AI applications are moving from demos to production — and the biggest challenge isn’t the model. It’s context. Agents reason over long conversation histories. RAG pipelines combine vector embeddings with structured metadata. Multi-agent systems share state across workflows. Behind all of these patterns is the same requirement: a scalable, reliable way to store and retrieve context.
Most teams try to solve this by stitching together a vector database, a relational database, and a caching layer. But this fragmented approach leads to data duplication, sync issues, and growing operational complexity — and it’s where many AI projects break down on the path to production. What AI applications actually need is a context platform: a unified operational data layer that supports vector search, relational queries, and real-time analytics in a single system.
