docs: Requested changes

2026-10-04 02:18:29 +02:00 · 2024-10-01 09:29:20 +05:30
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-title: "Qdrant and Sprinklr: Building a Platform for GenAI Customer Experience Management"
+title: "How Sprinklr Leveraged Qdrant to Build a GenAI Platform for Customer Experience Management"
 short_description: "Using vector search to power AI-driven tools for customer engagement."
 description: "Learn how Sprinklr uses vector search to power AI-driven tools for customer engagement, improving speed, cost-efficiency, and scalability"
 preview_image: /blog/case-study-sprinklr/preview.png
@@ -15,8 +15,6 @@ tags:

 ![case-study-shakudo-1](/blog/case-study-sprinklr/image5.png)

-## About Sprinklr
-
 [Sprinklr](https://www.sprinklr.com/), a leader in customer experience management, relies on cutting-edge technology to help global brands engage customers meaningfully across every channel. To achieve this, Sprinklr needed a robust platform that could support their AI-driven tools, particularly in handling the vast data requirements of customer interactions.

 Raghav Sonavane, Associate Director of Machine Learning Engineering at Sprinklr, leads the Applied AI team, focusing on Generative AI (GenAI) and Retrieval-Augmented Generation (RAG). His team is responsible for training and fine-tuning in-house models and deploying advanced retrieval and generation systems for customer-facing applications like FAQ bots and other [GenAI-driven services](https://www.sprinklr.com/blog/how-sprinklr-uses-RAG/). The team provides all of these capabilities in a centralized platform to the Sprinklr product engineering teams.
@@ -41,9 +39,9 @@ After evaluating several options, including Pinecone, Weaviate, and ElasticSearc

 - **Developer-Friendly Documentation:** “Qdrant’s clear [documentation](https://qdrant.tech/documentation/) enabled our team to integrate it quickly into our workflows,” notes Sonavane.
 - **High Customizability:** Qdrant provided Sprinklr with essential flexibility through high-level abstractions that allowed for extensive customizations. The diverse teams at Sprinklr, working on various GenAI applications, needed a solution that could adapt to different workloads. “The ability to fine-tune configurations at the collection level was crucial for our varied AI applications,” says Sonavane. Qdrant met this need by offering:
-    - **Configuration for high-speed search** that fine-tunes settings for optimal performance.
-    - [**Quantized vectors**](https://qdrant.tech/documentation/guides/quantization/) for high-dimensional data workloads
-    - [**Memory map**](https://qdrant.tech/documentation/concepts/storage/#configuring-memmap-storage) for efficient search optimizing memory usage.
+  - **Configuration for high-speed search** that fine-tunes settings for optimal performance.
+  - [**Quantized vectors**](https://qdrant.tech/documentation/guides/quantization/) for high-dimensional data workloads
+  - [**Memory map**](https://qdrant.tech/documentation/concepts/storage/#configuring-memmap-storage) for efficient search optimizing memory usage.
 - **Speed and Cost Efficiency:** Qdrant provided the best combination of speed and cost, making it the most viable solution for Sprinklr’s needs. “We needed a solution that wouldn’t just meet our performance requirements but also keep costs in check, and Qdrant delivered on both fronts,” says Sonavane.
 - **Enhanced Monitoring:** Qdrant’s monitoring tools further boosted system efficiency, allowing Sprinklr to maintain high performance across their platforms.