Rename from ARAG to AIS

Author: Oxyjun

.github/CODEOWNERS

@@ -26,7 +26,7 @@
 /src/content/release-notes/workers-ai.yaml @kathayl @mchenco @kodster28 @cloudflare/pcx-technical-writing
 /src/content/release-notes/ai-gateway.yaml @kathayl @mchenco @kodster28 @cloudflare/pcx-technical-writing
 /src/content/release-notes/vectorize.yaml @elithrar @mchenco @sejoker @cloudflare/pcx-technical-writing
-/src/content/docs/autorag/ @rita3ko @irvinebroque @aninibread @cloudflare/pcx-technical-writing
+/src/content/docs/ai-search/ @rita3ko @irvinebroque @aninibread @cloudflare/pcx-technical-writing
 
 # Analytics & Logs
 

.github/workflows/publish-production.yml

@@ -67,7 +67,7 @@ jobs:
             --config bin/rclone.conf \
             distmd \
             zt:zt-dashboard-dev-docs
-      - name: Upload vendored Markdown files to AutoRAG DevDocs bucket
+      - name: Upload vendored Markdown files to AI Search DevDocs bucket
         env:
           AWS_ACCESS_KEY_ID: ${{ secrets.AUTORAG_DEVDOCS_ACCESS_KEY_ID }}
           AWS_SECRET_ACCESS_KEY: ${{ secrets.AUTORAG_DEVDOCS_SECRET_ACCESS_KEY }}

public/__redirects

@@ -245,8 +245,8 @@
 /api-shield/security/sequence-mitigation/configure/ /api-shield/security/sequence-mitigation/api/ 301
 
 #autorag
-/autorag/usage/recipes/ /autorag/how-to/ 301
-/autorag/configuration/metadata-filtering/ /autorag/configuration/metadata/ 301
+/autorag/usage/recipes/ /ai-search/how-to/ 301
+/autorag/configuration/metadata-filtering/ /ai-search/configuration/metadata/ 301
 
 # bots
 /bots/about/plans/ /bots/plans/ 301

src/assets/images/autorag/RAG.png renamed to src/assets/images/ai-search/RAG.png

@@ -0,0 +1,23 @@
+---
+title: Create fully-managed RAG pipelines for your AI applications with AI Search
+description: AI Search lets you create fully-managed, retrieval-augmented generation (RAG) pipelines that continuously updates and scales on Cloudflare.
+products:
+  - ai-search
+  - vectorize
+date: 2025-04-07
+---
+
+[AI Search](/ai-search/) is now in open beta, making it easy for you to build fully-managed retrieval-augmented generation (RAG) pipelines without managing infrastructure. Just upload your docs to [R2](/r2/get-started/), and AI Search handles the rest: embeddings, indexing, retrieval, and response generation via API.
+
+![AI Search open beta demo](~/assets/images/changelog/ai-search/autorag-open-beta.gif)
+
+With AI Search, you can:
+
+- **Customize your pipeline:** Choose from [Workers AI](/workers-ai) models, configure chunking strategies, edit system prompts, and more.
+- **Instant setup:** AI Search provisions everything you need from [Vectorize](/vectorize), [AI gateway](/ai-gateway), to pipeline logic for you, so you can go from zero to a working RAG pipeline in seconds.
+- **Keep your index fresh:** AI Search continuously syncs your index with your data source to ensure responses stay accurate and up to date.
+- **Ask questions:** Query your data and receive grounded responses via a [Workers binding](/ai-search/usage/workers-binding/) or [API](/ai-search/usage/rest-api/).
+
+Whether you're building internal tools, AI-powered search, or a support assistant, AI Search gets you from idea to deployment in minutes.
+
+Get started in the [Cloudflare dashboard](https://dash.cloudflare.com/?to=/:account/ai/autorag) or check out the [guide](/ai-search/get-started/) for instructions on how to build your RAG pipeline today.