GenAI isn’t stuck because of models. It’s stuck because of the data layer. Teams juggle an OLTP database, a separate vector store, caches, and sync jobs, creating a “Frankenstack” that slows delivery and serves stale context.
This ebook reveals how four companies replaced that sprawl with a unified data layer that handles transactions, vectors, and even graph data. The result: simpler architectures, fresher context, and AI features that actually ship.
You’ll discover:
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- How Dify.AI consolidated eight data containers into one unified database cluster and scaled horizontally without re-architecture.
- Why TOPRISM mixed vector similarity and SQL filters in a single query to power fast, accurate conversational BI.
- How APTSell built a “digital sales employee” that orchestrates tasks over unified SQL, vector, and graph data.
- How a major retailer delivered GraphRAG at scale for 70% faster IT ticket resolution, 45% lower documentation costs, and 40% shorter training.
If you’re scaling agentic AI, start with the data foundation. Check out this ebook to see how a unified database turns GenAI from slideware into shipped software.