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2025-04-15 17:18:17 +02:00
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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."
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"
featured: true
@@ -18,7 +18,7 @@ tags:
---
# 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
@@ -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
![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.
@@ -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.
![NTT Architecture](/blog/case-study-Lyzr/NTT-Visual.png)
![NTT Architecture](/blog/case-study-lyzr/NTT-visual.png)
---
@@ -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.
![NPD Architecture](/blog/case-study-Lyzr/NPD-visual.png)
![NPD Architecture](/blog/case-study-lyzr/NPD-visual.png)
## Final Thoughts