reorganize genai suite overview pages

Author: nerpaula

site/content/gen-ai/_index.md

@@ -2,22 +2,35 @@
 title: GenAI Suite
 menuTitle: GenAI Suite
 weight: 2
-layout: default
+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
 ---
 
-{{< cards >}}
+## What's included
 
-{{% card title="GraphRAG" link="graphrag/" %}}
-Arango's GenAI solution for generating knowledge graphs from documents
-and chatting with your data.
-{{% /card %}}
+The GenAI Suite is comprised of two major components:
 
-{{% card title="GraphML" link="graphml/" %}}
-Discover Arango's graph-powered machine learning features.
-{{% /card %}}
+- [**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.
 
-{{% card title="Graph Analytics" link="graph-analytics/" %}}
-Run algorithms such as PageRank on your graph data.
-{{% /card %}}
+Each component has an intuitive graphical user interface integrated into the
+Arango Data Platform web interface, guiding you through the process.
 
-{{< /cards >}}
+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](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 Arango Data Platform.
+- **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.

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

@@ -1,14 +1,13 @@
 ---
-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
 aliases:
   - data-science/overview
 ---
-- [Link to 3.12](../arangodb/3.12/aql/_index.md)
 
 {{< tip >}}
 The Arango Data Platform & GenAI Suite is available as a pre-release. To get
@@ -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
-Arango Data 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 Arango Data 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 Arango Data 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/graphrag/_index.md

@@ -56,5 +56,5 @@ relationships and connections becoming more sophisticated over time.
 - **[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](services/importer.md)**: Transform documents into knowledge graphs.
-- **[Retriever Service](services/retriever.md)**: Query and extract insights from your knowledge graphs.
+- **[Importer Service](../services/importer.md)**: Transform documents into knowledge graphs.
+- **[Retriever Service](../services/retriever.md)**: Query and extract insights from your knowledge graphs.

site/content/gen-ai/technical-overview.md renamed to site/content/gen-ai/graphrag/technical-overview.md

@@ -76,7 +76,7 @@ information in a structured graph format, allowing efficient querying and retrie
 3. Store the generated Knowledge Graph in the database for retrieval and reasoning.
 
 For detailed information about the service, see the
-[Importer](services/importer.md) service documentation.
+[Importer](../services/importer.md) service documentation.
 
 ### Extract information from the Knowledge Graph
 
@@ -87,7 +87,7 @@ You can extract information from Knowledge Graphs using two distinct methods:
 - Local retrieval
 
 For detailed information about the service, see the
-[Retriever](services/retriever.md) service documentation.
+[Retriever](../services/retriever.md) service documentation.
 
 #### Global retrieval
 
@@ -134,7 +134,7 @@ collection, and then it expands that subgraph over related entities, relations
 
 If you're working in an air-gapped environment or need to keep your data
 private, you can use the private LLM mode with 
-[Triton Inference Server](services/triton-inference-server.md).
+[Triton Inference Server](../services/triton-inference-server.md).
 
 This option allows you to run the service completely within your own
 infrastructure. The Triton Inference Server is a crucial component when