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daniel-azoulai
2026-01-09 13:07:05 -08:00
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---
draft: false
title: "How Flipkart built real-time multimodal fraud detection with Qdrant"
short_description: "Flipkart’s Trust & Safety team reduced fraud detection time from hours to minutes by moving from batch-based similarity search to real-time multimodal retrieval with Qdrant."
description: "Discover how Flipkart’s Trust & Safety team uses Qdrant to power real-time multimodal similarity search for fraud detection, address clustering, and internal RAG workloads—cutting detection time from 9 hours to under a minute."
title: "Building real-time multimodal similarity search in Flipkart Trust & Safety with Qdrant"
short_description: "Tackling fraud and abuse with scalable similarity search."
description: "Tackling fraud and abuse with scalable similarity search."
preview_image: /blog/case-study-flipkart/social_preview_partnership-flipkart.png
social_preview_image: /blog/case-study/social_preview_partnership-flipkart.png
date: 2026-01-09
@@ -19,8 +19,6 @@ tags:
- case study
---
## Building real-time multimodal similarity search in Flipkart Trust & Safety with Qdrant
### Tackling fraud and abuse with scalable similarity search
At Flipkart, the Trust & Safety team is focused on detecting and preventing platform abuse and fraud. A critical part of this work involves running large-scale similarity searches across customer and seller-submitted data, particularly images. This allows the team to identify patterns associated with fraudulent activity, such as repeat returns or duplicate seller claims, before they cause downstream harm.