Merge pull request #1197 from qdrant/qdrant-shakudo

[Blog] Shakudo Case Study

qdrant-landing/content/blog/case-study-kern.md

@@ -1,7 +1,7 @@
 ---
 draft: false
 title: "Kern AI & Qdrant: Precision AI Solutions for Finance and Insurance"
-short_description: "Transforming customer service in finance and insurance with vector search-based retrieval.</p>"
+short_description: "Transforming customer service in finance and insurance with vector search-based retrieval."
 description: "Revolutionizing customer service in finance and insurance by leveraging vector search for faster responses and improved operational efficiency."
 preview_image: /blog/case-study-kern/preview.png
 social_preview_image: /blog/case-study-kern/preview.png

qdrant-landing/content/blog/case-study-nyris.md

@@ -1,7 +1,7 @@
 ---
 draft: false
 title: "Nyris & Qdrant: How Vectors are the Future of Visual Search"
-short_description: "Transforming customer service in finance and insurance with vector search-based retrieval.</p>"
+short_description: "Transforming customer service in finance and insurance with vector search-based retrieval."
 description: "Revolutionizing customer service in finance and insurance by leveraging vector search for faster responses and improved operational efficiency."
 preview_image: /blog/case-study-nyris/preview.png
 social_preview_image: /blog/case-study-nyris/preview.png
@@ -34,7 +34,7 @@ During his time at Amazon, Lukasson observed that search engines like Google oft
 
 In their quest for the perfect visual search provider, Nyris ultimately decided to develop their own solution.
 
-## The Path to Vector-based Visual Search
+## The Path to Vector-Based Visual Search
 
 Initially in 2015, the team explored traditional search algorithms based on key value SIFT (Scale Invariant Feature Transform) features to locate specific elements within images. However, they quickly realized that these methods were imprecise and unreliable. To address this, Nyris began experimenting with the first Convolutional Neural Networks (CNNs) to extract embeddings for vector search.
 

qdrant-landing/content/blog/case-study-shakudo.md

@@ -0,0 +1,43 @@
+---
+draft: false
+title: "Qdrant and Shakudo: Secure & Performant Vector Search in VPC Environments"
+short_description: "Transforming customer service in finance and insurance with vector search-based retrieval."
+description: "Implementing vector search for enterprise AI via Qdrant's Hybrid Cloud integration into Shakudo’s virtual private cloud."
+preview_image: /blog/case-study-shakudo/preview.png
+social_preview_image: /blog/case-study-shakudo/preview.png
+date: 2024-09-23T00:02:00Z
+author: Qdrant
+featured: false
+tags:
+  - Shakudo
+  - Vector Search
+---
+
+We are excited to announce that Qdrant has partnered with [Shakudo](https://www.shakudo.io/), bringing [Qdrant Hybrid Cloud](https://qdrant.tech/hybrid-cloud/) to Shakudo’s virtual private cloud (VPC) deployments. This collaboration allows Shakudo clients to seamlessly integrate Qdrant’s high-performance vector database as a managed service into their private infrastructure, ensuring data sovereignty, scalability, and low-latency vector search for enterprise AI applications.
+
+## Data Sovereignty and Compliance with Secure Vector Search
+
+Shakudo’s VPC deployments ensure that client data remains within their infrastructure, providing strict control over sensitive information while leveraging a fully managed AI toolset. Qdrant Hybrid Cloud is tailored for environments where data privacy and regulatory compliance are paramount. It keeps the data plane inside the customer's infrastructure, with only essential telemetry shared externally, guaranteeing database isolation and security, while providing a fully managed service.
+
+![shakudo-case-study](/blog/case-study-shakudo/shakudo-case-study.jpg)
+
+## Scaling and Performance Optimization for Enterprise Vector Search
+
+Qdrant Hybrid Cloud is optimized for Kubernetes, allowing for fast, automated deployments and hands-off cluster management. Shakudo’s platform, designed for VPC-based environments, allows businesses to deploy Qdrant’s vector search clusters with no DevOps overhead. Qdrant’s ability to handle billions of vectors - powered by our customized Hierarchical Navigable Small World (HNSW) indexing - ensures real-time processing and high accuracy for AI-driven applications like semantic search, recommendation systems, and retrieval-augmented generation (RAG).
+
+## Staying Compatible with the Entire Stack
+
+By deploying Qdrant Hybrid Cloud on Shakudo, organizations gain immediate compatibility with their existing data sources, pipelines, and applications. It integrates seamlessly with the existing stack, ensuring smooth and efficient operation across all components. As business needs evolve, the data stack can easily scale and adapt to new demands.
+
+## Key Benefits of Qdrant in Shakudo's Virtual Private Cloud
+
+- **Data Privacy & Control**: Shakudo users can run a Qdrant vector database inside their own VPC, ensuring sensitive data never leaves their infrastructure, while enjoying a managed service for simplicity and reliability.
+- **Seamless Integration**: Qdrant’s Kubernetes-native setup allows rapid deployment on Shakudo’s VPC-based infrastructure, which provides pre-configured environments optimized for AI workloads.
+- **Scalability**: Qdrant’s ability to handle billions of vectors and its high-performance indexing like HNSW make it ideal for applications requiring fast, accurate similarity searches.
+- **Enterprise Flexibility**: With both on-premise and cloud-native setups available, this partnership offers businesses the flexibility to balance operational needs with privacy requirements​.
+
+## Learn More
+
+Ready to learn how Qdrant on Shakudo can enhance your AI infrastructure? Contact the Shakudo team to explore how they can help you deploy secure, high-performance vector search in your VPC environment, or get started [here](https://www.shakudo.io/integrations/qdrant).
+
+If you are interested in Qdrant’s Managed Cloud, Hybrid Cloud, or Private Cloud solutions for flexible deployment options for top-tier data privacy, [contact us](https://qdrant.tech/contact-sales/).