Case Study: Kakao achieves faster internal support answers with Qdrant

Kakao cuts internal support response times to under 30 seconds with Qdrant

Kakao, a leading South Korean technology company, needed a faster and more reliable way for its employees to find answers about internal systems and procedures. Their challenge was searching across two very different types of data: long-form technical documentation and historical Q&A data. Pure keyword or pure vector search alone was insufficient. They required a solution that could handle both semantic questions and exact-term matching while meeting strict infrastructure requirements, leading them to evaluate vector databases including Qdrant.

Kakao implemented Qdrant as the core vector store for its AI-powered Service Desk Agent. Using Qdrant's hybrid search capabilities, which combine dense and sparse vectors with Reciprocal Rank Fusion, the system could accurately address both conceptual and exact-match queries. The result was a significant reduction in support staff workload and improved employee satisfaction with an average end-to-end response time under 30 seconds. The system now supports approximately 1 million vectors and Qdrant provided the operational stability and performance required for the project's success.

Kakao