Case Study: Alhena AI achieves faster ecommerce conversions with Qdrant

Alhena AI cuts vector retrieval latency by 90% with Qdrant

Alhena AI, an ecommerce AI agent provider, faced significant infrastructure challenges as they scaled. Their vector search layer was fragmented, relying on a complex combination of FAISS and Pinecone. This patchwork system lacked robust filtering, introduced latency, and required extensive custom routing logic, which slowed customer onboarding and reduced system reliability. To unify its AI stack and improve performance, Alhena AI sought a solution from vendor Qdrant.

By migrating entirely to Qdrant Cloud, Alhena AI replaced its multiple backends with a single, high-performance system. The solution provided hybrid search capabilities, advanced filtering, and real-time boosting, which led to more relevant product recommendations and improved conversion rates for their ecommerce clients. Qdrant reduced retrieval latency by up to 90%, enabling Alhena AI to meet strict SLAs. The vendor's multitenancy features also simplified scaling, allowing for faster deployments and a more streamlined infrastructure.

Alhena AI