What became possible in your product with Astra that was not practical before?
Maker
Roseram moved from rigid database scaling and complex vector infrastructure to instant, turn-key AI performance.
Before Astra, delivering real-time, highly personalized recommendations and intelligent data features required managing custom vector search pipelines alongside standard relational databases. It wasn't just expensive; it was operationally impractical.
Here is what Astra unlocked for Roseram:
1. Zero-Latency Hybrid Search at Scale
Before: Querying structured user preferences alongside unstructured vector embeddings meant orchestrating two separate databases, leading to high latency and synchronization headaches.
With Astra: Roseram leverages Astra’s native hybrid search (combining metadata filtering with high-dimensional vector search) in a single request, returning hyper-relevant recommendations in sub-50 milliseconds.
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