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daniel-azoulai
2026-01-09 13:07:05 -08:00
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title: "How Flipkart built real-time multimodal fraud detection with Qdrant" title: "Building real-time multimodal similarity search in Flipkart Trust & Safety 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." short_description: "Tackling fraud and abuse with scalable similarity search."
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." description: "Tackling fraud and abuse with scalable similarity search."
preview_image: /blog/case-study-flipkart/social_preview_partnership-flipkart.png preview_image: /blog/case-study-flipkart/social_preview_partnership-flipkart.png
social_preview_image: /blog/case-study/social_preview_partnership-flipkart.png social_preview_image: /blog/case-study/social_preview_partnership-flipkart.png
date: 2026-01-09 date: 2026-01-09
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- case study - case study
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## Building real-time multimodal similarity search in Flipkart Trust & Safety with Qdrant
### Tackling fraud and abuse with scalable similarity search ### 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. 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.