Beyond Vector Search: Building Trustworthy AI for Production

Beyond vector search, building trustworthy AI for production requires more than finding semantically similar information. In this webinar, Mark Malincovic, Head of Product Marketing at Arango, and Michael Hackstein, Chief Architect at Arango, explore how organizations can move beyond basic vector RAG and build AI systems that are more connected, explainable, and production-ready. The session compares vector RAG, GraphRAG, and more advanced RAG strategies, including hybrid, adaptive, and agentic approaches. Michael also explains the challenges that can emerge when multiple databases and retrieval systems are stitched together, including data consistency, synchronization, monitoring, governance, and maintenance. You will also see how Arango's AutoGraph and AutoRAG capabilities are designed to simplify this architecture by bringing graph, vector, document, and search capabilities together in a unified platform. What You'll Learn 🔍 Where vector RAG works well and where it falls short 🔗 How GraphRAG adds relationships and connected context to retrieval 🧠 How hybrid, adaptive, and agentic RAG strategies extend traditional retrieval 📖 Why citations, tracing, and explainability matter for trustworthy AI ⚙️ How fragmented AI stacks can introduce synchronization and data consistency challenges 🚀 How AutoGraph and AutoRAG can simplify the path from enterprise data to trusted AI responses Topics Covered Vector RAG GraphRAG Hybrid RAG Adaptive RAG Agentic RAG Knowledge Graphs Vector Search AI Governance AI Explainability AutoGraph AutoRAG Contextual Data Platform Production AI CHAPTERS 0:00 Intro: going beyond vector search 1:02 The problem: scattered documents your LLM can't see 3:13 Non-negotiables: citations, grounding, and governance 4:22 How vector RAG actually works 5:56 Multi-modal vector search and database requirements 6:41 Where vector RAG breaks: the contract alias problem 8:36 Why your prototype won't catch this failure 9:31 What is GraphRAG? 11:39 GraphRAG solves the contract question 12:20 Multi-modal GraphRAG and the NVIDIA VSS blueprint 13:44 Advanced RAG: hybrid, adaptive, and agentic strategies 15:39 Q&A: Can you use any embedding model with GraphRAG? 17:08 RAG strategies compared: vector to agentic 17:50 The hidden requirements: governance, skills, and TCO 20:07 The Frankenstack: what building it yourself looks like 22:52 AutoGraph and AutoRAG: the single-engine alternative 25:07 One query language, one engine 27:09 What breaks in production: distributed system failures 28:14 When one database lags behind: skewed answers 30:25 Single point of commit: guaranteed consistency 31:40 Why your backups may already be inconsistent 33:45 Q&A: Is AutoGraph part of ArangoDB or separate? 35:37 Poll: what blocks trust in AI answers 37:48 The 80/20 engineering effort problem 39:26 Resources and wrap-up Learn More 🌐 Website: https://arango.ai 📖 Documentation: https://docs.arango.ai 💻 GitHub: https://github.com/arangodb 🔗 LinkedIn: https://www.linkedin.com/company/arangodb/ 📅 Upcoming webinars and events: https://arango.ai/events/ Subscribe for more videos on Enterprise AI, Contextual AI, GraphRAG, Knowledge Graphs, AI Architecture, and building trustworthy AI systems. #EnterpriseAI #GraphRAG #VectorSearch #RAG #KnowledgeGraph #TrustedAI #ArangoDB #AIArchitecture