right](https://arango.ai/resources/graph-done-right/): A graph database uses graph structures for semantic queries with nodes, edges, and properties to represent and store data about each node and graph. Graph databases are often used where data is hard to model with traditional relational databases of tables with rows and columns.
- [Comparison: RAG with Vector Databases vs. ArangoDB GraphRAG with Knowledge Graphs](https://arango.ai/resources/comparison-rag-with-vector-databases-vs-arangodb-graphrag-with-knowledge-graphs/): Businesses need solutions that are accurate, cost-effective, and scalable to get meaningful insights from massive amounts of data. Retrieval-Augmented Generation (RAG) is an advantageous technique that can raise the game for large language models by integrating them with a retrieval system. This white paper compares two RAG implementations, (i) using vector databases and (ii) ArangoDB GraphRAG with knowledge graphs, focusing on four main criteria:
- [Unlocking GenAI’s Potential with Knowledge Graphs](https://arango.ai/resources/unlocking-genais-potential-with-knowledge-graphs/): McKinsey says 63% of business leaders are all-in on Generative AI (GenAI), but here’s the kicker—91% of them admit they’re nowhere near ready to handle the tech responsibly. That's a lot of enthusiasm with a side of panic. The risks, like inaccurate data outputs and cybersecurity issues, are huge hurdles they can’t ignore (McKinsey & Company).

## Case Studies
- [Linx Security](https://arango.ai/case-studies/linx-security/): By building on the Arango Contextual Data Platform, Linx Security created a live contextual data layer that connects identity relationships, access data, and governance information. The result is faster risk detection, improved visibility into identity relationships, automated remediation workflows, and a scalable foundation for AI-native identity governance.
- [Cybersecurity Software Company](https://arango.ai/case-studies/cybersecurity-software-company/): The company built a contextual data layer using the Arango Contextual Data Platform to provide AI systems and support engineers with trusted operational context for faster troubleshooting, more informed decision-making, and customer support at scale.
- [PSI](https://arango.ai/case-studies/psi/): PSI built SYNETIC™, an AI-enabled knowledge engine powered by the Arango Contextual Data Platform.
- [Kaseware](https://arango.ai/case-studies/kaseware/): Kaseware
- [Neostella](https://arango.ai/case-studies/neostella/): Neostella
- [Leading global medical technology, diagnostics, and digital solutions company](https://arango.ai/case-studies/medical-technology-company/): Medical Technology & Digital Solutions Company
- [HPE Aruba Networking](https://arango.ai/case-studies/hpe-aruba-networking/): HPE Aruba Networking
- [Cloud Imperium Games / Star Citizen](https://arango.ai/case-studies/cloud-imperium-games-star-citizen/): Cloud Imperium Games / Star Citizen
- [Articul8](https://arango.ai/case-studies/articul8/): Articul8
- [NVIDIA NVBugs](https://arango.ai/case-studies/nvidia-nvbugs/): Identify related issues, dependencies, and root causes across millions of bug reports.
- [NVIDIA Video Search & Summarization](https://arango.ai/case-studies/nvidia-video-search-summarization/): NVIDIA Video Search & Summarization
- [Matpriskollen](https://arango.ai/case-studies/matpriskollen/): Matpriskollen

## eBooks
- [The Missing Layer: What a Contextual Data Layer Is (and Isn’t)](https://arango.ai/ebooks/the-missing-layer-what-a-contextual-data-layer-is-and-isnt/): Chapter 3
- [The AI Failure Zone: Where Most Organizations Get Stuck](https://arango.ai/ebooks/the-ai-failure-zone-where-most-organizations-get-stuck/): Chapter 2
- [The Definitive Guide to Agentic AI-Ready Data Architecture](https://arango.ai/ebooks/the-definitive-guide-to-agentic-ai-ready-data-architecture/): Guide
- [The Case for Urgency: Why Enterprise AI Is a Competitive Mandate](https://arango.ai/ebooks/the-case-for-urgency-why-enterprise-ai-is-a-competitive-mandate/): Chapter 1
- [Why enterprise AI fails in production](https://arango.ai/ebooks/the-contextual-data-layer-for-enterprise-ai/): eBook
- [Why agentic AI breaks traditional architectures](https://arango.ai/ebooks/why-agentic-ai-breaks-traditional-architectures/): Chapter 1
- [What is a contextual data layer?](https://arango.ai/ebooks/what-is-a-contextual-data-layer/): Chapter 2
- [The 6 requirements your contextual data layer must deliver](https://arango.ai/ebooks/the-6-requirements-your-contextual-data-layer-must-deliver/): Chapter 3
- [Case studies](https://arango.ai/ebooks/case-studies/): Chapter 4
- [Additional use case examples](https://arango.ai/ebooks/additional-use-case-examples/): Chapter 5
- [The Arango Contextual Data Platform](https://arango.ai/ebooks/the-arango-contextual-data-platform/): Chapter 6
- [Why 2 of the 3 enterprise AI architectures hit a ceiling](https://arango.ai/ebooks/why-2-of-the-3-enterprise-ai-architectures-hit-a-ceiling/): Chapter 7
- [Frankenstacks were not built for this.](https://arango.ai/ebooks/frankenstacks-were-not-built-for-this/): Closing
- [FAQ, glossary, and sources](https://arango.ai/ebooks/faq-glossary-and-sources/): Appendix

## News
- [Arango Launches U.S. Federal Government Practice to Support Defense, Intelligence, Aerospace, and Civilian Agency Missions](https://arango.ai/news/arango-launches-u-s-federal-government-practice-to-support-defense-intelligence-aerospace-and-civilian-agency-missions/): SAN FRANCISCO — July 14, 2026 — Arango today announced the launch of its dedicated U.S. Federal Government Practice, expanding the company's long-term investment in helping defense, intelligence, aerospace, and civilian agencies modernize data environments, improve information sharing, reduce operational complexity, and prepare for secure AI-enabled operations.
- [Arango Recognized as a Strong Performer in Multimodel Data Platforms, Q2 2026 Evaluation](https://arango.ai/news/arango-recognized-as-a-strong-performer-in-multimodel-data-platforms-q2-2026-evaluation/): SAN FRANCISCO — June 30, 2026 — Arango, the company pioneering the live Contextual Data Layer for enterprise AI, today announced it has been named a Strong Performer in The Forrester Wave™: Multimodel Data Platforms, Q2 2026. According to the report, Arango is "well-suited to organizations seeking a contextual data foundation where multihop graph performance and verifiable reasoning are mission-critical for trusted AI."
- [Arango Showcases Live Contextual Data Layer for Enterprise AI at Snowflake Summit 2026](https://arango.ai/news/arango-showcases-live-contextual-data-layer-for-enterprise-ai-at-snowflake-summit-2026/): SAN FRANCISCO — June 2, 2026 — Arango today announced it will showcase Arango AutoGraph and preview Arango Virtual Graph at Snowflake Summit 2026, two capabilities of the Arango Contextual Data Platform that help organizations create an always-on Live Contextual Data Layer for enterprise AI. Together, these capabilities help organizations build connected business context once and make it continuously available across enterprise-wide AI initiatives.
- [PSI Reduces Clinical Trial Site Identification From Weeks to Minutes Using AI Agent Powered by Arango Contextual Data Platform](https://arango.ai/news/psi-reduces-clinical-trial-site-identification-from-weeks-to-minutes-using-ai-agent-powered-by-arango-contextual-data-platform/): SAN JOSE, Calif.--(BUSINESS WIRE) — NVIDIA GTC — Arango today announced that PSI CRO, a global clinical research organization, has reduced clinical trial site identification from up to six weeks to minutes using SYNETIC™, an AI-enabled knowledge engine powered by the Arango Contextual Data Platform. By unifying fragmented clinical research data into a trusted contextual data layer, PSI enables researchers to identify higher-performing trial sites faster, reduce non-enrolling institutions, and potentially save millions of dollars per clinical trial.
- [Arango Launches Contextual Data Platform 4.0 for AI-Agent-Ready Enterprise Data](https://arango.ai/news/arango-launches-contextual-data-platform-4-for-ai-agent-ready-enterprise-data/): SAN JOSE — March 17, 2026 —  At NVIDIA GTC, Arango today announced the release of Arango Contextual Data PlatformTM 4.0, designed to help organizations build and deploy enterprise AI agents, assistants, and applications faster and more reliably. The platform introduces the Contextual Data Layer, a new architectural approach that transforms fragmented enterprise data into a unified, current, and trusted business context that AI systems can reason over at scale. The release introduces the Agentic AI Suite, including more than 20 built-in AI services, a library of proprietary Arango tools, and capabilities such as AutoGraph, AutoRAG, and Arango Ada.
- [Arango Invites AI Leaders and Builders to Explore the Contextual Data Layer Powering Production AI Agents at NVIDIA GTC 2026](https://arango.ai/news/arango-invites-ai-leaders-and-builders-to-explore-the-contextual-data-layer-powering-production-ai-agents-at-nvidia-gtc-2026/): SAN FRANCISCO & COLOGNE, Germany--(BUSINESS WIRE)--Arango, announced it will participate in NVIDIA GTC 2026, taking place March 16–19 at the San Jose Convention Center. AI leaders and builders attending the event are invited to visit Booth 3421 to meet Arango executives and contextual data experts, explore demonstrations, and see how organizations are using a contextual data layer to power AI agents that reason, decide, and act across enterprise data.
- [Transient.AI Selects Arango as Its AI Data Infrastructure](https://arango.ai/news/transient-ai-selects-arango-as-its-ai-data-infrastructure/): SAN FRANCISCO & COLOGNE, Germany —Transient.AI, a fast-growing AI platform for capital markets, has selected the Arango AI Data Platform as its AI data infrastructure to power explainable, real-time intelligence for hedge funds, asset managers, and investment banks. The decision underpins Transient.AI’s Declarative AI Operating System, enabling financial institutions to model complex market relationships and automate decision-making while maintaining full transparency into how AI-driven outcomes are produced.
- [Arango Recognized in Analyst Report, The Multimodel Data Platforms Landscape, Q4 2025](https://arango.ai/news/arango-recognized-in-analyst-report-the-multimodel-data-platforms-landscape-q4-2025/): SAN FRANCISCO and COLOGNE, Germany — January 21, 2026 — Arango, a provider of Contextual AI data infrastructure, today announced that it has been recognized in Forrester’s report, The Multimodel Data Platforms Landscape, Q4 2025, authored by Indranil Bandyopadhyay, which provides an overview of 18 vendors in the multimodel data platforms (MMDP) market. The report is intended to help technology and data leaders understand the value they can expect from an MMDP vendor, learn how vendors differ, and evaluate options based on size and market focus.
- [Arango Launches AI Data Platform at NVIDIA GTC Introducing Contextual AI: Enterprise AI Powered by Business Context ](https://arango.ai/news/arango-launches-ai-data-platform-at-nvidia-gtc-introducing-the-system-of-context-for-enterprise-ai/): WASHINGTON, D.C. — October 28, 2025 —Arango unveiled the Arango AI Data Platform, a trusted data foundation for Contextual AI — transforming enterprise data into a System of Context that truly represents the business, so LLMs can deliver better outcomes with unlimited scale and cost efficiency. Operating at the data infrastructure layer, Arango fills the missing layer in the AI stack — because traditional data architectures weren’t built to support AI workloads or the data complexity they require.
- [Rated the #1 Graph Database in the world for Fall 2025](https://arango.ai/news/rated-the-1-graph-database-in-the-world-for-fall-2025/): ArangoDB is honored to have received the G2 Fall 2025 Leader badge as the #1 graph database for customer satisfaction. This means ArangoDB users love us more than any other graph database users love them!
- [Arango Brings “The GenAI Edge You’ve Been Missing” to NVIDIA GTC Washington, D.C.](https://arango.ai/news/arango-brings-the-genai-edge-youve-been-missing-to-nvidia-gtc-washington-d-c/): SAN FRANCISCO, CA, October 7, 2025 — Arango, the trusted data platform for enterprise-grade GenAI applications and solutions, today announced it will debut its new GenAI Data Platform at NVIDIA GTC Washington, D.C. on October 27–29, 2025 at the Walter E. Washington Convention Center. Under the theme “The GenAI Edge You’ve Been Missing,” Arango will showcase four live demos: GPU-accelerated graph analytics, NVIDIA VSS integration, GraphRAG for natural language Q&A, and enterprise-grade identity and access management. These demonstrations highlight how organizations can scale GenAI efficiently, reduce costs, and move from pilot to production faster.
- [Arango Names Jakki Geiger as CMO to Boost GenAI Innovation](https://arango.ai/news/arango-names-jakki-geiger-as-cmo-to-boost-genai-innovation/)
- [Arango Appoints Jakki Geiger as CMO to Accelerate Growth and Educate the Market on Enterprise-Grade GenAI Data Infrastructure](https://arango.ai/news/arango-appoints-jakki-geiger-as-cmo-to-accelerate-growth-and-educate-the-market-on-enterprise-grade-genai-data-infrastructure/): SAN FRANCISCO, CA – September 23, 2025 – Arango, the trusted data platform for enterprise-grade GenAI applications and solutions, today announced the appointment of Jakki Geiger as Chief Marketing Officer. A three-time CMO and veteran data and analytics leader, Geiger will lead global marketing as Arango builds on its multi-model database capabilities to give AI innovators and builders a trusted data foundation for building enterprise-grade GenAI applications, making it easier to extend today’s solutions with future-ready AI capabilities.
- [ArangoDB Further Powers Graph at Scale with Release of ArangoDB 3.9](https://arango.ai/news/arangodb-further-powers-graph-at-scale-with-release-of-arangodb-3-9/): San Francisco and Cologne, Germany – February 15, 2022 – ArangoDB, the most scalable open source graph database, today announced the GA release of ArangoDB 3.9. ArangoDB 3.9 includes new capabilities that improve graph query performance across distributed data sets, as well as additional search functionality, supporting the ever-growing need for businesses to perform analytics focused around graph and full-text search.
- [ArangoDB Democratizes Machine Learning with ArangoGraphML](https://arango.ai/news/arangodb-democratizes-machine-learning-with-arangographml/): SAN FRANCISCO – October 4, 2022 – ArangoDB, the company behind the most complete graph data and analytics platform, today released two new products to make machine learning more accessible: ArangoGraphML (closed beta) and Jupyter Notebooks-as-a-service (open beta).
- [ArangoDB + Intel: Accelerating GraphML with Advanced Processing Technologies](https://arango.ai/news/arangodb-intel-accelerating-graphml-with-advanced-processing-technologies/): SAN FRANCISCO, CA – December 4, 2023 – ArangoDB, a leader in graph database technology, has joined forces with Intel to harness the power of advanced processing technologies for Graph Machine Learning (GraphML). This collaboration revolves around the optimization of ArangoDB’s solutions with Intel's high-performance processors, notably the Xeon series, Flex series GPUs, and the Max series GPUs.
- [ArangoGraphML: Simplifying the Power of Graph Machine Learning](https://arango.ai/news/arangographml-simplifying-the-power-of-graph-machine-learning/): SAN FRANCISCO – October 11, 2023 – ArangoDB, the company behind the most complete and scalable graph data and analytics platform, announced the GA release of ArangoGraphML, a fully managed and intuitive graph machine learning platform.
- [ArangoDB Expands Executive Team with Chief Revenue Officer and Head of Engineering & Machine Learning](https://arango.ai/news/arangodb-expands-executive-team/): San Francisco and Cologne, Germany, August 14, 2019 – ArangoDB, the leading open-source native multi-model database, today announced it has appointed Matt Ekstrom to Chief Revenue Officer (CRO) and Jörg Schad to Head of Engineering and Machine Learning. Ekstrom will lead ArangoDB’s global sales efforts from San Francisco and Schad will focus on harmonizing development efforts that will continue product and technology innovation for the company.
- [ArangoDB Boosts Performance and Usability Across Search, Graph, and Analytics with Release of ArangoDB 3.11](https://arango.ai/news/arangodb-boosts-performance-and-usability-across-search-graph-and-analytics-with-release-of-arangodb-3-11/): SAN FRANCISCO – May 30, 2023 – ArangoDB, the company behind the most complete graph data and analytics platform, today announced the GA release of ArangoDB 3.11 to accelerate its performance across search, graph, and analytics use cases. ArangoDB 3.11 includes performance improvements for ArangoSearch, ArangoDB’s natively-integrated full-text search and ranking engine, as well as new functionality to its web interface to simplify the database’s operations.
- [CPO/CCO, Former CEO, and Co-Founder Claudius Weinberger Will Leave ArangoDB](https://arango.ai/news/cpo-cco-former-ceo-and-co-founder-claudius-weinberger-will-leave-arangodb/): SAN FRANCISCO – May 8, 2023 – Today, ArangoDB, the company behind the most complete graph data and analytics platform, announced that Chief Product Officer and Co-Founder Claudius Weinberger has decided to leave the company. As ArangoDB’s former CEO, Weinberger has played a major role in developing and steering the company. He will continue to be part of the Board of Directors and support the senior management team.
- [ArangoDB Further Strengthens Executive Team with New Chief Revenue Officer](https://arango.ai/news/arangodb-further-strengthens-executive-team-with-new-chief-revenue-officer/): SAN FRANCISCO – March 1, 2023 – ArangoDB, the company behind the most complete graph data and analytics platform, today announced Roman Bartik has joined the company as Chief Revenue Officer (CRO) to accelerate and scale the global business. Roman will leverage his track record of building successful businesses to lead ArangoDB’s global field organization.
- [ArangoDB Provides Graph Usability at Scale with Release of ArangoDB 3.10](https://arango.ai/news/arangodb-provides-graph-usability-at-scale-with-release-of-arangodb-3-10/): SAN FRANCISCO – October 4, 2022 – ArangoDB, the company behind the most complete graph data and analytics platform, today announced the GA release of ArangoDB 3.10 to provide graph usability at scale. ArangoDB 3.10 includes new features, such as EnterpriseGraph, ArangoSearch 2.0, and locality-sensitive hashing, that not only make it simpler for organizations to implement and use graph technology at scale, but also combine it with full-text search.
- [ArangoDB Appoints New Chief Executive Officer to Drive Next Phase of Growth](https://arango.ai/news/arangodb-appoints-new-chief-executive-officer-to-drive-next-phase-of-growth/): San Francisco and Cologne, Germany – March 2, 2022 – ArangoDB, the most scalable open source graph database, today announced it has appointed Shekhar Iyer as Chief Executive Officer; he will also join ArangoDB’s Board of Directors. Claudius Weinberger, ArangoDB co-founder, will assume the role of Chief Product Officer (CPO) going forward, and will continue to serve on the Board of Directors. Experienced global marketing executive James Smith has also joined ArangoDB as Chief Marketing Officer (CMO).
- [ArangoDB Announces $27.8 Million Series B Investment to Accelerate Development of Next-Generation Graph ML, Providing Advanced Analytics and AI Capabilities at Enterprise Scale](https://arango.ai/news/arangodb-announces-27-8-million-series-b-investment-to-accelerate-development-of-next-generation-graph-ml-providing-advanced-analytics-and-ai-capabilities-at-enterprise-scale/): San Francisco and Cologne, Germany – October 6, 2021 – ArangoDB, the most scalable open source graph database, today announced a $27.8 million Series B financing round led by Iris Capital, with participation from existing investors Bow Capital and Target Partners, alongside New Forge. Curt Gunsenheimer, Managing Partner at Iris Capital, and New Forge’s Mirko Novakovic, co-founder and former CEO of Instana, join ArangoDB’s board of directors. This Series B investment brings ArangoDB’s total financing to $47 million since its foundation.
- [ArangoDB Strengthens Executive Team with New Chief Revenue Officer and Chief Technology Officer](https://arango.ai/news/arangodb-strengthens-executive-team-with-new-chief-revenue-officer-and-chief-technology-officer/): San Francisco and Cologne, Germany – October 5, 2021 – ArangoDB, the most scalable open source graph database, today announced it has appointed Frank Swain as Chief Revenue Officer (CRO), as well as promoted Jörg Schad, Ph.D., to Chief Technology Officer (CTO). Swain will serve as the leader of ArangoDB’s global field organization, while Schad will continue to lead ArangoDB’s product development efforts.
- [ArangoDB Supports Analytics at Scale with Release of ArangoDB 3.8](https://arango.ai/news/arangodb-supports-analytics-at-scale-with-release-of-arangodb-3-8/): San Francisco and Cologne, Germany – July 29, 2021 – ArangoDB, the leading open source multi-model graph database, today announced the GA release of ArangoDB 3.8. ArangoDB 3.8 includes new graph query and search functionality, helping to meet increasing demand for businesses to perform graph-powered analytics at scale.
- [ArangoDB Named to Accel Open100 and Tracxn Emerging Startups 2020](https://arango.ai/news/arangodb-named-to-accel-open100-and-tracxn-emerging-startups-2020/): San Francisco and Cologne, Germany – November 12, 2020 – ArangoDB, the leading open source multi-model graph database, today announced it has been named to Accel’s inaugural Open100 list, as well as selected by Tracxn as a ‘Minicorn’ on its Emerging Startups 2020: Database Technology list.
- [ArangoDB Reaches Significant Graph and Search Milestones with Release of ArangoDB 3.7](https://arango.ai/news/arangodb-release-3-7/): San Francisco and Cologne, Germany – August 27, 2020 – ArangoDB, the leading open source multi-model graph database, today announced the GA release of ArangoDB 3.7. ArangoDB 3.7 introduces new graph and search features that improve the database’s capability to work with highly-connected data at any scale — furthering the company’s goal of providing the most flexible database on the market.
- [ArangoDB Achieves Red Hat OpenShift Operator Certification](https://arango.ai/news/arangodb-achieves-red-hat-openshift-operator-certification/): San Francisco and Cologne, Germany – May 28, 2020 – ArangoDB, the leading open source multi-model graph database, today announced its Kubernetes Operator has achieved Red Hat OpenShift Operator Certification. This certification provides Red Hat customers a fully-supported, multi-model graph database that can achieve a stateful deployment in minutes on Red Hat OpenShift, the industry’s most comprehensive enterprise Kubernetes platform.
- [ArangoDB Expands Machine Learning Offering with ArangoML Pipeline Cloud](https://arango.ai/news/arangoml-pipeline-cloud-press-release/): San Francisco and Cologne, Germany – January 30, 2020 – ArangoDB, the leading open source native multi-model database, today announced the release of ArangoML Pipeline Cloud, a fully-hosted, fully-managed common metadata layer for production-grade data science and Machine Learning (ML) platforms. ArangoML Pipeline Cloud runs on ArangoDB Oasis, ArangoDB’s recently released cloud service, and is the latest offering in ArangoDB’s ML extension, ArangoML.
- [ArangoDB Boosts Multi-Model Database Performance with Release of ArangoDB 3.6](https://arango.ai/news/arangodb-3-6-press-release/): San Francisco and Cologne, Germany – January 8, 2020 – ArangoDB, the leading open source native multi-model database, today announced the GA release of ArangoDB 3.6. ArangoDB 3.6 introduces OneShard, the ability to restrict individual databases to one node in a cluster, to ArangoDB’s Enterprise offering, and also includes major performance improvements that increase query speeds up to 30x faster.
- [ArangoDB Joins Cloud Native Computing Foundation](https://arango.ai/news/arangodb-joins-cloud-native-computing-foundation/): San Francisco and Cologne, Germany – November 20, 2019 – ArangoDB and the Cloud Native Computing Foundation (CNCF) announced today, that the leading open source multi-model database, ArangoDB, is now a member of the foundation behind some of the most popular open-source projects like Kubernetes, Prometheus, Fluentd or Rook.
- [ArangoDB Releases First Fully-Managed Multi-Model Data Platform with ArangoDB ArangoGraph](https://arango.ai/news/arangodb-oasis-press-release/): San Francisco and Cologne, Germany – November 14, 2019 – ArangoDB, the leading open source native multi-model database, today announced the availability of ArangoDB ArangoGraph, the first fully-hosted, managed and monitored native multi-model database service. ArangoDB ArangoGraph runs the full Enterprise version of ArangoDB, bringing all the benefits of native multi-model to the cloud, with additional features to solve enterprise-scale problems and keep data secure.
- [ArangoDB Extends Open Source Solution with ArangoML Pipeline; First Multi-Model Metadata Layer for Machine Learning Pipelines](https://arango.ai/news/arangoml-press-release/): San Francisco and Cologne, Germany – October 2, 2019 – ArangoDB, the leading open source native multi-model database, today announced the release of ArangoML Pipeline, the first multi-model metadata layer for Machine Learning (ML) pipelines, an open source project that provides a common metadata layer for production-grade data science and ML platforms. ArangoML Pipeline is the first offering in ArangoDB’s new extension, ArangoML.
- [ArangoDB Boosts Multi-Model Database Scalability Across Distributed Environments with Release of ArangoDB 3.5](https://arango.ai/news/arangodb-3-5-press-release/): San Francisco and Cologne, Germany – August 21 2019 – ArangoDB, the leading open source native multi-model database, today announced the GA release of ArangoDB 3.5. ArangoDB 3.5 contains new features that allow its growing customer base to more easily query, secure and search their growing data sets across multiple data models, making database scalability across distributed environments a reality.
- [ArangoDB Named to Database Trends and Applications 2019 “DBTA 100”](https://arango.ai/news/arangodb-named-to-database-trends-and-applications-2019-dbta-100/): San Francisco and Cologne, Germany – June 21, 2019 – ArangoDB, the leading open source native multi-model database, today announced that it has been included in the Database Trends and Applications (DBTA) magazine’s “DBTA 100: The Companies That Matter Most in Data,” a special list that salutes today’s top companies in data management and analysis. The annual DBTA 100 highlights the most important companies in data that are addressing the evolving demands for hardware, software, and services that help companies unlock their data’s potential.
- [ArangoDB secures $10 million Series A investment as global demand for its native multi-model database accelerates](https://arango.ai/news/arangodb-secures-10-million-investment/): San Francisco and Cologne, Germany – 14 March 2019 – ArangoDB the leading open source native multi-model database, today announced it has raised $10 million in a Series A financing led by Bow Capital, and has moved its headquarters to the U.S. to better service its fastest-growing market. As part of the investment, Bow Capital Advisor Murat Sönmez, a former EVP of Global Field Operations at TIBCO and Managing Director at the World Economic Forum, joins ArangoDB’s board of directors. Existing investor Target Partners also participated in the round, bringing ArangoDB’s total financing to $17 million.
- [ArangoDB 3.4 Introduces Native Search Engine and Full GeoJSON Support](https://arango.ai/news/introducing-arangodb-3-4/): San Francisco, CA, United States, December 6th, 2018 – ArangoDB, the leading open source native multi-model database, today announced the GA release of ArangoDB 3.4 – a transactional database solution which enables developers to efficiently interact with multiple data models by using just one technology and one query language. Major new enhancements in ArangoDB 3.4 include ArangoSearch, a feature which transforms ArangoDB, when combined with traversals or joins in AQL, from a data retrieval to an information retrieval solution; and full GeoJSON Support enabled by a Google S2 Geo Index library integration.
- [ArangoDB Among Highest Rated Operational Database Management Systems in Latest Gartner Report with 4.7/5 Rating](https://arango.ai/news/highest-rated-operational-database-management-systems-gartner-report/): SAN FRANCISCO, CA, UNITED STATES — August 28, 2018 — ArangoDB, the leading native multi-model database, today announced that it had been recognized as a vendor in the Customers’ Choice Zone in the Gartner Peer Insights ”Voice of the Customer”, as reviewed by customers.
- [Native Multi-Model ArangoDB Accelerates](https://arango.ai/news/native-multi-model-arangodb-accelerates/): SAN FRANCISCO, United States, August 1st, 2018 — ArangoDB, the leading native multi-model database announces today that it crossed the 6,000 stargazer milestone on Github and further extends its customer base.
- [ArangoDB Appoints Ted Dunning as Advisor](https://arango.ai/news/arangodb-appoints-ted-dunning-advisor/): Cologne, Germany – April 04, 2018 – ArangoDB, the leading native multi-model database, today announced the appointment of Ted Dunning to its advisory board. He will join Florian Leibert (CEO and Co-Founder of Mesosphere). Dunning succeeds Luca Olivari, who is now President of the company.
- [ArangoDB seeds 1.85M€ to build the Multi-Model DB for the Cloud](https://arango.ai/news/arangodb-seeds-investment/): Cologne, Germany – February 10, 2015 – ArangoDB announced today that the company has recently secured €1.85 million ($2.1m) funding from Machao Holding AG and an undisclosed investor to boost ArangoDB’s presence in the database market.

## Events
- [Beyond Vector Search: Building Trustworthy AI for Production](https://arango.ai/events/beyond-vector-search-building-trustworthy-ai-for-production/): If you've put a RAG pipeline into production, you've probably hit the same wall: vector search can tell you what looks similar, not how things are actually related. That's why teams building anything beyond basic Q&A need their agents to be able to reason across relationships and references. Hence they are quietly introducing a second data model, like a graph, alongside their vector store.
- [Nvidia GTC](https://arango.ai/events/nvidia-gtc/)
- [From Data to Decisions: The Contextual Data Layer for Enterprise AI](https://arango.ai/events/from-data-to-decisions-the-contextual-data-layer-for-enterprise-ai/): AI agents, assistants and applications are only as good as the data they can reason, decide and are only as effective as the context they can access.. But most enterprise AI architectures still rely on fragmented data spread across multiple systems—limiting an agent’s ability to reason, explain decisions, and act with business context.
- [Activate your ArangoDB for AI: Build Agents with Trusted Context](https://arango.ai/events/activate-your-arangodb-for-ai-build-agents-with-trusted-context/): Join Mark Milinkovich, Director of Product Marketing, and Daniel Morris, Solutions Engineer at Arango, for an exclusive session designed for ArangoDB customers.
- [How to Build Reliable, Context-Aware AI at Enterprise Scale](https://arango.ai/events/how-to-build-reliable-context-aware-ai-at-enterprise-scale/): Discover how leading organizations are creating AI systems that combine real-time enterprise knowledge, governance, and connected data to support intelligent decision-making and action.

## Use Cases
- [U.S. Federal Government](https://arango.ai/use-cases/us-federal-government/): Improve information, support mission analytics, and create a mission-ready data foundation.
- [Financial Services](https://arango.ai/use-cases/financial-services/): Reduce risk, detect fraud, and serve customers better.
- [Healthcare](https://arango.ai/use-cases/healthcare/): Better insights, better care, lower cost.
- [Telecommunications](https://arango.ai/use-cases/telecommunications/): Smarter networks, stronger security, better experiences.
- [High-Tech](https://arango.ai/use-cases/high-tech/): Simplify your stack and accelerate innovation.
- [Network Asset Management](https://arango.ai/use-cases/network-asset-management/): Keep critical infrastructure visible and resilient.
- [Enterprise Knowledge Graphs](https://arango.ai/use-cases/enterprise-knowledge-graphs/): Turn disconnected data into connected intelligence.
- [Enterprise Context Management](https://arango.ai/use-cases/enterprise-context-management/): Connect all your knowledge for accurate co-pilots.
- [360° View](https://arango.ai/use-cases/360-view/): See the full picture across customers, products, patients, or assets.
- [Supply Chain](https://arango.ai/use-cases/supply-chain/): Anticipate disruptions and optimize logistics.
- [Security, Risk & Compliance](https://arango.ai/use-cases/security-risk-compliance/): Protect systems, reduce risk, and prove compliance.
- [HybridRAG/GraphRAG](https://arango.ai/use-cases/hybrid-graphrag/): Add context, relationships and reasoning to RAG.
- [Chatbots/Agents/MCPs](https://arango.ai/use-cases/chatbots-agents-mcps/): Reliable assistants, powered by enterprise data.
- [AIOps & ITOps Co-pilots](https://arango.ai/use-cases/aiops-itops-co-pilots/): Smarter operations and faster recovery.
- [Analytical Co-pilots](https://arango.ai/use-cases/analytical-co-pilots/): Faster insights, grounded in connected data.

## About Arango (formerly known as ArangoDB)
Arango (legal entity: ArangoDB Inc.) is the company behind the Arango Contextual Data Platform for enterprise AI. The brand is Arango; the website is https://arango.ai.

ArangoDB is still the product name for our multi-model database — the contextual data foundation (graph, document, search, and vector in one engine). It powers the broader Arango Contextual Data Platform used by AI agents, assistants, and applications that need trusted business context to reason, decide, and act.

When people say "ArangoDB," they usually mean our database. When people say "Arango," they mean the company and the Contextual Data Platform built on that foundation. Docs and database references may still use ArangoDB; product marketing uses Arango.

## Key links
- [Product](https://arango.ai/products/contextual-data-platform/)
- [Documentation](https://docs.arangodb.com/)
- [Blog](https://arango.ai/blog/)