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.
This webinar will walk through the six key requirements for building a modern contextual data layer, including semantic clarity, graph-native relationships, temporal awareness, provenance and trust, AI-native services, and unified multimodel data.
You’ll leave with a clearer framework for designing AI architectures that support scalable agent workflows, explainable reasoning, and continuously connected enterprise context.
What you will learn:
- Why production AI breaks at scale: How fragmented context creates unreliable AI outcomes.
- The six requirements for enterprise AI: A framework for evaluating AI-ready architectures.
- Why relationships, time, and provenance matter: How graph-native context improves trust and explainability.
- How enterprises are rethinking the AI stack: Why organizations are consolidating fragmented infrastructure.
- How Arango powers production AI: Unified graph, vector, search, retrieval, and governance in one platform.
Innovate Together: Build the Future of Production AI
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.
Speakers
Ravi Marwaha
Chief Operating Officer & Chief Technology Product Officer
Arango
Mark Milinkovich
Director of Product Marketing
Arango