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@@ -5,7 +5,7 @@ short_description: "Lyzr scaled intelligent agents by upgrading to Qdrant."
description: "Discover how Lyzr improved latency, throughput, and infrastructure efficiency for its AI agents with Qdrant." description: "Discover how Lyzr improved latency, throughput, and infrastructure efficiency for its AI agents with Qdrant."
preview_image: /blog/case-study-lyzr/Social_Preview_Partnership-Lyzr.jpg preview_image: /blog/case-study-lyzr/Social_Preview_Partnership-Lyzr.jpg
social_preview_image: /blog/case-study-lyzr/Social_Preview_Partnership-Lyzr.jpg social_preview_image: /blog/case-study-lyzr/Social_Preview_Partnership-Lyzr.jpg
date: 2025-04-14T00:00:00Z date: 2025-04-15T00:00:00Z
author: "Daniel Azoulai" author: "Daniel Azoulai"
featured: true featured: true
@@ -18,7 +18,7 @@ tags:
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# How Lyzr Supercharged AI Agent Performance with Qdrant # How Lyzr Supercharged AI Agent Performance with Qdrant
![How Lyzr Supercharged AI Agent Performance with Qdrant](/blog/case-study-Lyzr/Case-Study-Lyzr-Summary-Dark.jpg) ![How Lyzr Supercharged AI Agent Performance with Qdrant](/blog/case-study-lyzr/case-study-lyzr-summary-dark.jpg)
## Scaling Intelligent Agents: How Lyzr Supercharged Performance with Qdrant ## Scaling Intelligent Agents: How Lyzr Supercharged Performance with Qdrant
@@ -30,7 +30,7 @@ This is how they rethought their stack and adopted Qdrant as the foundation for
## The Scaling Limits of Early Stack Choices ## The Scaling Limits of Early Stack Choices
![Lyzr-architecture](/blog/case-study-Lyzr/Lyzr-Architecture.png) ![Lyzr-architecture](/blog/case-study-lyzr/lyzr-architecture.png)
Lyzr’s architecture used Weaviate, with additional benchmarking on Pinecone. Initially, this setup was fine for development and controlled testing. The system managed around 1,500 vector entries, with a small number of agents issuing moderate query loads in a steady pattern. Lyzr’s architecture used Weaviate, with additional benchmarking on Pinecone. Initially, this setup was fine for development and controlled testing. The system managed around 1,500 vector entries, with a small number of agents issuing moderate query loads in a steady pattern.
@@ -97,7 +97,7 @@ One deployment, built for NTT Data, focused on automating IT change request work
After migrating to Qdrant, the difference was immediate. Retrieval accuracy improved substantially, even for long-tail queries. The system maintained high responsiveness under concurrent loads, and horizontal scaling became simpler—ensuring consistent performance as project demands evolved. After migrating to Qdrant, the difference was immediate. Retrieval accuracy improved substantially, even for long-tail queries. The system maintained high responsiveness under concurrent loads, and horizontal scaling became simpler—ensuring consistent performance as project demands evolved.
![NTT Architecture](/blog/case-study-Lyzr/NTT-Visual.png) ![NTT Architecture](/blog/case-study-lyzr/NTT-visual.png)
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@@ -107,7 +107,7 @@ Another example involved NPD, which deployed customer-facing agents across six w
Qdrant’s vector search enabled accurate, low-latency retrieval across thousands of entries. Even under increasing user traffic, the platform delivered consistent performance, eliminating the latency spikes experienced with previous solutions. Qdrant’s vector search enabled accurate, low-latency retrieval across thousands of entries. Even under increasing user traffic, the platform delivered consistent performance, eliminating the latency spikes experienced with previous solutions.
![NPD Architecture](/blog/case-study-Lyzr/NPD-visual.png) ![NPD Architecture](/blog/case-study-lyzr/NPD-visual.png)
## Final Thoughts ## Final Thoughts