Platform split | Reorganize Data Platform and AI content, and update product names

README.md

@@ -367,7 +367,7 @@ Inner shortcode
 Tags let you display badges, usually below a headline.
 
 This is mainly used for pointing out if a feature is only available in the
-GenAI Suite, the Data Platform, the Arango Managed Platform (AMP), or multiple
+AI Services, the Data Platform, the Arango Managed Platform (AMP), or multiple
 of them. See [Environment remarks](#environment-remarks) for details.
 
 It is also used for [Edition remarks](#edition-remarks) in content before
@@ -677,15 +677,15 @@ Pages and sections about features that are only available in certain environment
 such as in ArangoDB Shell should indicate where they are available using the
 `tag` shortcode.
 
-Features exclusive to the Data Platform, GenAI Data Platform,
+Features exclusive to the Data Platform, AI Services Data Platform,
 Arango Managed Platform (AMP), and ArangoDB generally don't need to be tagged
 because they are in dedicated parts of the documentation. However, if there are
 subsections with different procedures, each can be tagged accordingly.
 
-In the GenAI Data Platform only:
+In the AI Services Data Platform only:
 
 ```markdown
-{{< tag "GenAI Data Platform" >}}
+{{< tag "AI Services Data Platform" >}}
 ```
 
 In the Arango Managed Platform only:

site/content/ai-services/_index.md

@@ -0,0 +1,36 @@
+---
+title: AI Services
+menuTitle: AI Services
+weight: 2
+description: >-
+  A comprehensive AI solution that transforms your data into intelligent knowledge graphs with GraphRAG capabilities, applies advanced machine learning with GraphML, and provides enterprise-grade tools for analytics, natural language querying, and AI-powered insights, all through an intuitive web interface
+---
+
+## What's included
+
+AI Services are comprised of two major components:
+
+- [**GraphRAG**](./graphrag/_index.md): A complete solution for extracting entities
+  from text files to create a knowledge graph that you can then query with a
+  natural language interface.
+- [**GraphML**](./graphml/_index.md): Apply machine learning to graphs for link prediction,
+  classification, and similar tasks.
+
+Each component has an intuitive graphical user interface integrated into the
+Arango Data Platform web interface, guiding you through the process.
+
+Alongside these components, you also get the following additional features:
+
+- [**Graph Analytics**](graph-analytics.md): Run graph algorithms such as PageRank
+  on dedicated compute resources.
+- [**Jupyter notebooks**](notebook-servers.md): Run a Jupyter kernel in the platform for hosting
+  interactive notebooks for experimentation and development of applications
+  that use ArangoDB as their backend.
+- **Public and private LLM support**: Use public LLMs such as OpenAI
+  or private LLMs with [Triton Inference Server](reference/triton-inference-server.md).  
+- [**MLflow integration**](reference/mlflow.md): Use the popular MLflow as a model registry for private LLMs
+  or to run machine learning experiments as part of the Arango Data Platform.
+- **Application Programming Interfaces**: Use the underlying APIs of the
+  AI Services and build your own integrations. See the
+  [API reference](https://arangoml.github.io/platform-dss-api/GenAI-Service/proto/index.html) documentation
+  for more details.

site/content/gen-ai/graph-analytics.md → site/content/ai-services/graph-analytics.md

@@ -17,7 +17,7 @@ and network flow analysis.
 ArangoDB offers a feature for running algorithms on your graph data,
 called Graph Analytics Engines (GAEs). It is available on request for the
 [Arango Managed Platform (AMP)](https://dashboard.arangodb.cloud/home?utm_source=docs&utm_medium=cluster_pages&utm_campaign=docs_traffic)
-and included in the [ArangoDB Platform](../data-platform/about/_index.md).
+and included in the [Arango Data Platform](../data-platform/_index.md).
 
 Key features:
 
@@ -40,11 +40,11 @@ How to perform the steps is detailed in the subsequent sections.
 
 {{< tabs "platforms" >}}
 
-{{< tab "ArangoDB Platform" >}}
+{{< tab "Arango Data Platform" >}}
 1. Determine the approximate size of the data that you will load into the GAE
    and ensure the machine to run the engine on has sufficient memory. The data as well as the
    temporarily needed space for computations and results needs to fit in memory.
-2. [Start a `graphanalytics` service](#start-a-graphanalytics-service) via the GenAI service
+2. [Start a `graphanalytics` service](#start-a-graphanalytics-service) via the AI service
    that manages various Platform components for graph intelligence and machine learning.
    It only takes a few seconds until the engine service can be used. The engine
    runs adjacent to the pods of the ArangoDB Core.
@@ -88,9 +88,9 @@ Single server deployments using ArangoDB version 3.11 are not supported.
 
 {{< tabs "platforms" >}}
 
-{{< tab "ArangoDB Platform" >}}
-You can use any of the available authentication methods the ArangoDB Platform
-supports to start and stop `graphanalytics` services via the GenAI service as
+{{< tab "Arango Data Platform" >}}
+You can use any of the available authentication methods the Arango Data Platform
+supports to start and stop `graphanalytics` services via the AI service as
 well as to authenticate requests to the [Engine API](#engine-api).
 
 - HTTP Basic Authentication
@@ -129,30 +129,30 @@ setting in ArangoGraph:
 
 The interface for managing the engines depends on the environment you use:
 
-- **ArangoDB Platform**: [GenAI service](#genai-service)
+- **Arango Data Platform**: [AI service](#ai-service)
 - **ArangoGraph**: [Management API](#management-api)
 
-### GenAI service
+### AI service
 
-{{< tag "GenAI Data Platform" >}}
+{{< tag "AI Services Data Platform" >}}
 
-GAEs are deployed and deleted via the [GenAI service](services/gen-ai.md)
-in the ArangoDB Platform.
+GAEs are deployed and deleted via the [AI service](reference/gen-ai.md)
+in the Arango Data Platform.
 
 If you use cURL, you need to use the `-k` / `--insecure` option for requests
 if the Platform deployment uses a self-signed certificate (default).
 
 #### Start a `graphanalytics` service
 
-`POST <ENGINE_URL>/gen-ai/v1/graphanalytics`
+`POST <ENGINE_URL>/ai/v1/graphanalytics`
 
-Start a GAE via the GenAI service with an empty request body:
+Start a GAE via the AI service with an empty request body:
 
 ```sh
 # Example with a JWT session token
 ADB_TOKEN=$(curl -sSk -d '{"username":"root", "password": ""}' -X POST https://127.0.0.1:8529/_open/auth | jq -r .jwt)
 
-Service=$(curl -sSk -H "Authorization: bearer $ADB_TOKEN" -X POST https://127.0.0.1:8529/gen-ai/v1/graphanalytics)
+Service=$(curl -sSk -H "Authorization: bearer $ADB_TOKEN" -X POST https://127.0.0.1:8529/ai/v1/graphanalytics)
 ServiceID=$(echo "$Service" | jq -r ".serviceInfo.serviceId")
 if [[ "$ServiceID" == "null" ]]; then 
   echo "Error starting gral engine"
@@ -164,21 +164,21 @@ echo "$Service" | jq
 
 #### List the services
 
-`POST <ENGINE_URL>/gen-ai/v1/list_services`
+`POST <ENGINE_URL>/ai/v1/list_services`
 
-You can list all running services managed by the GenAI service, including the
+You can list all running services managed by the AI service, including the
 `graphanalytics` services:
 
 ```sh
-curl -sSk -H "Authorization: bearer $ADB_TOKEN" -X POST https://127.0.0.1:8529/gen-ai/v1/list_services | jq
+curl -sSk -H "Authorization: bearer $ADB_TOKEN" -X POST https://127.0.0.1:8529/ai/v1/list_services | jq
 ```
 
 #### Stop a `graphanalytics` service
 
-Delete the desired engine via the GenAI service using the service ID:
+Delete the desired engine via the AI service using the service ID:
 
 ```sh
-curl -sSk -H "Authorization: bearer $ADB_TOKEN" -X DELETE https://127.0.0.1:8529/gen-ai/v1/service/$ServiceID | jq
+curl -sSk -H "Authorization: bearer $ADB_TOKEN" -X DELETE https://127.0.0.1:8529/ai/v1/service/$ServiceID | jq
 ```
 
 ### Management API
@@ -309,12 +309,12 @@ curl -H "Authorization: bearer $ARANGO_GRAPH_TOKEN" -X DELETE "$BASE_URL/engines
 
 {{< tabs "platforms" >}}
 
-{{< tab "ArangoDB Platform" >}}
+{{< tab "Arango Data Platform" >}}
 To determine the base URL of the engine API, use the base URL of the Platform
 deployment and append `/gral/<SERVICE_ID>`, e.g.
 `https://127.0.0.1:8529/gral/arangodb-gral-tqcge`.
 
-The service ID is returned by the call to the GenAI service for
+The service ID is returned by the call to the AI service for
 [starting the `graphanalytics` service](#start-a-graphanalytics-service).
 You can also list the service IDs like so:
 

site/content/gen-ai/graph-to-ai.md → site/content/ai-services/graph-to-ai.md

@@ -1,17 +1,16 @@
 ---
-title: Generative Artificial Intelligence (GenAI) and Data Science
-menuTitle: GenAI & Data Science
+title: From Graph to AI
+menuTitle: From Graph to AI
 weight: 25
 description: >-
   ArangoDB's set of tools and technologies enables analytics, machine learning,
-  and GenAI applications powered by graph data
+  and AI applications powered by graph data
 aliases:
   - data-science/overview
 ---
-- [Link to 3.12](../arangodb/3.12/aql/_index.md)
 
 {{< tip >}}
-The ArangoDB Platform & GenAI Suite is available as a pre-release. To get
+The Arango Data Platform & AI Services are available as a pre-release. To get
 exclusive early access, [get in touch](https://arangodb.com/contact/) with
 the ArangoDB team.
 {{< /tip >}}
@@ -21,7 +20,7 @@ data science applications. The core database system includes multi-model storage
 of information with scalable graph and information retrieval capabilities that
 you can directly use for your research and product development.
 
-ArangoDB also offers a dedicated GenAI Suite, using the database core
+ArangoDB also offers dedicated AI Services, using the database core
 as the foundation for higher-level features. Whether you want to turbocharge
 generative AI applications with a GraphRAG solution or apply analytics and
 machine learning to graph data at scale, ArangoDB covers these needs.
@@ -33,45 +32,6 @@ engineering space can make use of ArangoDB's set of tools and technologies that
 enable analytics and machine learning on graph data. 
 -->
 
-## GenAI Suite
-
-The GenAI Suite is comprised of two major components:
-
-- [**GraphRAG**](#graphrag): A complete solution for extracting entities
-  from text files to create a knowledge graph that you can then query with a
-  natural language interface.
-- [**GraphML**](#graphml): Apply machine learning to graphs for link prediction,
-  classification, and similar tasks.
-
-Each component has an intuitive graphical user interface integrated into the
-ArangoDB Platform web interface, guiding you through the process.
-
-Alongside these components, you also get the following additional features:
-
-- [**Graph Visualizer**](../data-platform/graph-visualizer.md): A web-based tool for exploring your graph data with an
-  intuitive interface and sophisticated querying capabilities.
-- [**Jupyter notebooks**](notebook-servers.md): Run a Jupyter kernel in the platform for hosting
-  interactive notebooks for experimentation and development of applications
-  that use ArangoDB as their backend.
-- **Public and private LLM support**: Use public LLMs such as OpenAI
-  or private LLMs with [Triton Inference Server](services/triton-inference-server.md).  
-- [**MLflow integration**](services/mlflow.md): Use the popular MLflow as a model registry for private LLMs
-  or to run machine learning experiments as part of the ArangoDB Platform.
-- [**Adapters**](../ecosystem/adapters/_index.md): Use ArangoDB together with cuGraph, NetworkX,
-  and other data science tools. 
-- **Application Programming Interfaces**: Use the underlying APIs of the
-  GenAI Suite services and build your own integrations. See the
-  [API reference](https://arangoml.github.io/platform-dss-api/GenAI-Service/proto/index.html) documentation
-  for more details.
-
-## Other tools and features
-
-The ArangoDB Platform includes the following features independent of the
-GenAI Suite:
-
-- [**Graph Analytics**](graph-analytics.md): Run graph algorithms such as PageRank
-  on dedicated compute resources.
-
 ## From graph to AI
 
 This section classifies the complexity of the queries you can answer with

site/content/gen-ai/graphml/_index.md → site/content/ai-services/graphml/_index.md

@@ -8,7 +8,7 @@ aliases:
   - arangographml
 ---
 {{< tip >}}
-The ArangoDB Platform & GenAI Suite is available as a pre-release. To get
+The Arango Data Platform & AI Services are available as a pre-release. To get
 exclusive early access, [get in touch](https://arangodb.com/contact/) with
 the ArangoDB team.
 {{< /tip >}}

site/content/gen-ai/graphml/notebooks-api.md → site/content/ai-services/graphml/notebooks-api.md

@@ -9,7 +9,7 @@ aliases:
   - ../arangographml/getting-started
   - ../arangographml-getting-started-with-arangographml
 ---
-The ArangoDB Platform provides an easy-to-use & scalable interface to run
+The Arango Data Platform provides an easy-to-use & scalable interface to run
 Graph Machine Learning on ArangoDB data. Since all the orchestration and Machine Learning logic is
 managed by ArangoDB, all that is typically required are JSON specifications outlining
 individual processes to solve a Machine Learning task.
@@ -80,7 +80,7 @@ news sources, and locations are interconnected into a large graph.
 ![Example Event](../../images/ArangoML_open_intelligence_visualization.png)
 
 The [`arango-datasets`](../../arangodb/3.12/components/tools/arango-datasets.md) Python package
-allows you to load pre-defined datasets into ArangoDB Platform. It comes pre-installed in the
+allows you to load pre-defined datasets into Arango Data Platform. It comes pre-installed in the
 GraphML notebook environment.
 
 ```py

site/content/gen-ai/graphml/quickstart.md → site/content/ai-services/graphml/quickstart.md

@@ -3,14 +3,14 @@ title: How to get started with GraphML
 menuTitle: Quickstart
 weight: 5
 description: >-
-  You can use GraphML straight within the ArangoDB Platform, via the web interface
+  You can use GraphML straight within the Arango Data Platform, via the web interface
   or via Notebooks
 aliases:
   - ../arangographml/deploy  
 ---
 ## Web interface versus Jupyter Notebooks
 
-The ArangoDB Platform provides enterprise-ready Graph Machine Learning in two options,
+The Arango Data Platform provides enterprise-ready Graph Machine Learning in two options,
 tailored to suit diverse requirements and preferences: 
 - Using the web interface
 - In a scriptable manner, using the integrated Jupyter Notebooks and the HTTP API for GraphML
@@ -20,7 +20,7 @@ tailored to suit diverse requirements and preferences:
 {{< tabs "graphml-setup" >}}
 
 {{< tab "Web Interface" >}}
-The web interface of the ArangoDB Platform allows you to create, configure, and
+The web interface of the Arango Data Platform allows you to create, configure, and
 run a full machine learning workflow for GraphML. To get started, see the
 [Web interface for GraphML](ui.md) page.
 {{< /tab >}}

site/content/gen-ai/graphml/ui.md → site/content/ai-services/graphml/ui.md

@@ -1,5 +1,5 @@
 ---
-title: How to use GraphML in the ArangoDB Platform web interface
+title: How to use GraphML in the Arango Data Platform web interface
 menuTitle: Web Interface
 weight: 10
 description: >-
@@ -19,10 +19,10 @@ giving you a clear path from data to prediction:
 
 ## Create a GraphML project
 
-To create a new GraphML project using the ArangoDB Platform web interface, follow these steps:
+To create a new GraphML project using the Arango Data Platform web interface, follow these steps:
 
 1. From the left-hand sidebar, select the database where you want to create the project.
-2. In the left-hand sidebar, click **GenAI Suite** to open the GraphML project management interface, then click **Run GraphML**.
+2. In the left-hand sidebar, click **AI Services** to open the GraphML project management interface, then click **Run GraphML**.
    ![Create GraphML Project](../../images/create-graphml-project-ui.png)  
 3. In the **GraphML projects** view, click **Add new project**.
 4. The **Create ML project** modal opens. Enter a **Name** for your machine learning project.

site/content/ai-services/graphrag/_index.md

@@ -0,0 +1,60 @@
+---
+title: GraphRAG
+menuTitle: GraphRAG
+weight: 5
+description: >-
+  ArangoDB's GraphRAG solution combines graph-based retrieval-augmented generation
+  with Large Language Models (LLMs) for turbocharged AI solutions
+aliases:
+  llm-knowledge-graphs
+---
+{{< tip >}}
+The Arango Data Platform & AI Services are available as a pre-release. To get
+exclusive early access, [get in touch](https://arangodb.com/contact/) with
+the ArangoDB team.
+{{< /tip >}}
+
+## Transform unstructured documents into intelligent knowledge graphs
+
+ArangoDB's GraphRAG solution enables organizations to extract meaningful insights 
+from their document collections by creating knowledge graphs that capture not just 
+individual facts, but the intricate relationships between concepts across documents. 
+This approach goes beyond traditional RAG systems by understanding document 
+interconnections and providing both granular detail-level responses and high-level 
+conceptual understanding.
+
+- **Intelligent document understanding**: Automatically extracts and connects knowledge across multiple document sources
+- **Contextual intelligence**: Maintains relationships between concepts, enabling more accurate and comprehensive responses  
+- **Multi-level insights**: Provides both detailed technical answers and strategic high-level understanding
+- **Seamless knowledge access**: Natural language interface for querying complex document relationships
+
+## Key benefits for enterprise applications
+
+- **Cross-document relationship intelligence**:
+Unlike traditional RAG systems that treat documents in isolation, ArangoDB's GraphRAG 
+pipeline detects and leverages references between documents and chunks. This enables 
+more accurate responses by understanding how concepts relate across your entire knowledge base.
+
+- **Multi-level understanding architecture**:
+The system provides both detailed technical responses and high-level strategic insights 
+from the same knowledge base, adapting response depth based on query complexity and user intent.
+
+- **Reference-aware knowledge graph**:
+GraphRAG automatically detects and maps relationships between document chunks while 
+maintaining context of how information connects across different sources.
+
+- **Dynamic knowledge evolution**:
+The system learns and improves understanding as more documents are added, with 
+relationships and connections becoming more sophisticated over time.
+
+
+## What's next
+
+- **[GraphRAG Enterprise Use Cases](use-cases.md)**: Understand the business value through real-world scenarios.
+- **[GraphRAG Technical Overview](technical-overview.md)**: Dive into the architecture, services, and implementation details.
+- **[GraphRAG Web Interface](web-interface.md)**: Try GraphRAG using the interactive web interface.
+- **[GraphRAG Tutorial using integrated Notebook servers](tutorial-notebook.md)**: Follow hands-on examples and implementation guidance via Jupyter Notebooks.
+
+For deeper implementation details, explore the individual services:
+- **[Importer Service](../reference/importer.md)**: Transform documents into knowledge graphs.
+- **[Retriever Service](../reference/retriever.md)**: Query and extract insights from your knowledge graphs.