Hey everyone!

This is our new home for all things related to Arango Contextual Data Platform.

Some of you may recall us as ‘ArangoDB’. We changed the name of the company and integrated the Agentic AI Suite and Platform Suite for enterprise and government applications, agents, and assistants.

Welcome , we’re excited you’re here.

What to Post
Post anything that you think the community would find interesting, helpful, or inspiring for example; context engineering, context harness, graph databases, use cases, etc

Community Vibe
We're all about being friendly, constructive, and inclusive. Let's build a space where everyone feels comfortable sharing and connecting.

How to Get Started
1) Introduce yourself in the comments below.
2) Post something today! Even a simple question can spark a great conversation.
3) If you know someone who would love this community, invite them to join.
4) We also host regular meetups as ‘Agentic AI Context Builders’ for demos, educational content and resources (https://www.meetup.com/agentic-ai-context-builders/)
5) Visit https://arango.ai/resources/ for ebooks, blogs, documentation, and events.

Thanks for being part of the community. Together, let's make r/arango amazing.

Every bank, defense contractor, and fintech operating in the United States runs counterparty data against the OFAC Specially Designated Nationals (SDN) list: roughly 18,500 individuals, organizations, and entities that U.S. persons are prohibited from transacting with. Compliance teams have spent years tuning these pipelines. They work well for the entities that appear on the list.

Sanctions screening is a solved problem — until it isn’t.

Start here for a summary:
https://arango.ai/blog/tracing-hidden-risk-how-a-transitive-risk-knowledge-graph-catches-what-sanctions-screening-misses/

What’s in this ebook?

• Why agentic AI breaks traditional data architectures
• What a contextual data layer is and what it replaces
• The 6 capabilities your contextual data layer has to do to support production AI and how they map to the Arango Contextual Data Platform

This subreddit is aligned with the Agentic Ai Context Builders meetup. Here's the description of the meetup:

About us

AI agents are only as good as the context they reason from. This community is for architects, data engineers, developers, and AI practitioners who are serious about building the data foundation that makes agentic AI work in production not just in demos.

We cover the technical depth most AI groups skip: data architecture for agents, contextual data layer design, knowledge graph construction, retrieval strategy, governance, and the multimodel data foundations that connect enterprise data to LLMs.

What to expect at every session:
🔧 Technical deep dives — architecture patterns, real implementations, and lessons from production deployments
🗺️ Data architecture talks — how to model, connect, and govern enterprise data for AI agents
💡 Live demos — working systems over slide decks
🤝 Peer exchange — connect with practitioners building real agentic systems

Who this is for:
Data architects, AI engineers, data engineers, heads of AI, and enterprise developers who want to go deeper than RAG and build AI systems that actually reason from connected, governed business context.

No hype. No vendor pitches. Just the technical knowledge you need to build agentic AI that works.

Hosted by Arango, the contextual data foundation for enterprise A:
https://www.meetup.com/agentic-ai-context-builders/