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.

That decision creates a trap most teams don’t see coming until it’s expensive. Stitch together two specialized databases, or find one system built to serve both models natively? The first option looks fine in a demo. Months later, teams are debugging sync lag and quietly inconsistent answers, problems that don’t show up in anyone’s pitch deck, and that a true multi-model architecture removes by design rather than by patching around them.

In this session, you’ll get:

• A practical framework for evaluating your RAG architecture and spotting multi-database trade-offs before they hit production
• A clear look at where separate-store architectures break down in practice
• A hands-on way to test a multi-model approach yourself, to produce context-aware, more accurate, trusted responses

Context Engineered by Arango

At Arango, we believe scalable AI requires a contextual data foundation built for enterprise complexity. Join us to learn how leading organizations are building reliable, explainable, and production-ready AI systems with Arango’s Contextual Data Platform.