diff --git a/examplecode/notebooks.mdx b/examplecode/notebooks.mdx
index bac46196..b5b24d36 100644
--- a/examplecode/notebooks.mdx
+++ b/examplecode/notebooks.mdx
@@ -6,6 +6,13 @@ description: "Notebooks contain complete working sample code for end-to-end solu
 ---
 
 <CardGroup cols={2}>
+    <Card title="Create a S3 to Qdrant Pipeline using the Unstructured API" href="https://colab.research.google.com/github/Unstructured-IO/notebooks/blob/main/notebooks/S3_to_Qdrant_Workflow_using_Unstructured_API.ipynb">
+    <br/>
+       This notebook walks through using the Unstructured Workflow Endpoint to set up a complete pipeline that pulls documents from S3, processes them using Unstructured, and stores the resulting embeddings in Qdrant for fast vector search and retrieval.
+    <br/>
+       ``Unstructured API`` ``Workflows`` ``S3`` ``Qdrant`` ``VLM`` ``Embeddings``
+    <br/>
+    </Card>
     <Card title="Create a S3 to MongoDB Pipeline using the Unstructured API" href="https://colab.research.google.com/github/Unstructured-IO/notebooks/blob/main/notebooks/S3_to_MongoDB_Workflow_using_Unstructured_API.ipynb">
     <br/>
        Learn how to build an end-to-end document processing pipeline that processes PDFs from S3 and stores structured results in MongoDB. Features VLM-powered partitioning, semantic chunking, and vector embeddings using the Unstructured Workflows API.