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 | [Portable.io](/documentation/platforms/portable/)  | Cloud platform for developing and deploying ELT transformations.                         |
 | [PrivateGPT](/documentation/platforms/privategpt/) | Tool to ask questions about your documents using local LLMs emphasising privacy.         |
 | [Rivet](/documentation/platforms/rivet/)           | A visual programming environment for building AI agents with LLMs.                       |
+| [Vectorize](/documentation/platforms/vectorize/)   | Platform to automate data extraction, RAG evaluation, deploy RAG pipelines.              |
diff --git a/qdrant-landing/content/documentation/platforms/vectorize.md b/qdrant-landing/content/documentation/platforms/vectorize.md
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+---
+title: Vectorize
+---
+
+# Vectorize
+
+[Vectorize](https://vectorize.io/) is a SaaS platform that automates data extraction from [several sources](https://docs.vectorize.io/integrations/source-connectors) and lets you quickly deploy real-time RAG pipelines for your unstructured data. It also includes evaluation to help figure out the best strategies for the RAG system.
+
+Vectorize pipelines natively integrate with Qdrant by converting unstructured data into vector embeddings and storing them in a collection. When a pipeline is running, any new change in the source data is immediately processed, keeping the vector index up-to-date.
+
+## Prerequisites
+
+1. A Qdrant instance to connect to. You can get a free cloud instance at [cloud.qdrant.io](https://cloud.qdrant.io/).
+2. An account at [Vectorize.io](https://vectorize.io) for building those seamless pipelines.
+
+## Set Up
+
+- From the Vectorize dashboard, click `Vector Databases` -> `New Vector Database Integration` and select Qdrant.
+
+- Set up a connection using the hostname and API key of your Qdrant instance.
+
+<aside role="alert">
+  Don't include a port number in the host value.
+</aside>
+
+![Vectorize connection](/documentation/platforms/vectorize/vectorize-connection.png)
+
+- You can now select this Qdrant instance when setting up a [RAG pipeline](https://docs.vectorize.io/rag-pipelines/creating). Enter the name of the collection to use. It'll be created automatically if it doesn't exist.
+
+![Vectorize collection](/documentation/platforms/vectorize/vectorize-collection.png)
+
+- Select an embeddings provider.
+
+![Vectorize Embeddings](/documentation/platforms/vectorize/vectorize-embeddings.png)
+
+- Select a source from which to ingest data.
+
+![Vectorize Sources](/documentation/platforms/vectorize/vectorize-sources.png)
+
+Your Vectorize pipeline powered by Qdrant should now be up and ready to be scheduled and monitored.
+
+## Further Reading
+
+- Vectorize [Documentation](https://docs.vectorize.io)
+- Vectorize [Tutorials](https://docs.vectorize.io/tutorials/).
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