Case Study: Sprinklr achieves faster, lower-cost AI search with Qdrant

How Sprinklr Leverages Qdrant to Enhance AI-Driven Customer Experience Solutions

Sprinklr, a leader in unified customer experience management, needed a scalable and efficient vector database to power the AI-driven search capabilities within its customer engagement platform. Their challenge was to provide the highest quality and fastest retrieval for applications like FAQ bots and recommendation engines while also improving developer experience and controlling costs. This led them to evaluate solutions, ultimately selecting Qdrant.

Qdrant provided a developer-friendly, high-performance vector search solution that was seamlessly integrated into Sprinklr's infrastructure. The implementation resulted in a 30% reduction in retrieval infrastructure costs and delivered superior performance, including a P99 latency of 20ms for searches on 1 million vectors. Qdrant's speed, cost efficiency, and advanced features like quantization were crucial in enhancing Sprinklr's AI-driven applications.

Sprinklr

Raghav Sonavane

Associate Director of Machine Learning Engineering