@@ -6,13 +6,14 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: concept-article
-ms.date: 03/06/2026
+ms.date: 03/27/2026
 ms.author: lajanuar
 ms.custom: references_regions
 ---
-# Language supported regions
+<!-- markdownlint-disable MD025 -->
+# Azure Language region support
 
-The Language is available for use in several Azure regions. Use this article to learn about the regional support and limitations.
+The Azure Language features are available for use in several Azure regions. Use this article to learn about the regional support and limitations.
 
 ## Region support overview
 
@@ -161,6 +162,8 @@ Custom text classification is only available in some Azure regions. Some regions
 
 ## Summarization
 
+[!INCLUDE [availability](../summarization/includes/regional-availability.md)]
+
 |Region                |Text abstractive summarization|Conversation summarization               |
 |----------------------|------------------------------|-----------------------------------------|
 |AustraliaEast|✓|✓|

@@ -6,15 +6,19 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: overview
-ms.date: 12/05/2025
+ms.date: 03/30/2026
 ms.author: lajanuar
 ms.custom: language-service-clu
 ---
+<!-- markdownlint-disable MD025 -->
 # What is conversational language understanding?
 
-Conversational language understanding is one of the custom features offered by [Azure Language](../overview.md). It's a cloud-based API service that applies machine-learning intelligence to enable you to build natural language understanding component to be used in an end-to-end conversational application. 
+> [!IMPORTANT]
+> Conversational Language Understanding (CLU) is retiring from Azure Language effective **March 31, 2029**. After this date, the CLU feature is no longer supported.   During the support window, we recommend that users migrate existing workloads and direct all new projects to [Microsoft Foundry models](../../../foundry/concepts/foundry-models-overview.md), which offer enhanced capabilities for natural language understanding and can be easily integrated into your applications.
 
-Conversational language understanding (CLU) enables users to build custom natural language understanding models to predict the overall intention of an incoming utterance and extract important information from it. CLU only provides the intelligence to understand the input text for the client application and doesn't perform any actions. Developers can iteratively label utterances, train, and evaluate model performance before making it available for consumption by creating a CLU project. The quality of the labeled data greatly impacts model performance. To simplify building and customizing your model, the service offers a custom web portal that can be accessed through the [Microsoft Foundry](https://ai.azure.com/). You can easily get started with the service by following the steps in this [quickstart](quickstart.md). 
+Conversational language understanding is one of the custom features offered by [Azure Language](../overview.md). It's a cloud-based API service that applies machine-learning intelligence to enable you to build natural language understanding component to be used in an end-to-end conversational application.
+
+Conversational language understanding (CLU) enables users to build custom natural language understanding models to predict the overall intention of an incoming utterance and extract important information from it. CLU only provides the intelligence to understand the input text for the client application and doesn't perform any actions. Developers can iteratively label utterances, train, and evaluate model performance before making it available for consumption by creating a CLU project. The quality of the labeled data greatly impacts model performance. To simplify building and customizing your model, the service offers a custom web portal that can be accessed through the [Microsoft Foundry](https://ai.azure.com/). You can easily get started with the service by following the steps in this [quickstart](quickstart.md).
 
 This documentation contains the following article types:
 
@@ -27,7 +31,7 @@ This documentation contains the following article types:
 
 CLU can be used in multiple scenarios across various industries. Some examples are:
 
-### Multi-turn conversations 
+### Multi-turn conversations
 
 Use CLU with entity slot filling to enable natural, progressive information gathering across multiple conversation turns. Instead of overwhelming users with complex forms, your application can collect required details as they emerge naturally in dialogue. This approach is ideal for scenarios like booking systems, customer service workflows, or any application where complete information needs to be gathered through conversational exchanges.
 
@@ -70,15 +74,15 @@ Option 1 (LLM-powered quick deploy):
 
 2. **Deploy the model**: Deploying a model with the LLM-based training config makes it available for use via the Runtime API.
 
-3. **Predict intents and entities**: Use your custom model deployment to predict custom intents and prebuilt entities from user's utterances. 
+3. **Predict intents and entities**: Use your custom model deployment to predict custom intents and prebuilt entities from user's utterances.
 
 Option 2 (Custom machine learned model)
 
 Follow these steps to get the most out of your trained model:
 
 1. **Define your schema**: Know your data and define the actions and relevant information that needs to be recognized from user's input utterances. In this step, you create the [intents](glossary.md#intent) that you want to assign to user's utterances, and the relevant [entities](glossary.md#entity) you want extracted.
 
-2. **Label your data**: The quality of data labeling is a key factor in determining model performance. 
+2. **Label your data**: The quality of data labeling is a key factor in determining model performance.
 
 3. **Train the model**: Your model starts learning from your labeled data.
 
@@ -101,16 +105,16 @@ As you use CLU, see the following reference documentation and samples for Azure
 |C# (Runtime)    | [C# documentation](/dotnet/api/overview/azure/ai.language.conversations-readme)        | [C# samples](https://github.com/Azure/azure-sdk-for-net/tree/main/sdk/cognitivelanguage/Azure.AI.Language.Conversations/samples)        |
 |Python (Runtime)| [Python documentation](/python/api/overview/azure/ai-language-conversations-readme?view=azure-python-preview&preserve-view=true)        | [Python samples](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/cognitivelanguage/azure-ai-language-conversations/samples) |
 
-## Responsible AI 
+## Responsible AI
 
-An AI system includes the technology, the individuals who operate the system, the people who experience its effects, and the broader environment where the system functions all play a role. Read the transparency note for CLU to learn about responsible AI use and deployment in your systems. 
+An AI system includes the technology, the individuals who operate the system, the people who experience its effects, and the broader environment where the system functions all play a role. Read the transparency note for CLU to learn about responsible AI use and deployment in your systems.
 
 [!INCLUDE [Responsible AI links](../includes/overview-responsible-ai-links.md)]
 
 ## Next steps
 
-* Use the [quickstart article](quickstart.md) to start using conversational language understanding.  
+* Use the [quickstart article](quickstart.md) to start using conversational language understanding.
 
-* As you go through the project development lifecycle, review the [glossary](glossary.md) to learn more about the terms used throughout the documentation for this feature. 
+* As you go through the project development lifecycle, review the [glossary](glossary.md) to learn more about the terms used throughout the documentation for this feature.
 
 * Remember to view the [service limits](service-limits.md) for information such as [regional availability](service-limits.md#regional-availability).

@@ -1,18 +1,19 @@
 ---
-title: Custom named entity recognition - Foundry Tools
+title: What is the custom named entity recognition (CNER) feature in Azure Language?
 titleSuffix: Foundry Tools
 description: Customize an AI model to label and extract information from documents using Azure Language.
 author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: overview
-ms.date: 11/18/2025
+ms.date: 03/30/2026
 ms.author: lajanuar
 ms.custom: language-service-custom-ner
 ---
-# What is custom named entity recognition?
+<!-- markdownlint-disable MD025 -->
+# What is custom named entity recognition in Azure Language?
 
-Custom named entity recognition (NER) is a cloud-based API service that uses machine learning to help you build models designed for your unique entity recognition requirements. It's one of the specialized features available through [Azure Language in Foundry Tools](../overview.md). With custom NER, you can create AI models that extract domain-specific entities from unstructured text, such as contracts or financial documents. When you start a Custom NER project, you can repeatedly label data, train and evaluate your model, and improve its performance before deploying it. The quality of your labeled data is essential, as it directly impacts the model's accuracy.
+Custom named entity recognition (CNER) is an Azure Language [core capability](../overview.md#core-capabilities). The CNER feature is a cloud-based API service that uses machine learning to help you build models designed for your unique entity recognition requirements. You can create AI models that extract domain-specific entities from unstructured text, such as contracts or financial documents. When you start a custom CNER project, you can repeatedly label data, train and evaluate your model, and improve its performance before deploying it. The quality of your labeled data is essential, as it directly impacts the model's accuracy.
 
 To simplify building and customizing your model, the service offers a custom web platform that can be accessed through the [Microsoft Foundry](https://ai.azure.com/). You can easily get started with the service by following the steps in this [quickstart](quickstart.md).
 

@@ -7,17 +7,21 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: overview
-ms.date: 12/15/2025
+ms.date: 03/30/2026
 ms.author: lajanuar
 ms.custom: language-service-custom-classification
 ---
+<!-- markdownlint-disable MD025 -->
 # What is custom text classification?
 
-Custom text classification is one of the custom features offered by [Azure Language in Foundry Tools](../overview.md). It's a cloud-based API service that applies machine-learning intelligence to enable you to build custom models for text classification tasks. 
+> [!IMPORTANT]
+> Custom text classification is retiring from Azure Language effective **March 31, 2029**. After this date, the feature is no longer supported. During the support window, we recommend that users migrate existing workloads and direct all new projects to [Microsoft Foundry models](../../../foundry/concepts/foundry-models-overview.md), which offer enhanced capabilities for natural language understanding and can be easily integrated into your applications.
 
-Custom text classification enables users to build custom AI models to classify text into custom classes predefined by the user. By creating a custom text classification project, developers can iteratively label data, train, evaluate, and improve model performance before making it available for consumption. The quality of the labeled data greatly impacts model performance. To simplify building and customizing your model, the service offers a unified platform for building, managing, and deploying AI solutions that can be accessed through the [Microsoft Foundry](https://ai.azure.com/). You can easily get started with the service by following the steps in this [quickstart](quickstart.md). 
+Custom text classification is one of the custom features offered by [Azure Language in Foundry Tools](../overview.md). It's a cloud-based API service that applies machine-learning intelligence to enable you to build custom models for text classification tasks.
 
-Custom text classification supports two types of projects: 
+Custom text classification enables users to build custom AI models to classify text into custom classes predefined by the user. By creating a custom text classification project, developers can iteratively label data, train, evaluate, and improve model performance before making it available for consumption. The quality of the labeled data greatly impacts model performance. To simplify building and customizing your model, the service offers a unified platform for building, managing, and deploying AI solutions that can be accessed through the [Microsoft Foundry](https://ai.azure.com/). You can easily get started with the service by following the steps in this [quickstart](quickstart.md).
+
+Custom text classification supports two types of projects:
 
 * **Single label classification** - you can assign a single class for each document in your dataset. For example, a movie script could only be classified as "Romance" or "Comedy."
 * **Multi label classification** - you can assign multiple classes for each document in your dataset. For example, a movie script could be classified as "Comedy" or "Romance" and "Comedy."
@@ -42,7 +46,7 @@ Search is foundational to any app that surfaces text content to users. Common sc
 
 ## Project development lifecycle
 
-Creating a custom text classification project typically involves several different steps. 
+Creating a custom text classification project typically involves several different steps.
 
 :::image type="content" source="media/development-lifecycle.png" alt-text="The development lifecycle" lightbox="media/development-lifecycle.png":::
 
@@ -73,16 +77,16 @@ As you use custom text classification, see the following reference documentation
 | JavaScript (Runtime) | [JavaScript documentation](/javascript/api/overview/azure/ai-text-analytics-readme?view=azure-node-preview&preserve-view=true) | [JavaScript samples - Single label classification](https://github.com/Azure/azure-sdk-for-js/blob/%40azure/ai-text-analytics_6.0.0-beta.1/sdk/textanalytics/ai-text-analytics/samples/v5/javascript/customText.js) [JavaScript samples - Multi label classification](https://github.com/Azure/azure-sdk-for-js/blob/%40azure/ai-text-analytics_6.0.0-beta.1/sdk/textanalytics/ai-text-analytics/samples/v5/javascript/customText.js) |
 | Python (Runtime) | [Python documentation](/python/api/azure-ai-textanalytics/azure.ai.textanalytics?view=azure-python-preview&preserve-view=true) | [Python samples - Single label classification](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/textanalytics/azure-ai-textanalytics/samples/sample_single_label_classify.py) [Python samples - Multi label classification](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/textanalytics/azure-ai-textanalytics/samples/sample_multi_label_classify.py) |
 
-## Responsible AI 
+## Responsible AI
 
-An AI system includes not only the technology, but also the people who use it, the people affected by it, and the deployment environment. Read the [transparency note for custom text classification](/azure/ai-foundry/responsible-ai/language-service/custom-text-classification-transparency-note) to learn about responsible AI use and deployment in your systems. 
+An AI system includes not only the technology, but also the people who use it, the people affected by it, and the deployment environment. Read the [transparency note for custom text classification](/azure/ai-foundry/responsible-ai/language-service/custom-text-classification-transparency-note) to learn about responsible AI use and deployment in your systems.
 
 [!INCLUDE [Responsible AI links](../includes/overview-responsible-ai-links.md)]
 
 ## Next steps
 
-* Use the [quickstart article](quickstart.md) to start using custom text classification.  
+* Use the [quickstart article](quickstart.md) to start using custom text classification.
 
-* As you go through the project development lifecycle, review the [glossary](glossary.md) to learn more about the terms used throughout the documentation for this feature. 
+* As you go through the project development lifecycle, review the [glossary](glossary.md) to learn more about the terms used throughout the documentation for this feature.
 
 * Remember to view the [service limits](service-limits.md) for information such as regional availability.

@@ -6,14 +6,14 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: overview
-ms.date: 11/18/2025
+ms.date: 03/30/2026
 ms.author: lajanuar
 ms.custom: language-service-entity-linking
 ---
 # What is entity linking in Azure Language in Foundry Tools?
 
 > [!IMPORTANT]
-> Entity Linking is retiring from Azure Language in Foundry Tools effective **September 1, 2028**. After this date, the Entity Linking feature is no longer supported.   During the support window, we recommend that users migrate existing workloads and direct all new projects to Language [**Named Entity Recognition**](../named-entity-recognition/overview.md) or consider other alternative solutions.
+> Entity Linking is retiring from Azure Language in Foundry Tools effective **September 1, 2028**. After this date, the Entity Linking feature is no longer supported.   During the support window, we recommend that users migrate existing workloads and direct all new projects to Azure Language [**Named Entity Recognition**](../named-entity-recognition/overview.md) or [Foundry models](../../../foundry/concepts/foundry-models-overview.md), which offer enhanced capabilities for natural language understanding and can be easily integrated into your applications.
 
 
 Entity linking is one of the features offered by [Language](../overview.md), a collection of machine learning and AI algorithms in the cloud for developing intelligent applications that involve written language. Entity linking identifies and disambiguates the identity of entities found in text. For example, in the sentence "*We went to Seattle last week.*", the word "*Seattle*" would be identified, with a link to more information on Wikipedia.

@@ -6,12 +6,16 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: overview
-ms.date: 11/18/2025
+ms.date: 03/30/2026
 ms.author: lajanuar
 ms.custom: language-service-key-phrase
 ---
+<!-- markdownlint-disable MD025 -->
 # What is key phrase extraction in Azure Language in Foundry Tools?
 
+> [!IMPORTANT]
+> Key phrase extraction is retiring from Azure Language effective **March 31, 2029**. After this date, the feature is no longer supported. During the support window, we recommend that users migrate existing workloads and direct all new projects to [Microsoft Foundry models](../../../foundry/concepts/foundry-models-overview.md), which offer enhanced capabilities for natural language understanding and can be easily integrated into your applications.
+
 Key phrase extraction is one of the features offered by [Azure Language in Foundry Tools](../overview.md). This capability is part of a suite of cloud-based machine learning and AI tools designed for building intelligent applications that process written language. Use key phrase extraction to quickly identify the main concepts in text. For example, in the text "*The food was delicious and the staff were wonderful.*", key phrase extraction returns the main topics: "*food*" and "*wonderful staff*."
 
 This documentation contains the following types of articles:
@@ -37,5 +41,5 @@ An AI system includes the technology, the individuals who operate the system, th
 ## Next steps
 
 There are two ways to get started using the entity linking feature:
-* [Microsoft Foundry](../../../ai-foundry/what-is-foundry.md) is a web-based platform that lets you use several Language features without needing to write code.
+* [Microsoft Foundry](../../../foundry/what-is-foundry.md) is a web-based platform that lets you use several Language features without needing to write code.
 * The [quickstart article](quickstart.md) for instructions on making requests to the service using the REST API and client library SDK.  

@@ -1,18 +1,19 @@
 ---
-title: What is language detection in Azure Language in Foundry Tools?
+title: What is language detection in Azure Language?
 titleSuffix: Foundry Tools
 description: An overview of language detection in Azure Language, which helps you detect the language that text is written in by returning language codes.
 author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: overview
-ms.date: 11/18/2025
+ms.date: 03/30/2026
 ms.author: lajanuar
 ms.custom: language-service-language-detection
 ---
-# What is language detection in Azure Language in Foundry Tools?
+<!-- markdownlint-disable MD025 -->
+# What is language detection in Azure Language?
 
-Language detection is one of the features offered by [Azure Language in Foundry Tools](../overview.md), a collection of machine learning and AI algorithms in the cloud for developing intelligent applications that involve written language. Language detection is able to detect more than 100 languages in their primary script. In addition, the service offers [script detection](./how-to/call-api.md#script-name-and-script-code) for each detected language using  [ISO 15924 standard](https://wikipedia.org/wiki/ISO_15924) for a [select number of languages](./language-support.md#script-detection).
+Language detection is an Azure Language prebuilt [core capability](../overview.md#core-capabilities). Language detection can identify more than 100 languages in their primary script. In addition, the service offers [script detection](./how-to/call-api.md#script-name-and-script-code) for each detected language using [ISO 15924 standard](https://wikipedia.org/wiki/ISO_15924) for a [select number of languages](./language-support.md#script-detection).
 This documentation contains the following types of articles:
 
 * [**Quickstarts**](quickstart.md) are getting-started instructions to guide you through making requests to the service.
@@ -22,25 +23,25 @@ This documentation contains the following types of articles:
 
 * Language detection: For each document, returns the main language, its ISO 639-1 code, readable name, confidence score, script name, and ISO 15924 script code.
 
-* Script detection: To distinguish between multiple scripts used to write certain languages, such as Kazakh, language detection returns a script name and script code according to the ISO 15924 standard.  
+* Script detection: To distinguish between multiple scripts used to write certain languages, such as Kazakh, language detection returns a script name and script code according to the ISO 15924 standard.
 
 * Ambiguous content handling: To help disambiguate language based on the input, you can specify an ISO 3166-1 alpha-2 country/region code. For example, the word "communication" is common to both English and French. Specifying the origin of the text as France can help the language detection model determine the correct language.
 
 [!INCLUDE [Typical workflow for pre-configured language features](../includes/overview-typical-workflow.md)]
 
-
 ## Get started with language detection
 
 [!INCLUDE [development options](./includes/development-options.md)]
 
-## Responsible AI 
+## Responsible AI
 
-An AI system includes not only the technology, but also individuals who operate the system, people who experience its effects, and the broader environment where the system functions. Read the [transparency note for language detection](/azure/ai-foundry/responsible-ai/language-service/transparency-note-language-detection) to learn about responsible AI use and deployment in your systems. 
+An AI system includes not only the technology, but also individuals who operate the system, people who experience its effects, and the broader environment where the system functions. Read the [transparency note for language detection](/azure/ai-foundry/responsible-ai/language-service/transparency-note-language-detection) to learn about responsible AI use and deployment in your systems.
 
 [!INCLUDE [Responsible AI links](../includes/overview-responsible-ai-links.md)]
 
 ## Next steps
 
-There are two ways to get started using the entity linking feature:
+There are two ways to get started using the language detection feature:
+
 * [Microsoft Foundry](../../../ai-foundry/what-is-foundry.md) is a web-based platform that lets you use several Language features without needing to write code.
-* The [quickstart article](quickstart.md) for instructions on making requests to the service using the REST API and client library SDK.  
+* The [quickstart article](quickstart.md) for instructions on making requests to the service using the REST API and client library SDK.

@@ -1,18 +1,19 @@
 ---
-title: What is the Named Entity Recognition (NER) feature in Azure Language in Foundry Tools?
+title: What is the named entity recognition (NER) feature in Azure Language?
 titleSuffix: Foundry Tools
-description: An overview of the Named Entity Recognition feature in Azure Language, which helps you extract categories of entities in text.
+description: An overview of the named entity recognition feature in Azure Language, which helps you extract categories of entities in text.
 author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: overview
-ms.date: 11/18/2025
+ms.date: 03/30/2026
 ms.author: lajanuar
 ms.custom: language-service-ner
 ---
-# What is Named Entity Recognition (NER) in Azure Language in Foundry Tools?
+<!-- markdownlint-disable MD025 -->
+# What is named entity recognition (NER) in Azure Language?
 
-Named Entity Recognition (NER) is one of the features offered by [Azure Language in Foundry Tools](../overview.md), a collection of machine learning and AI algorithms in the cloud for developing intelligent applications that involve written language. The NER feature can identify and categorize entities in unstructured text. For example: people, places, organizations, and quantities. The prebuilt NER feature has a preset list of [recognized entities](concepts/named-entity-categories.md). The custom NER feature allows you to train the model to recognize specialized entities specific to your use case.
+Named entity recognition (NER) is an Azure Language prebuilt [core capability](../overview.md#core-capabilities). The NER feature can identify and categorize entities in unstructured text such as people, places, organizations, and quantities. The prebuilt NER feature has a preset list of [recognized entities](concepts/named-entity-categories.md). The custom NER feature allows you to train the model to recognize specialized entities specific to your use case.
 
 * [**Quickstarts**](quickstart.md) are getting-started instructions to guide you through making requests to the service.
 * [**How-to guides**](how-to-call.md) contain instructions for using the service in more specific or customized ways.

@@ -6,13 +6,16 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: overview
-ms.date: 01/17/2026
+ms.date: 03/30/2026
 ms.author: lajanuar
 ms.custom: language-service-orchestration
 ---
 <!-- markdownlint-disable MD025 -->
 # What is orchestration workflow?
 
+> [!IMPORTANT]
+> Orchestration workflow is retiring from Azure Language effective **March 31, 2029**. After this date, the orchestration workflow feature is no longer supported. During the support window, we recommend that users migrate existing workloads and direct all new projects to [Microsoft Foundry models](../../../foundry/concepts/foundry-models-overview.md), which offer enhanced capabilities for natural language understanding and can be easily integrated into your applications.
+
 Orchestration workflow is one of the features offered by [Azure Language in Foundry Tools](../overview.md). This cloud-based API service uses machine learning to facilitate the development of orchestration models that seamlessly integrate [Conversational Language Understanding (CLU)](../conversational-language-understanding/overview.md) and [Custom question Answering](../question-answering/overview.md) projects.
 Developers can create an orchestration workflow to iteratively tag utterances, train models, and evaluate their performance before deployment.
 

@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: overview
-ms.date: 11/18/2025
+ms.date: 03/30/2026
 ms.author: lajanuar
 ---
 
@@ -15,226 +15,204 @@ ms.author: lajanuar
 
 Azure Language is a cloud-based service that provides Natural Language Processing (NLP) features for understanding and analyzing text. Use this service to help build intelligent applications using the web-based Microsoft Foundry, REST APIs, and client libraries. For AI agent development, the service capabilities are also available as tools in Azure Language [MCP server](#azure-language-mcp-server), which is available both as a remote server in the **Microsoft Foundry Tool Catalog** and as a local server for self-hosted environments.
 
-## Available tools
+## Core capabilities
 
-Azure Language provides specialized tools that enable seamless integration between AI agents and language processing services through standardized protocols.
+**Recommended for new development**
 
-### Azure Language MCP server 
+Core capabilities are the primary, actively evolving features of Azure Language. These features receive ongoing investment and feature updates, and are recommended for new development and long-term planning. If you are starting a new project or designing a future architecture, use core capabilities as the foundation for your natural language processing workflows.
 
-The MCP (Model Context Protocol) server creates a standardized bridge that connects AI agents directly to Azure Language services through industry-standard protocols. This integration enables developers to build sophisticated conversational applications with reliable natural language processing capabilities while ensuring enterprise-grade compliance, data protection, and processing accuracy throughout their AI workflows. 
+* [Language detection](#language-detection)
+* **Named entity recognition (NER)**
+  * [Custom NER](#custom-ner)
+  * [Prebuilt NER](#prebuilt-ner)
+* [PII detection](#personally-identifiable-information-pii-detection)
+* [Text analytics for health](#text-analytics-for-health)
 
-Azure Language provides both remote and local MCP server options:
-* **Remote server**: Available through Foundry Tool Catalog for cloud-hosted deployments.
-* **Local server**: Available for developers who prefer to host the server in their own environment.
+> [!TIP]
+> Unsure which feature to use? See [Which Azure Language core feature should I use](#which-core-language-feature-should-i-use) to help you decide.
 
-For more information, *see* [Azure Language MCP server](concepts/foundry-tools-agents.md#azure-language-mcp-server-preview).
+[**Microsoft Foundry**](https://ai.azure.com/) enables you to use most of the following service features without the need to write code.
 
-## Available agents
+### Language detection
 
-Azure Language offers prebuilt agents that handle specific conversational AI scenarios with built-in governance, routing logic, and quality control mechanisms.
+[Language detection](./language-detection/overview.md) evaluates text and detects a wide range of languages and variant dialects.
 
-### Azure Language Intent Routing agent 
+:::image type="content" source="media/overview/language-detection.png" alt-text="A screenshot of language detection in Foundry." lightbox="media/overview/language-detection.png":::
 
-The Intent Routing agent intelligently manages conversation flows by understanding user intentions and delivering accurate responses in conversational AI applications. This agent uses predictable decision-making processes combined with controlled response generation to ensure consistent, reliable interactions that organizations can trust and monitor. 
+### Custom NER
 
-For more information, *see* [Azure Language Intent Routing agent](concepts/foundry-tools-agents.md#azure-language-intent-routing-agent-preview).
+[Custom named entity recognition (CNER)](custom-named-entity-recognition/overview.md) enables you to build custom AI models to extract custom entity categories (labels for words or phrases), using unstructured text that you provide.
 
-### Azure Language Exact Question Answering agent 
+:::image type="content" source="media/studio-examples/custom-named-entity-recognition.png" alt-text="A screenshot of a custom NER example." lightbox="media/studio-examples/custom-named-entity-recognition.png":::
 
-The Exact Question Answering agent provides reliable, word-for-word responses to your most important business questions. This agent automates frequently asked questions while maintaining human oversight and quality control to ensure accuracy and compliance.
+### Prebuilt NER
 
-For more information, *see* [Azure Language Exact Question Answering agent](concepts/foundry-tools-agents.md#azure-language-exact-question-answering-agent-preview).
+[Prebuilt named entity recognition (NER)](./named-entity-recognition/overview.md) identifies different entries in text and categorizes them into predefined types.
 
-## Available features
+:::image type="content" source="media/overview/named-entity-recognition.png" alt-text="A screenshot of named entity recognition in Foundry." lightbox="media/overview/named-entity-recognition.png":::
 
-> [!TIP]
-> Unsure which feature to use? See [Which Language feature should I use](#which-language-feature-should-i-use) to help you decide.
+### Personally identifiable information (PII) detection
 
-[**Microsoft Foundry**](https://ai.azure.com/) enables you to use most of the following service features without the need to write code.
+> [!IMPORTANT]
+> The Azure Language in Foundry Tools text personally identifiable information (PII) detection anonymization feature (synthetic replacement) is currently available in `preview` and licensed to you as part of your Azure subscription. Your use of this feature is subject to the terms applicable to **Previews** as described in the [Supplemental Terms of Use for Microsoft Azure Previews](https://azure.microsoft.com/support/legal/preview-supplemental-terms) and the [Microsoft Products and Services Data Protection Addendum (DPA)](https://www.microsoft.com/licensing/docs/view/microsoft-products-and-services-data-protection-addendum-dpa).
 
-### Named Entity Recognition (NER)
+[Personally identifiable information (PII) detection](./personally-identifiable-information/overview.md) identifies entities in text and conversations (chat or transcripts) that are associated with individuals.
 
-:::row:::
-   :::column span="":::
-      :::image type="content" source="media/overview/named-entity-recognition.png" alt-text="A screenshot of named entity recognition in Foundry." lightbox="media/overview/named-entity-recognition.png":::
-   :::column-end:::
-   :::column span="":::
-      [Named entity recognition](./named-entity-recognition/overview.md) identifies different entries in text and categorizes them into predefined types.
+***Conversation PII detection***
 
-   :::column-end:::
-:::row-end:::
+:::image type="content" source="media/overview/conversation-pii.png" alt-text="A screenshot of conversation personally identifying information in Foundry." lightbox="media/overview/conversation-pii.png":::
 
-### Personal and health data information detection
+***Text PII detection***
 
-> [!IMPORTANT]
-> The Azure Language in Foundry Tools Text Personally Identifiable Information (PII) detection anonymization feature (synthetic replacement) is currently available in `preview` and licensed to you as part of your Azure subscription. Your use of this feature is subject to the terms applicable to **Previews** as described in the [Supplemental Terms of Use for Microsoft Azure Previews](https://azure.microsoft.com/support/legal/preview-supplemental-terms) and the [Microsoft Products and Services Data Protection Addendum (DPA)](https://www.microsoft.com/licensing/docs/view/microsoft-products-and-services-data-protection-addendum-dpa).
+:::image type="content" source="media/overview/text-pii.png" alt-text="A screenshot of text personally identifying information in Foundry." lightbox="media/overview/text-pii.png":::
 
-:::row:::
-   :::column span="":::
-      :::image type="content" source="media/overview/conversation-pii.png" alt-text="A screenshot of conversation personally identifying information in Foundry." lightbox="media/overview/conversation-pii.png":::
-      :::image type="content" source="media/overview/text-pii.png" alt-text="A screenshot of text personally identifying information in Foundry." lightbox="media/overview/text-pii.png":::
-   :::column-end:::
-   :::column span="":::
+### Text analytics for health
 
-      [Personally Identifiable Information (PII) detection](./personally-identifiable-information/overview.md) identifies entities in text and conversations (chat or transcripts) that are associated with individuals.
+[Text analytics for health](./text-analytics-for-health/overview.md) extracts and labels relevant health information from unstructured text.
 
-   :::column-end:::
-:::row-end:::
+:::image type="content" source="media/overview/text-analytics-for-health.png" alt-text="A screenshot of text analytics for health in Foundry." lightbox="media/overview/text-analytics-for-health.png":::
 
-### Language detection
+## Legacy capabilities
 
-:::row:::
-   :::column span="":::
-      :::image type="content" source="media/overview/language-detection.png" alt-text="A screenshot of language detection in Foundry." lightbox="media/overview/language-detection.png":::
-   :::column-end:::
-   :::column span="":::
-      [Language detection](./language-detection/overview.md) evaluates text and detects a wide range of languages and variant dialects.
+**Supported for existing implementations**
 
-   :::column-end:::
-:::row-end:::
+Legacy capabilities are established features that provide a stable, supported base for existing workloads and scenarios. These features are supported for existing implementations and established use cases.
 
-### Sentiment Analysis and opinion mining
+* [Conversational language understanding](#conversational-language-understanding)
+* [Custom text classification](#custom-text-classification)
+* [Entity linking](#entity-linking)
+* [Key phrase extraction](#key-phrase-extraction)
+* [Orchestration workflow](#orchestration-workflow)
+* [Question answering](#question-answering)
+* [Sentiment analysis and opinion mining](#sentiment-analysis-and-opinion-mining)
+* [Summarization](#summarization)
 
-:::row:::
-   :::column span="":::
-      :::image type="content" source="media/overview/sentiment-analysis.png" alt-text="A screenshot of sentiment analysis in Foundry." lightbox="media/overview/sentiment-analysis.png":::
-   :::column-end:::
-   :::column span="":::
-      [Sentiment analysis and opinion mining](./sentiment-opinion-mining/overview.md) preconfigured features that help you understand public perception of your brand or topic. These features analyze text to identify positive or negative sentiments and can link them to specific elements within the text.
+### Conversational language understanding
 
-   :::column-end:::
-:::row-end:::
+[Conversational language understanding (CLU)](./conversational-language-understanding/overview.md) enables users to build custom natural language understanding models to predict the overall intention of an incoming utterance and extract important information from it.
 
-### Summarization
+:::image type="content" source="media/overview/conversational-language-understanding.png" alt-text="A screenshot of a conversational language understanding example." lightbox="media/overview/conversational-language-understanding.png":::
 
-:::row:::
-   :::column span="":::
-      :::image type="content" source="media/overview/conversation-summarization.png" alt-text="A screenshot of conversation summarization  in Foundry." lightbox="media/overview/conversation-summarization.png":::
-      :::image type="content" source="media/overview/call-center-summarization.png" alt-text="A screenshot of call center summarization in Foundry." lightbox="media/overview/call-center-summarization.png":::
-      :::image type="content" source="media/overview/text-summarization.png" alt-text="A screenshot of text summarization in Foundry." lightbox="media/overview/text-summarization.png":::
-   :::column-end:::
-   :::column span="":::
-      [Summarization](./summarization/overview.md) condenses information for text and conversations (chat and transcripts).
-      Text summarization generates a summary, supporting two approaches: Extractive summarization creates a summary by selecting key sentences from the document and preserving their original positions. In contrast, abstractive summarization generates a summary by producing new, concise, and coherent sentences or phrases that aren't directly copied from the original document.
-Conversation summarization recaps and segments long meetings into timestamped chapters. Call center summarization summarizes customer issues and resolution.
-   :::column-end:::
-:::row-end:::
+### Custom text classification
 
-### Key phrase extraction
+[Custom text classification](./custom-text-classification/overview.md) enables you to build custom AI models to classify unstructured text documents into custom classes you define. Creating a custom text classification project typically involves several different steps:
 
-:::row:::
-   :::column span="":::
-      :::image type="content" source="media/overview/key-phrase-extraction.png" alt-text="A screenshot of key phrase extraction in Foundry." lightbox="media/overview/key-phrase-extraction.png":::
-   :::column-end:::
-   :::column span="":::
-      [Key phrase extraction](./key-phrase-extraction/overview.md) is a preconfigured feature that evaluates and returns the main concepts in unstructured text, and returns them as a list.
-   :::column-end:::
-:::row-end:::
+:::image type="content" source="media/overview/text-classification.png" alt-text="A screenshot of a custom text classification example." lightbox="media/overview/text-classification.png":::
 
 ### Entity linking
 
-> [!IMPORTANT]
-> Entity Linking is retiring from Azure Language in Foundry Tools effective **September 1, 2028**. After this date, the Entity Linking feature is no longer supported.   During the support window, we recommend that users migrate existing workloads and direct all new projects to Azure Language [**Named Entity Recognition**](named-entity-recognition/overview.md) or consider other alternative solutions.
+[Entity linking](./entity-linking/overview.md) is a preconfigured feature that disambiguates the identity of entities (words or phrases) found in unstructured text and returns links to Wikipedia.
 
+:::image type="content" source="media/overview/entity-linking.png" alt-text="A screenshot of an entity linking example." lightbox="media/overview/entity-linking.png":::
 
-:::row:::
-   :::column span="":::
-      :::image type="content" source="media/studio-examples/entity-linking.png" alt-text="A screenshot of an entity linking example." lightbox="media/studio-examples/entity-linking.png":::
-   :::column-end:::
-   :::column span="":::
-      [Entity linking](./entity-linking/overview.md) is a preconfigured feature that disambiguates the identity of entities (words or phrases) found in unstructured text and returns links to Wikipedia.
-   :::column-end:::
-:::row-end:::
+### Key phrase extraction
 
-### Text analytics for health
+[Key phrase extraction](./key-phrase-extraction/overview.md) is a preconfigured feature that evaluates and returns the main concepts in unstructured text, and returns them as a list.
 
-:::row:::
-   :::column span="":::
-      :::image type="content" source="media/overview/text-analytics-for-health.png" alt-text="A screenshot of text analytics for health in Foundry." lightbox="media/overview/text-analytics-for-health.png":::
-   :::column-end:::
-   :::column span="":::
-      [Text analytics for health](./text-analytics-for-health/overview.md) Extracts and labels relevant health information from unstructured text.
-   :::column-end:::
-:::row-end:::
+:::image type="content" source="media/overview/key-phrase-extraction.png" alt-text="A screenshot of key phrase extraction in Foundry." lightbox="media/overview/key-phrase-extraction.png":::
 
-### Custom text classification
+### Orchestration workflow
 
-:::row:::
-   :::column span="":::
-      :::image type="content" source="media/studio-examples/single-classification.png" alt-text="A screenshot of a custom text classification example." lightbox="media/studio-examples/single-classification.png":::
-   :::column-end:::
-   :::column span="":::
-      [Custom text classification](./custom-text-classification/overview.md) enables you to build custom AI models to classify unstructured text documents into custom classes you define.
-   :::column-end:::
-:::row-end:::
+[Orchestration workflow](./orchestration-workflow/overview.md) is a custom feature that enables you to connect [conversational language understanding (CLU)](./conversational-language-understanding/overview.md) AND  [custom question answering (CQA)](./question-answering/overview.md) applications.
 
-### Custom Named Entity Recognition (Custom NER)
+:::image type="content" source="media/overview/orchestration-workflow.png" alt-text="A screenshot of an orchestration workflow example." lightbox="media/overview/orchestration-workflow.png":::
 
+### Question answering
 
-:::row:::
-   :::column span="":::
-      :::image type="content" source="media/studio-examples/custom-named-entity-recognition.png" alt-text="A screenshot of a custom NER example." lightbox="media/studio-examples/custom-named-entity-recognition.png":::
-   :::column-end:::
-   :::column span="":::
-      [Custom NER](custom-named-entity-recognition/overview.md) enables you to build custom AI models to extract custom entity categories (labels for words or phrases), using unstructured text that you provide.
-   :::column-end:::
-:::row-end:::
+[Question answering](./question-answering/overview.md) is a custom feature that identifies the most suitable answer for user inputs. This feature is typically utilized to develop conversational client applications, including social media platforms, chat bots, and speech-enabled desktop applications.
 
+:::image type="content" source="media/overview/question-answering.png" alt-text="A screenshot of a question answering example." lightbox="media/overview/question-answering.png":::
 
-### Conversational language understanding
+### Sentiment analysis and opinion mining
 
-:::row:::
-   :::column span="":::
-      :::image type="content" source="media/studio-examples/conversational-language-understanding.png" alt-text="A screenshot of a conversational language understanding example." lightbox="media/studio-examples/conversational-language-understanding.png":::
-   :::column-end:::
-   :::column span="":::
-      [Conversational language understanding (CLU)](./conversational-language-understanding/overview.md) enables users to build custom natural language understanding models to predict the overall intention of an incoming utterance and extract important information from it.
-   :::column-end:::
-:::row-end:::
+[Sentiment analysis and opinion mining](./sentiment-opinion-mining/overview.md) preconfigured features that help you understand public perception of your brand or topic. These features analyze text to identify positive or negative sentiments and can link them to specific elements within the text.
 
-### Orchestration workflow
+:::image type="content" source="media/overview/sentiment-analysis.png" alt-text="A screenshot of sentiment analysis in Foundry." lightbox="media/overview/sentiment-analysis.png":::
 
-:::row:::
-   :::column span="":::
-      :::image type="content" source="media/studio-examples/orchestration-workflow.png" alt-text="A screenshot of an orchestration workflow example." lightbox="media/studio-examples/orchestration-workflow.png":::
-   :::column-end:::
-   :::column span="":::
-      [Orchestration workflow](./language-detection/overview.md) is a custom feature that enables you to connect [Conversational Language Understanding (CLU)](./conversational-language-understanding/overview.md), [question answering](./question-answering/overview.md), and [LUIS](../LUIS/what-is-luis.md) applications.
+### Summarization
 
-   :::column-end:::
-:::row-end:::
+[Summarization](./summarization/overview.md) condenses information for text and conversations (chat and transcripts).
 
-### Question answering
+##### Conversation summarization
+
+Conversation summarization recaps and segments long meetings into timestamped chapters.
+
+:::image type="content" source="media/overview/conversation-summarization.png" alt-text="A screenshot of conversation summarization  in Foundry." lightbox="media/overview/conversation-summarization.png":::
+
+##### Call center summarization
+
+Call center summarization summarizes customer issues and resolution.
+
+:::image type="content" source="media/overview/call-center-summarization.png" alt-text="A screenshot of call center summarization in Foundry." lightbox="media/overview/call-center-summarization.png":::
 
-:::row:::
-   :::column span="":::
-      :::image type="content" source="media/studio-examples/question-answering.png" alt-text="A screenshot of a question answering example." lightbox="media/studio-examples/question-answering.png":::
-   :::column-end:::
-   :::column span="":::
-      [Question answering](./question-answering/overview.md) is a custom feature that identifies the most suitable answer for user inputs. This feature is typically utilized to develop conversational client applications, including social media platforms, chat bots, and speech-enabled desktop applications.
+##### Text summarization
 
-   :::column-end:::
-:::row-end:::
+Text summarization generates a summary, supporting two approaches:
 
-## Which Language feature should I use?
+* **Extractive summarization** creates a summary by selecting key sentences from the document and preserving their original positions.
+* **Abstractive summarization** generates a summary by producing new, concise, and coherent sentences or phrases that aren't directly copied from the original document.
 
-This section helps you decide which Language feature you should use for your application:
+   :::image type="content" source="media/overview/text-summarization.png" alt-text="A screenshot of text summarization in Foundry." lightbox="media/overview/text-summarization.png":::
 
-|What do you want to do?  |Document format  |Your best solution  | Is this solution customizable?* |
-|---------|---------|---------|---------|
+## Available tools
+
+Azure Language provides specialized tools that enable seamless integration between AI agents and language processing services through standardized protocols.
+
+### Azure Language MCP server
+
+The MCP (Model Context Protocol) server creates a standardized bridge that connects AI agents directly to Azure Language services through industry-standard protocols. This integration enables developers to build sophisticated conversational applications with reliable natural language processing capabilities while ensuring enterprise-grade compliance, data protection, and processing accuracy throughout their AI workflows.
+
+Azure Language provides both remote and local MCP server options:
+
+* **Remote server**: Available through Foundry Tool Catalog for cloud-hosted deployments.
+* **Local server**: Available for developers who prefer to host the server in their own environment.
+
+For more information, *see* [Azure Language MCP server](concepts/foundry-tools-agents.md#azure-language-mcp-server-preview).
+
+## Azure Language agents
+
+Azure Language offers prebuilt agents that handle specific conversational AI scenarios with built-in governance, routing logic, and quality control mechanisms.
+
+### Azure Language Intent Routing agent
+
+The Intent Routing agent intelligently manages conversation flows by understanding user intentions and delivering accurate responses in conversational AI applications. This agent uses predictable decision-making processes combined with controlled response generation to ensure consistent, reliable interactions that organizations can trust and monitor.
+
+For more information, *see* [Azure Language Intent Routing agent](concepts/foundry-tools-agents.md#azure-language-intent-routing-agent-preview).
+
+### Azure Language Exact Question Answering agent
+
+The Exact Question Answering agent provides reliable, word-for-word responses to your most important business questions. This agent automates frequently asked questions while maintaining human oversight and quality control to ensure accuracy and compliance.
+
+For more information, *see* [Azure Language Exact Question Answering agent](concepts/foundry-tools-agents.md#azure-language-exact-question-answering-agent-preview).
+
+## Which core Language feature should I use?
+
+This section helps you decide which core Language feature you should use for your application:
+
+| What do you want to do? | Document format | Your best solution | Is this solution customizable?* |
+| --------- | --------- | --------- | --------- |
 | Detect and/or redact sensitive information such as `PII` and `PHI`. | Unstructured text, <br> transcribed conversations | [PII detection](./personally-identifiable-information/overview.md) | |
-| Extract categories of information without creating a custom model.     | Unstructured text         | The [preconfigured NER feature](./named-entity-recognition/overview.md) |       |
+| Extract categories of information without creating a custom model. | Unstructured text | The [preconfigured NER feature](./named-entity-recognition/overview.md) | |
 | Extract categories of information using a model specific to your data. | Unstructured text | [Custom NER](./custom-named-entity-recognition/overview.md) | ✓ |
-|Extract main topics and important phrases.     | Unstructured text        | [Key phrase extraction](./key-phrase-extraction/overview.md) |   |
-| Determine the sentiment and opinions expressed in text. | Unstructured text | [Sentiment analysis and opinion mining](./sentiment-opinion-mining/overview.md) |  |
+| Extract medical information from clinical/medical documents, without building a model. | Unstructured text | [Text analytics for health](./text-analytics-for-health/overview.md) | |
+
+\* If a feature is customizable, you can train an AI model using our tools to fit your data specifically. Otherwise a feature is preconfigured, meaning the AI models it uses can't be changed. You just send your data, and use the feature's output in your applications.
+
+### Which legacy Language feature should I use?
+
+This section helps you decide which legacy Language feature you should use for your application:
+
+| What do you want to do? | Document format | Your best solution | Is this solution customizable?* |
+| --------- | --------- | --------- | --------- |
+| Extract main topics and important phrases. | Unstructured text | [Key phrase extraction](./key-phrase-extraction/overview.md) | |
+| Determine the sentiment and opinions expressed in text. | Unstructured text | [Sentiment analysis and opinion mining](./sentiment-opinion-mining/overview.md) | |
 | Summarize long chunks of text or conversations. | Unstructured text, <br> transcribed conversations. | [Summarization](./summarization/overview.md) | |
 | Disambiguate entities and get links to Wikipedia. | Unstructured text | [Entity linking](./entity-linking/overview.md) | |
-| Classify documents into one or more categories. | Unstructured text | [Custom text classification](./custom-text-classification/overview.md) | ✓|
-| Extract medical information from clinical/medical documents, without building a model. | Unstructured text | [Text analytics for health](./text-analytics-for-health/overview.md) | |
-| Build a conversational application that responds to user inputs. | Unstructured user inputs | [Question answering](./question-answering/overview.md) | ✓ |
+| Classify documents into one or more categories. | Unstructured text | [Custom text classification](./custom-text-classification/overview.md) | ✓ |
 | Detect the language that a text was written in. | Unstructured text | [Language detection](./language-detection/overview.md) | |
 | Predict the intention of user inputs and extract information from them. | Unstructured user inputs | [Conversational language understanding](./conversational-language-understanding/overview.md) | ✓ |
-| Connect apps from conversational language understanding, LUIS, and question answering. | Unstructured user inputs | [Orchestration workflow](./orchestration-workflow/overview.md) | ✓ |
+| Connect apps from conversational language understanding and custom question answering. | Unstructured user inputs | [Orchestration workflow](./orchestration-workflow/overview.md) | ✓ |
+| Build a conversational application that responds to user inputs. | Unstructured user inputs | [Question answering](./question-answering/overview.md) | ✓ |
 
 \* If a feature is customizable, you can train an AI model using our tools to fit your data specifically. Otherwise a feature is preconfigured, meaning the AI models it uses can't be changed. You just send your data, and use the feature's output in your applications.
 
@@ -258,13 +236,14 @@ You can find more code samples on GitHub for the following languages:
 * [Python](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/textanalytics/azure-ai-textanalytics/samples)
 
 ## Deploy on premises using Docker containers
+
 Use Language containers to deploy API features on-premises. These Docker containers enable you to bring the service closer to your data for compliance, security, or other operational reasons. The Language offers the following containers:
 
 * [Sentiment analysis](sentiment-opinion-mining/how-to/use-containers.md)
 * [Language detection](language-detection/how-to/use-containers.md)
 * [Key phrase extraction](key-phrase-extraction/how-to/use-containers.md)
-* [Custom Named Entity Recognition](custom-named-entity-recognition/how-to/use-containers.md)
-* [Text Analytics for health](text-analytics-for-health/how-to/use-containers.md)
+* [Custom named entity recognition](custom-named-entity-recognition/how-to/use-containers.md)
+* [Text analytics for health](text-analytics-for-health/how-to/use-containers.md)
 * [Summarization](summarization/how-to/use-containers.md)
 
 ## Responsible AI

@@ -8,6 +8,7 @@ ms.author: lajanuar
 ms.custom: language-service-pii
 ai-usage: ai-assisted
 ---
+
 ## Prerequisites
 
 > [!TIP]

@@ -1,20 +1,20 @@
 ---
-title: What is the Personally Identifying Information (PII) detection feature in Azure Language in Foundry Tools?
+title: What is the Personally Identifying Information (PII) detection feature in Azure Language?
 titleSuffix: Foundry Tools
 description: An overview of the PII detection feature in Azure Language, which helps you extract entities and sensitive information (PII) in text.
 author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: overview
-ms.date: 01/18/2026
+ms.date: 03/30/2026
 ms.author: lajanuar
 ms.custom: language-service-pii
 ---
 
 <!-- markdownlint-disable MD025 -->
-# What is Azure Language PII detection?
+# What is PII detection in Azure Language?
 
-Azure Language in Foundry Tools Personally Identifiable Information (PII) detection is a feature offered by [Azure Language](../overview.md). The PII detection service is a cloud-based API that utilizes machine learning and AI algorithms to help you develop intelligent applications with advanced natural language understanding. Azure Language PII detection uses Named Entity Recognition (NER) to **identify and redact** sensitive information from input data. The service classifies sensitive personal data into predefined categories. These categories include phone numbers, email addresses, and identification documents. This classification helps to efficiently detect and eliminate such information.
+Personally Identifiable Information (PII) detection is an Azure Language [core capability](../overview.md#core-capabilities). The PII detection service is a cloud-based API that uses machine learning to **identify and redact** sensitive information from input data. The service classifies sensitive personal data into predefined categories such as phone numbers, email addresses, and identification documents.
 
 > [!TIP]
 > Try PII detection [in Microsoft Foundry portal](https://ai.azure.com/). There you can [utilize a currently existing Language Studio resource or create a new Foundry resource](../../../ai-services/connect-services-foundry-portal.md).

@@ -7,12 +7,16 @@ author: laujan
 ms.author: lajanuar
 recommendations: false
 ms.topic: overview
-ms.date: 03/06/2026
+ms.date: 03/30/2026
 keywords: "low code chat bots, multi-turn conversations"
 ms.custom: language-service-question-answering
 ---
+<!-- markdownlint-disable MD025 -->
 # What is custom question answering?
 
+> [!IMPORTANT]
+> Custom question answering is retiring from Azure Language effective **March 31, 2029**. After this date, the CQA feature is no longer supported. During the support window, we recommend that users migrate existing workloads and direct all new projects to [Microsoft Foundry models](../../../foundry/concepts/foundry-models-overview.md), which offer enhanced capabilities for natural language understanding and can be easily integrated into your applications.
+
 Custom question answering (CQA) is a cloud-based Natural Language Processing (NLP) service that creates conversational AI applications over your data. Build knowledge bases from FAQs, manuals, and documents to deliver accurate answers through chat bots, virtual assistants, and interactive interfaces.
 
 ## Key capabilities

@@ -6,11 +6,15 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: overview
-ms.date: 11/18/2025
+ms.date: 03/30/2026
 ms.author: lajanuar
 ms.custom: language-service-sentiment-opinion-mining
 ---
-# What is sentiment analysis and opinion mining?
+<!-- markdownlint-disable MD025 -->
+# What are sentiment analysis and opinion mining?
+
+> [!IMPORTANT]
+> Sentiment analysis and opinion mining are retiring from Azure Language effective **March 31, 2029**. After this date, these features are no longer supported. During the support window, we recommend that users migrate existing workloads and direct all new projects to [Microsoft Foundry models](../../../foundry/concepts/foundry-models-overview.md), which offer enhanced capabilities for natural language understanding and can be easily integrated into your applications.
 
 Sentiment analysis and opinion mining are features offered by [Azure Language](../overview.md), a collection of machine learning and AI algorithms in the cloud for developing intelligent applications that involve written language. These features help you discover what people think about your brand or topic by analyzing text for signs of positive or negative sentiment. They can also link these sentiments to specific aspects of the text.
 
@@ -36,10 +40,10 @@ Opinion mining is a feature of sentiment analysis, also known as aspect-based se
 
 As you use sentiment analysis, see the following reference documentation and samples for Azure Language:
 
-|Development option / language  |Reference documentation |Samples  |
-|---------|---------|---------|
-|REST APIs (Authoring)   | [REST API documentation](https://aka.ms/ct-authoring-swagger)        |         |
-|REST APIs (Runtime)    | [REST API documentation](https://aka.ms/ct-runtime-swagger)        |         |
+| Development option / language | Reference documentation | Samples |
+| -- | -- | -- |
+| REST APIs (Authoring) | [REST API documentation](https://aka.ms/ct-authoring-swagger) | |
+| REST APIs (Runtime) | [REST API documentation](https://aka.ms/ct-runtime-swagger) | |
 
 ---
 

@@ -3,16 +3,17 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: include
-ms.date: 11/18/2025
+ms.date: 03/27/2026
 ms.author: lajanuar
 ms.custom:
   - references_regions
-  - ignite-2023
 ---
+
 > [!IMPORTANT]
 > Our preview region, Sweden Central, showcases our latest and continually evolving LLM fine tuning techniques based on GPT models. You're welcome to try them out with a Language resource in the Sweden Central region.
 >
 > Conversation summarization is only available using:
->  - REST API
->  - Python
->  - C#
+>
+> * REST API
+> * Python
+> * C#

@@ -6,13 +6,15 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: overview
-ms.date: 11/18/2025
+ms.date: 03/30/2026
 ms.author: lajanuar
 ms.custom: language-service-summarization, build-2024, ignite-2024
 ---
+<!-- markdownlint-disable MD025 -->
 # What is summarization?
 
-[!INCLUDE [availability](includes/regional-availability.md)]
+> [!IMPORTANT]
+> Summarization is retiring from Azure Language effective **March 31, 2029**. After this date, the summarization feature is no longer supported. During the support window, we recommend that users migrate existing workloads and direct all new projects to [Microsoft Foundry models](../../../foundry/concepts/foundry-models-overview.md), which offer enhanced capabilities for natural language understanding and can be easily integrated into your applications.
 
 Summarization is a feature offered by [Azure Language in Foundry Tools](../overview.md), a combination of generative Large Language models and task-optimized encoder models that offer summarization solutions with higher quality, cost efficiency, and lower latency.
 Use this article to learn more about this feature, and how to use it in your applications.
@@ -57,9 +59,10 @@ The text summarization API request is processed upon receipt of the request by c
 If we use the preceding example, the API might return these summaries:
 
 **Extractive summarization**:
-- "At Microsoft, we are on a quest to advance AI beyond existing techniques, by taking a more holistic, human-centric approach to learning and understanding."
-- "We believe XYZ-code enables us to fulfill our long-term vision: cross-domain transfer learning, spanning modalities and languages."
-- "The goal is to have pretrained models that can jointly learn representations to support a broad range of downstream AI tasks, much in the way humans do today."
+
+* "At Microsoft, we are on a quest to advance AI beyond existing techniques, by taking a more holistic, human-centric approach to learning and understanding."
+* "We believe XYZ-code enables us to fulfill our long-term vision: cross-domain transfer learning, spanning modalities and languages."
+* "The goal is to have pretrained models that can jointly learn representations to support a broad range of downstream AI tasks, much in the way humans do today."
 
 **Abstractive summarization**:
 - "Microsoft is taking a more holistic, human-centric approach to learning and understanding. We believe XYZ-code enables us to fulfill our long-term vision: cross-domain transfer learning, spanning modalities and languages. Over the past five years, we achieved human performance on benchmarks in conversational speech recognition."
@@ -102,8 +105,8 @@ As an example, consider the following example conversation:
 
 Conversation summarization feature would simplify the text as follows:
 
-|Example summary  | Remark | Conversation aspect |
-|---------|----|----|
+| Example summary | Remark | Conversation aspect |
+| ---------| ---- | ---- |
 | Customer is unable to set up wifi connection for Smart Brew 300 espresso machine |  a customer issue in a customer-and-agent conversation     | issue  |
 | The agent suggested several troubleshooting steps, including checking the wifi connection, checking the Contoso Coffee app, and performing a factory reset. However, none of these steps resolved the issue. The agent then put the customer on hold to look for another solution.   | solutions tried in a customer-and-agent conversation | resolution |
 | The customer contacted the agent for assistance with setting up a wifi connection for their Smart Brew 300 espresso machine. The agent guided the customer through several troubleshooting steps, including a wifi connection check, checking the power light, and a factory reset. Despite following these steps, the issue persisted. The agent then decided to explore other potential solutions | Summarizes a conversation into one paragraph | recap |
@@ -149,17 +152,17 @@ For more information, *see* [**Summarize native documents**](how-to/document-sum
 
 ## Input requirements and service limits
 
-# [Text summarization](#tab/text-summarization)
+### [Text summarization](#tab/text-summarization)
 
 * Summarization takes text for analysis. For more information, see [Data and service limits](../concepts/data-limits.md) in the how-to guide.
 * Summarization works with various written languages. For more information, see [language support](language-support.md?tabs=text-summarization).
 
-# [Conversation summarization](#tab/conversation-summarization)
+### [Conversation summarization](#tab/conversation-summarization)
 
 * Conversation summarization takes structured text for analysis. For more information, see [data and service limits](../concepts/data-limits.md).
 * Conversation summarization works with various spoken languages. For more information, see [language support](language-support.md?tabs=conversation-summarization).
 
-# [Document summarization](#tab/document-summarization)
+### [Document summarization](#tab/document-summarization)
 
 * Summarization takes text for analysis. For more information, see [Data and service limits](../concepts/data-limits.md) in the how-to guide.
 * Summarization works with various written languages. For more information, see [language support](language-support.md?tabs=document-summarization).

@@ -1,53 +1,54 @@
 ---
-title: What is the Text Analytics for health in Azure Language in Foundry Tools?
+title: What is Text analytics for health in Azure Language?
 titleSuffix: Foundry Tools
-description: An overview of Text Analytics for health in Azure Language, which helps you extract medical information from unstructured text, like clinical documents.
+description: An overview of Text analytics for health in Azure Language, which helps you extract medical information from unstructured text, like clinical documents.
 #services: cognitive-services
 author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: overview
-ms.date: 12/15/2025
+ms.date: 03/30/2026
 ms.author: lajanuar
 ms.custom: language-service-health
 ---
-# What is Text Analytics for health?
+<!-- markdownlint-disable MD025 -->
+# What is Text analytics for health in Azure Language?
 
 [!INCLUDE [service notice](includes/service-notice.md)]
 
-Text Analytics for health is one of the prebuilt features offered by [Azure Language in Foundry Tools](../overview.md). Text Analytics for health uses machine learning to identify and label medical information in unstructured text such as doctor's notes, clinical documents, and electronic health records. It extracts key data from sources like discharge summaries to support healthcare analysis.
+Text analytics for health is an Azure Language prebuilt [core capability](../overview.md#core-capabilities). Text analytics for health uses machine learning to identify and label medical information in unstructured text such as doctor's notes, clinical documents, and electronic health records. It extracts key data from sources like discharge summaries to support healthcare analysis.
 
 > [!TIP]
-> Try out Text Analytics for health [in Microsoft Foundry portal](https://ai.azure.com/). There you can [utilize a currently existing Language Studio resource or create a new Foundry resource](../../../ai-services/connect-services-foundry-portal.md) in order to use this service.
+> Try out Text analytics for health [in Microsoft Foundry portal](https://ai.azure.com/). There you can [utilize a currently existing Language Studio resource or create a new Foundry resource](../../../ai-services/connect-services-foundry-portal.md) in order to use this service.
 
 This documentation contains the following types of articles:
+
 * The [**quickstart article**](quickstart.md) provides a short tutorial that guides you with making your first request to the service.
 * The [**how-to guides**](how-to/call-api.md) contain detailed instructions on how to make calls to the service using the hosted API or using the on-premises Docker container.
 * The [**conceptual articles**](concepts/health-entity-categories.md) provide in-depth information on each of the service's features, named entity recognition, relation extraction, entity linking, and assertion detection.
 
-## Text Analytics for health features
+## Text analytics for health features
 
-Text Analytics for health performs four key functions, all with a single API call:
+Text analytics for health performs four key functions, all with a single API call:
 
 * Named entity recognition
 * Relation extraction
 * Entity linking
 * Assertion detection
 
-[!INCLUDE [Text Analytics for health](includes/features.md)]
-
-Text Analytics for health can receive unstructured text in English, German, French, Italian, Spanish, Portuguese, and Hebrew.
-
-Additionally, Text Analytics for health can return the processed output using the Fast Healthcare Interoperability Resources (FHIR) structure that enables the service's integration with other electronic health systems.
+[!INCLUDE [Text analytics for health](includes/features.md)]
 
+Text analytics for health can receive unstructured text in English, German, French, Italian, Spanish, Portuguese, and Hebrew.
 
+Additionally, Text analytics for health can return the processed output using the Fast Healthcare Interoperability Resources (FHIR) structure that enables the service's integration with other electronic health systems.
 
 > [!VIDEO https://learn.microsoft.com/Shows/AI-Show/Introducing-Text-Analytics-for-Health/player]
 
 ## Usage scenarios
 
-Text Analytics for health can be used in multiple scenarios across various industries.
-Some common customer motivations for using Text Analytics for health include:
+Text analytics for health can be used in multiple scenarios across various industries.
+Some common customer motivations for using Text analytics for health include:
+
 * Assisting and automating the processing of medical documents by proper medical coding to ensure accurate care and billing.
 * Increasing the efficiency of analyzing healthcare data to help drive the success of value-based care models similar to Medicare.
 * Minimizing healthcare provider effort by automating the aggregation of key patient data for trend and pattern monitoring.
@@ -63,22 +64,20 @@ Some common customer motivations for using Text Analytics for health include:
 |Review and report medical information|Potential medical information errors found during quality assurance reviews.|
 |Assist with decision support|Enable solutions that provide humans with assistive information relating to patients' medical information for faster and more reliable decisions.|
 
-## Get started with Text Analytics for health
+## Get started with Text analytics for health
 
 [!INCLUDE [Development options](./includes/development-options.md)]
 
-
 ## Input requirements and service limits
 
-Text Analytics for health is designed to receive unstructured text for analysis. For more information, see [data and service limits](../concepts/data-limits.md).
+Text analytics for health is designed to receive unstructured text for analysis. For more information, see [data and service limits](../concepts/data-limits.md).
 
-Text Analytics for health works with various input languages. For more information,  see [language support](language-support.md).
+Text analytics for health works with various input languages. For more information,  see [language support](language-support.md).
 
 [!INCLUDE [Developer reference](../includes/reference-samples-text-analytics.md)]
 
-
 ## Responsible use of AI
 
-An AI system includes the technology, the individuals who operate the system, the people who experience its effects, and the broader environment where the system functions all play a role. Read the [transparency note for Text Analytics for health](/azure/ai-foundry/responsible-ai/language-service/transparency-note-health) to learn about responsible AI use and deployment in your systems. 
+An AI system includes the technology, the individuals who operate the system, the people who experience its effects, and the broader environment where the system functions all play a role. Read the [transparency note for Text analytics for health](/azure/ai-foundry/responsible-ai/language-service/transparency-note-health) to learn about responsible AI use and deployment in your systems.
 
 [!INCLUDE [Responsible AI links](../includes/overview-responsible-ai-links.md)]

@@ -25,12 +25,262 @@ items:
   - name: Configure Azure resources
     href: conversational-language-understanding/how-to/configure-azure-resources.md
     displayName: configuration, fine-tuning, azure ai foundry, azure portal
-  - name: Migrate to Microsoft Foundry 
+  - name: Migrate to Microsoft Foundry
     href: migration-studio-to-foundry.md
     displayName: migrate, foundry, studio, permissions, roles, access, requisites, requirements
-- name: Azure Language in Foundry Tools capabilities
+- name: Core capabilities
   items:
 
+  - name: Language detection
+    items:
+    - name: Overview
+      href: language-detection/overview.md
+      displayName: language detection, language identification, automatic detection
+    - name: Quickstart
+      href: language-detection/quickstart.md
+    - name: Language support
+      href: language-detection/language-support.md
+    - name: Responsible use of AI
+      items:
+      - name: Transparency note for language detection
+        href: ../../ai-foundry/responsible-ai/language-service/transparency-note-language-detection.md
+        displayName: Transparency note for language detection, language detection transparency, Responsible AI, Responsible use of AI
+      - name: Integration and responsible use
+        href: ../../ai-foundry/responsible-ai/language-service/guidance-integration-responsible-use.md
+        displayName: Responsible deployment, Responsible use, Responsible integration, AI deployment, AI use
+      - name: Data, privacy, and security
+        href: ../../ai-foundry/responsible-ai/language-service/data-privacy.md
+        displayName: Data privacy, logging, data retention
+    - name: How-to guides
+      items:
+      - name: Call the API
+        href: language-detection/how-to/call-api.md
+      - name: Use containers
+        items:
+        - name: Use Docker containers
+          href: language-detection/how-to/use-containers.md
+        - name: Configure containers
+          href: concepts/configure-containers.md
+        - name: Use container instances
+          href: ../containers/azure-container-instance-recipe.md?context=/azure/ai-services/language-service/context/context
+        - name: Use containers in disconnected environments
+          href: ../containers/disconnected-containers.md
+        - name: Azure AI containers overview
+          href: ../cognitive-services-container-support.md
+
+  - name: Named Entity Recognition
+    items:
+    - name: Custom named entity recognition (CNER)
+      items:
+      - name: Overview
+        href: custom-named-entity-recognition/overview.md
+      - name: Quickstart
+        href: custom-named-entity-recognition/quickstart.md
+      - name: Language support
+        href: custom-named-entity-recognition/language-support.md
+      - name: FAQ
+        href: custom-named-entity-recognition/faq.md
+      - name: Glossary
+        href: custom-named-entity-recognition/glossary.md
+      - name: How-to guides
+        items:
+        - name: Create projects
+          href: custom-named-entity-recognition/how-to/create-project.md
+        - name: Data selection and schema design
+          href: custom-named-entity-recognition/how-to/design-schema.md
+        - name: Label data
+          href: custom-named-entity-recognition/how-to/tag-data.md
+        - name: Auto label your data (preview)
+          href: custom-named-entity-recognition/how-to/use-autolabeling.md
+        - name: Label data with Azure Machine Learning
+          href: custom/azure-machine-learning-labeling.md
+        - name: Train a model
+          href: custom-named-entity-recognition/how-to/train-model.md
+        - name: Model performance (preview)
+          href: custom-named-entity-recognition/how-to/view-model-evaluation.md
+        - name: Deploy a model
+          href: custom-named-entity-recognition/how-to/deploy-model.md
+        - name: Extract entities from text
+          href: custom-named-entity-recognition/how-to/call-api.md
+        - name: Back up and recover your models
+          href: custom-named-entity-recognition/fail-over.md
+        - name: Use CNER containers
+          items:
+          - name: Use CNER Docker containers
+            href: custom-named-entity-recognition/how-to/use-containers.md
+          - name: Configure containers
+            href: concepts/configure-containers.md
+          - name: Use container instances
+            href: ../containers/azure-container-instance-recipe.md?context=/azure/ai-services/language-service/context/context
+          - name: Azure AI containers overview
+            href: ../cognitive-services-container-support.md
+      - name: Concepts
+        items:
+          - name: Evaluation metrics
+            href: custom-named-entity-recognition/concepts/evaluation-metrics.md
+          - name: Accepted data formats
+            href: custom-named-entity-recognition/concepts/data-formats.md
+          - name: Deploy to multiple regions
+            href: concepts/custom-features/multi-region-deployment.md
+          - name: Project versioning
+            href: concepts/custom-features/project-versioning.md
+
+    - name: Prebuilt named entity recognition (NER)
+      items:
+      - name: Overview
+        href: named-entity-recognition/overview.md
+        displayName: named entity recognition, introduction, entity extraction
+      - name: Quickstart
+        href: named-entity-recognition/quickstart.md
+      - name: Language support
+        href: named-entity-recognition/language-support.md
+      - name: Responsible use of AI
+        items:
+        - name: Transparency note for NER
+          href: ../../ai-foundry/responsible-ai/language-service/transparency-note-named-entity-recognition.md
+          displayName: Transparency note for NER, Named Entity Recognition transparency, Responsible AI, Responsible use of AI
+        - name: Integration and responsible use
+          href: ../../ai-foundry/responsible-ai/language-service/guidance-integration-responsible-use.md
+          displayName: Responsible deployment, Responsible use, Responsible integration, AI deployment, AI use
+        - name: Data, privacy, and security
+          href: ../../ai-foundry/responsible-ai/language-service/data-privacy.md
+          displayName: Data privacy, logging, data retention
+      - name: How-to guides
+        items:
+        - name: Call NER
+          href: named-entity-recognition/how-to-call.md
+        - name: Use skill parameters
+          href: named-entity-recognition/how-to/skill-parameters.md
+        - name: Use containers
+          items:
+          - name: Use Docker containers
+            href: named-entity-recognition/how-to/use-containers.md
+          - name: Configure containers
+            href: concepts/configure-containers.md
+          - name: Use container instances
+            href: ../containers/azure-container-instance-recipe.md?context=/azure/ai-services/language-service/context/context
+          - name: Use containers in disconnected environments
+            href: ../containers/disconnected-containers.md
+          - name: Azure AI containers overview
+            href: ../cognitive-services-container-support.md
+      - name: Concepts
+        items:
+        - name: Recognized entity categories
+          href: named-entity-recognition/concepts/named-entity-categories.md
+        - name: Entity Metadata
+          href: named-entity-recognition/concepts/entity-metadata.md
+        - name: API version mapping
+          href: named-entity-recognition/concepts/ga-preview-mapping.md
+      - name: Tutorials
+        items:
+        - name: Extract information in Excel using Power Automate
+          href: named-entity-recognition/tutorials/extract-excel-information.md
+          displayName: excel integration, power automate, ner automation, extract entities
+
+  - name: Personally identifiable information (PII) recognition
+    items:
+    - name: Overview
+      href: personally-identifiable-information/overview.md
+      displayName: personally identifiable information, document
+    - name: Quickstart
+      href: personally-identifiable-information/quickstart.md
+    - name: Language support
+      href: personally-identifiable-information/language-support.md
+    - name: Responsible use of AI
+      items:
+      - name: Transparency note for PII
+        href: ../../ai-foundry/responsible-ai/language-service/transparency-note-personally-identifiable-information.md
+        displayName: Transparency note for PII, Personally Identifiable Information transparency, Responsible AI, Responsible use of AI
+      - name: Integration and responsible use
+        href: ../../ai-foundry/responsible-ai/language-service/guidance-integration-responsible-use.md
+        displayName: Responsible deployment, Responsible use, Responsible integration, AI deployment, AI use
+      - name: Data, privacy, and security
+        href: ../../ai-foundry/responsible-ai/language-service/data-privacy.md
+        displayName: Data privacy, logging, data retention
+    - name: How-to guides
+      items:
+      - name: Redact PII from text
+        href: personally-identifiable-information/how-to/redact-text-pii.md
+      - name: Redact PII from conversations
+        href: personally-identifiable-information/how-to/redact-conversation-pii.md
+      - name: Redact PII from native documents
+        href: personally-identifiable-information/how-to/redact-document-pii.md
+      - name:  Adapt PII to your domain
+        href: personally-identifiable-information/how-to/adapt-to-domain-pii.md
+      - name: Use Docker containers
+        items:
+          - name: Install and run containers
+            href: personally-identifiable-information/how-to/use-containers.md
+          - name: Configure containers
+            href: concepts/configure-containers.md
+          - name: Use container instances
+            href: ../containers/azure-container-instance-recipe.md?context=/azure/ai-services/language-service/context/context
+          - name: Use containers in disconnected environments
+            href: ../containers/disconnected-containers.md
+          - name: Azure AI containers overview
+            href: ../cognitive-services-container-support.md
+    - name: Recognized entity categories
+      items:
+      - name: PII in text
+        items:
+        - name: Extended format
+          href: personally-identifiable-information/concepts/entity-categories.md
+        - name: List format
+          href: personally-identifiable-information/concepts/entity-categories-list.md
+      - name: PII in conversations
+        items:
+        - name: Extended format
+          href: personally-identifiable-information/concepts/conversations-entity-categories.md
+        - name: List format
+          href: personally-identifiable-information/concepts/conversations-entities-list.md
+
+  - name: Text Analytics for health
+    items:
+    - name: Overview
+      href: text-analytics-for-health/overview.md
+      displayName: text analytics for health, healthcare nlp, medical text analysis, clinical text, health entities
+    - name: Quickstart
+      href: text-analytics-for-health/quickstart.md
+    - name: Language support
+      href: text-analytics-for-health/language-support.md
+    - name: Responsible use of AI
+      items:
+      - name: Transparency note for Text Analytics for health
+        href: ../../ai-foundry/responsible-ai/language-service/transparency-note-health.md
+        displayName: Transparency note for Text Analytics health, Text Analytics for health transparency, Responsible AI, Responsible use of AI
+      - name: Integration and responsible use
+        href: ../../ai-foundry/responsible-ai/language-service/guidance-integration-responsible-use.md
+        displayName: Responsible deployment, Responsible use, Responsible integration, AI deployment, AI use
+      - name: Data, privacy, and security
+        href: ../../ai-foundry/responsible-ai/language-service/data-privacy.md
+        displayName: Data privacy, logging, data retention
+    - name: How-to guides
+      items:
+      - name: How to call the API
+        href: text-analytics-for-health/how-to/call-api.md
+      - name: Use containers
+        items:
+        - name: Use Docker containers
+          href: text-analytics-for-health/how-to/use-containers.md
+        - name: Configure Docker containers
+          href: text-analytics-for-health/how-to/configure-containers.md
+        - name: Use container instances
+          href: ../containers/azure-container-instance-recipe.md?context=/azure/ai-services/language-service/context/context
+        - name: Azure AI containers overview
+          href: ../cognitive-services-container-support.md
+    - name: Concepts
+      items:
+      - name: Recognized entity categories
+        href: text-analytics-for-health/concepts/health-entity-categories.md
+      - name: Relation extraction
+        href: text-analytics-for-health/concepts/relation-extraction.md
+      - name: Assertion detection
+        href: text-analytics-for-health/concepts/assertion-detection.md
+      - name: Fast Healthcare Interoperability Resources structuring
+        href: text-analytics-for-health/concepts/fhir.md
+
+- name: Legacy capabilities
+  items:
   - name: "Conversational language understanding (CLU)"
     items:
     - name: Overview
@@ -41,7 +291,7 @@ items:
         - name: Get started
           href: conversational-language-understanding/quickstart.md
           displayName: getting started, tutorial, conversational ai setup
-        - name: Try multi-turn conversations 
+        - name: Try multi-turn conversations
           href: conversational-language-understanding/how-to/quickstart-multi-turn-conversations.md
           displayName: slot-filling, intent, entities, entity, association, foundry
     - name: Language support
@@ -62,7 +312,7 @@ items:
         href: /azure/ai-foundry/responsible-ai/clu/clu-data-privacy-security?context=/azure/ai-services/language-service/context/context
     - name: How-to guides
       items:
-        - name: Build a multi-turn conversation model 
+        - name: Build a multi-turn conversation model
           href:  conversational-language-understanding/how-to/build-multi-turn-model.md
           displayName: slot-filling, intent, entities, entity, association, foundry
         - name: Use containers
@@ -103,7 +353,7 @@ items:
           displayName: disaster recovery, failover, clu
     - name: Concepts
       items:
-        - name: Multi-turn conversations 
+        - name: Multi-turn conversations
           href:  conversational-language-understanding/concepts/multi-turn-conversations.md
           displayName: slot-filling, intent, entities, entity, association, foundry
         - name: Best practices
@@ -141,7 +391,7 @@ items:
       - name: Glossary
         href: conversational-language-understanding/glossary.md
 
-  - name: Custom question answering
+  - name: Custom question answering (CQA)
     items:
     - name: Overview
       href: question-answering/overview.md
@@ -166,7 +416,7 @@ items:
       - name: Create, test, and deploy a knowledge base
         href: question-answering/how-to/create-test-deploy.md
         displayName: knowledge base, test deploy
-      - name: Deploy a CQA agent 
+      - name: Deploy a CQA agent
         href: question-answering/how-to/deploy-agent.md
         displayName: virtual assistant, chatbot, knowledge base, deployment
       - name: Export/import/refresh
@@ -339,42 +589,7 @@ items:
         - name: How to call the API
           href: entity-linking/how-to/call-api.md
           displayName: entity linking, entity recognition
-  - name: Language detection
-    items:
-    - name: Overview
-      href: language-detection/overview.md
-      displayName: language detection, language identification, automatic detection
-    - name: Quickstart
-      href: language-detection/quickstart.md
-    - name: Language support
-      href: language-detection/language-support.md
-    - name: Responsible use of AI
-      items:
-      - name: Transparency note for language detection
-        href: ../../ai-foundry/responsible-ai/language-service/transparency-note-language-detection.md
-        displayName: Transparency note for language detection, language detection transparency, Responsible AI, Responsible use of AI
-      - name: Integration and responsible use
-        href: ../../ai-foundry/responsible-ai/language-service/guidance-integration-responsible-use.md
-        displayName: Responsible deployment, Responsible use, Responsible integration, AI deployment, AI use
-      - name: Data, privacy, and security
-        href: ../../ai-foundry/responsible-ai/language-service/data-privacy.md
-        displayName: Data privacy, logging, data retention
-    - name: How-to guides
-      items:
-      - name: Call the API
-        href: language-detection/how-to/call-api.md
-      - name: Use containers
-        items:
-        - name: Use Docker containers
-          href: language-detection/how-to/use-containers.md
-        - name: Configure containers
-          href: concepts/configure-containers.md
-        - name: Use container instances
-          href: ../containers/azure-container-instance-recipe.md?context=/azure/ai-services/language-service/context/context
-        - name: Use containers in disconnected environments
-          href: ../containers/disconnected-containers.md
-        - name: Azure AI containers overview
-          href: ../cognitive-services-container-support.md
+
 
   - name: Key phrase extraction
     items:
@@ -418,112 +633,7 @@ items:
         href: key-phrase-extraction/tutorials/integrate-power-bi.md
         displayName: business intelligence, data visualization
 
-  - name: Named Entity Recognition (prebuilt)
-    items:
-    - name: Overview
-      href: named-entity-recognition/overview.md
-      displayName: named entity recognition, introduction, entity extraction
-    - name: Quickstart
-      href: named-entity-recognition/quickstart.md
-    - name: Language support
-      href: named-entity-recognition/language-support.md
-    - name: Responsible use of AI
-      items:
-      - name: Transparency note for NER
-        href: ../../ai-foundry/responsible-ai/language-service/transparency-note-named-entity-recognition.md
-        displayName: Transparency note for NER, Named Entity Recognition transparency, Responsible AI, Responsible use of AI
-      - name: Integration and responsible use
-        href: ../../ai-foundry/responsible-ai/language-service/guidance-integration-responsible-use.md
-        displayName: Responsible deployment, Responsible use, Responsible integration, AI deployment, AI use
-      - name: Data, privacy, and security
-        href: ../../ai-foundry/responsible-ai/language-service/data-privacy.md
-        displayName: Data privacy, logging, data retention
-    - name: How-to guides
-      items:
-      - name: Call NER
-        href: named-entity-recognition/how-to-call.md
-      - name: Use skill parameters
-        href: named-entity-recognition/how-to/skill-parameters.md
-      - name: Use containers
-        items:
-        - name: Use Docker containers
-          href: named-entity-recognition/how-to/use-containers.md
-        - name: Configure containers
-          href: concepts/configure-containers.md
-        - name: Use container instances
-          href: ../containers/azure-container-instance-recipe.md?context=/azure/ai-services/language-service/context/context
-        - name: Use containers in disconnected environments
-          href: ../containers/disconnected-containers.md
-        - name: Azure AI containers overview
-          href: ../cognitive-services-container-support.md
-    - name: Concepts
-      items:
-      - name: Recognized entity categories 
-        href: named-entity-recognition/concepts/named-entity-categories.md
-      - name: Entity Metadata
-        href: named-entity-recognition/concepts/entity-metadata.md
-      - name: API version mapping
-        href: named-entity-recognition/concepts/ga-preview-mapping.md
-    - name: Tutorials
-      items:
-      - name: Extract information in Excel using Power Automate
-        href: named-entity-recognition/tutorials/extract-excel-information.md
-        displayName: excel integration, power automate, ner automation, extract entities
 
-  - name: Named Entity Recognition (custom)
-    items:
-    - name: Overview
-      href: custom-named-entity-recognition/overview.md
-    - name: Quickstart
-      href: custom-named-entity-recognition/quickstart.md
-    - name: Language support
-      href: custom-named-entity-recognition/language-support.md
-    - name: FAQ
-      href: custom-named-entity-recognition/faq.md
-    - name: Glossary
-      href: custom-named-entity-recognition/glossary.md
-    - name: How-to guides
-      items:
-      - name: Create projects
-        href: custom-named-entity-recognition/how-to/create-project.md
-      - name: Data selection and schema design
-        href: custom-named-entity-recognition/how-to/design-schema.md
-      - name: Label data
-        href: custom-named-entity-recognition/how-to/tag-data.md
-      - name: Auto label your data (preview)
-        href: custom-named-entity-recognition/how-to/use-autolabeling.md
-      - name: Label data with Azure Machine Learning
-        href: custom/azure-machine-learning-labeling.md
-      - name: Train a model
-        href: custom-named-entity-recognition/how-to/train-model.md
-      - name: Model performance (preview)
-        href: custom-named-entity-recognition/how-to/view-model-evaluation.md
-      - name: Deploy a model
-        href: custom-named-entity-recognition/how-to/deploy-model.md
-      - name: Extract entities from text
-        href: custom-named-entity-recognition/how-to/call-api.md
-      - name: Back up and recover your models
-        href: custom-named-entity-recognition/fail-over.md
-      - name: Use Custom NER containers
-        items:
-        - name: Use Custom NER Docker containers
-          href: custom-named-entity-recognition/how-to/use-containers.md
-        - name: Configure containers
-          href: concepts/configure-containers.md
-        - name: Use container instances
-          href: ../containers/azure-container-instance-recipe.md?context=/azure/ai-services/language-service/context/context
-        - name: Azure AI containers overview
-          href: ../cognitive-services-container-support.md
-      - name: Concepts
-        items:
-          - name: Evaluation metrics
-            href: custom-named-entity-recognition/concepts/evaluation-metrics.md
-          - name: Accepted data formats
-            href: custom-named-entity-recognition/concepts/data-formats.md
-          - name: Deploy to multiple regions
-            href: concepts/custom-features/multi-region-deployment.md
-          - name: Project versioning
-            href: concepts/custom-features/project-versioning.md
 
   - name: Orchestration workflow
     items:
@@ -582,70 +692,15 @@ items:
       - name: Glossary
         href: orchestration-workflow/glossary.md
 
-  - name: Personally Identifiable Information detection
-    items:
-    - name: Overview
-      href: personally-identifiable-information/overview.md
-      displayName: personally identifiable information, document
-    - name: Quickstart
-      href: personally-identifiable-information/quickstart.md
-    - name: Language support
-      href: personally-identifiable-information/language-support.md
-    - name: Responsible use of AI
-      items:
-      - name: Transparency note for PII
-        href: ../../ai-foundry/responsible-ai/language-service/transparency-note-personally-identifiable-information.md
-        displayName: Transparency note for PII, Personally Identifiable Information transparency, Responsible AI, Responsible use of AI
-      - name: Integration and responsible use
-        href: ../../ai-foundry/responsible-ai/language-service/guidance-integration-responsible-use.md
-        displayName: Responsible deployment, Responsible use, Responsible integration, AI deployment, AI use
-      - name: Data, privacy, and security
-        href: ../../ai-foundry/responsible-ai/language-service/data-privacy.md
-        displayName: Data privacy, logging, data retention
-    - name: How-to guides
-      items:
-      - name: Redact PII from text
-        href: personally-identifiable-information/how-to/redact-text-pii.md
-      - name: Redact PII from conversations
-        href: personally-identifiable-information/how-to/redact-conversation-pii.md
-      - name: Redact PII from native documents
-        href: personally-identifiable-information/how-to/redact-document-pii.md
-      - name:  Adapt PII to your domain
-        href: personally-identifiable-information/how-to/adapt-to-domain-pii.md
-      - name: Use Docker containers
-        items:
-          - name: Install and run containers
-            href: personally-identifiable-information/how-to/use-containers.md
-          - name: Configure containers
-            href: concepts/configure-containers.md
-          - name: Use container instances
-            href: ../containers/azure-container-instance-recipe.md?context=/azure/ai-services/language-service/context/context
-          - name: Use containers in disconnected environments
-            href: ../containers/disconnected-containers.md
-          - name: Azure AI containers overview
-            href: ../cognitive-services-container-support.md
-    - name: Recognized entity categories
-      items:
-      - name: PII in text
-        items:
-        - name: Extended format
-          href: personally-identifiable-information/concepts/entity-categories.md
-        - name: List format
-          href: personally-identifiable-information/concepts/entity-categories-list.md
-      - name: PII in conversations
-        items:
-        - name: Extended format
-          href: personally-identifiable-information/concepts/conversations-entity-categories.md
-        - name: List format
-          href: personally-identifiable-information/concepts/conversations-entities-list.md
+
   - name: Sentiment analysis and opinion mining
     items:
       - name: Overview
         href: sentiment-opinion-mining/overview.md
         displayName: sentiment analysis introduction, opinion mining overview, sentiment detection
       - name: Quickstart
         href: sentiment-opinion-mining/quickstart.md
-      - name: language-detection/how-to/use-containers.mdanguage support
+      - name: Language support
         href: sentiment-opinion-mining/language-support.md
       - name: Responsible use of AI
         items:
@@ -723,57 +778,14 @@ items:
           href: ../../ai-foundry/responsible-ai/language-service/data-privacy.md
           displayName: Data privacy, logging, data retention
 
-  - name: Text Analytics for health
-    items:
-    - name: Overview
-      href: text-analytics-for-health/overview.md
-      displayName: text analytics for health, healthcare nlp, medical text analysis, clinical text, health entities
-    - name: Quickstart
-      href: text-analytics-for-health/quickstart.md
-    - name: Language support
-      href: text-analytics-for-health/language-support.md
-    - name: Responsible use of AI
-      items:
-      - name: Transparency note for Text Analytics for health
-        href: ../../ai-foundry/responsible-ai/language-service/transparency-note-health.md
-        displayName: Transparency note for Text Analytics health, Text Analytics for health transparency, Responsible AI, Responsible use of AI
-      - name: Integration and responsible use
-        href: ../../ai-foundry/responsible-ai/language-service/guidance-integration-responsible-use.md
-        displayName: Responsible deployment, Responsible use, Responsible integration, AI deployment, AI use
-      - name: Data, privacy, and security
-        href: ../../ai-foundry/responsible-ai/language-service/data-privacy.md
-        displayName: Data privacy, logging, data retention
-    - name: How-to guides
-      items:
-      - name: How to call the API
-        href: text-analytics-for-health/how-to/call-api.md
-      - name: Use containers
-        items:
-        - name: Use Docker containers
-          href: text-analytics-for-health/how-to/use-containers.md
-        - name: Configure Docker containers
-          href: text-analytics-for-health/how-to/configure-containers.md
-        - name: Use container instances
-          href: ../containers/azure-container-instance-recipe.md?context=/azure/ai-services/language-service/context/context
-        - name: Azure AI containers overview
-          href: ../cognitive-services-container-support.md
-    - name: Concepts
-      items:
-      - name: Recognized entity categories
-        href: text-analytics-for-health/concepts/health-entity-categories.md
-      - name: Relation extraction
-        href: text-analytics-for-health/concepts/relation-extraction.md
-      - name: Assertion detection
-        href: text-analytics-for-health/concepts/assertion-detection.md
-      - name: Fast Healthcare Interoperability Resources structuring
-        href: text-analytics-for-health/concepts/fhir.md
+
 
 - name: Concepts
   items:
   - name: Developer guide
     href: concepts/developer-guide.md
     displayName: development, sdk guide, programming
-  - name: Azure tools and agents 
+  - name: Azure tools and agents
     href: concepts/foundry-tools-agents.md
     displayName: mcp, exact, question, answering, intent, routing
   - name: Role-based-access-control