Building real-time multimodal similarity search in Flipkart Trust & Safety with Qdrant

The Trust & Safety team at Flipkart has significantly enhanced its ability to detect and prevent platform abuse and fraud by implementing a real-time multimodal similarity search system using the open-source vector database Qdrant. Previously reliant on a slower batch-processing method with HBase and Locality-Sensitive Hashing, the team faced challenges in handling high-dimensional data quickly enough to prevent fraudulent activities effectively. By choosing Qdrant for its efficient HNSW indexing and compatibility with Flipkart's infrastructure, they reduced detection times from nine hours to under a minute, enabling proactive fraud prevention. The new multi-tenant similarity service not only facilitates real-time image similarity checks for fraud detection but also supports other use cases like address clustering and retrieval-augmented generation for GenAI projects. The integration with Java gRPC SDK and Prometheus metrics has streamlined adoption and monitoring, while custom adapters ensure reusability across teams. Looking forward, the team plans to expand the use of Qdrant within Flipkart, standardizing it as a core component for various AI initiatives and exploring further automation with agentic AI frameworks.