diff --git a/docs/.vitepress/config.ts b/docs/.vitepress/config.ts
index de10f119..afc95c16 100644
--- a/docs/.vitepress/config.ts
+++ b/docs/.vitepress/config.ts
@@ -108,6 +108,15 @@ function sidebarDefault() {
         }
       ]
     },
+    {
+      text: "✨ 1.0 Beta Release",
+      collapsible: true,
+      items: [
+        {text: "OCR4all 1.0", link: "/beta"},
+        {text: "Introduction", link: "/beta/introduction"},
+        {text: "Setup", link: "/beta/setup"},
+      ]
+    }
   ]
 }
 
diff --git a/docs/beta/index.md b/docs/beta/index.md
new file mode 100644
index 00000000..0921e12e
--- /dev/null
+++ b/docs/beta/index.md
@@ -0,0 +1,20 @@
+---
+title: OCR4all 1.0
+next:
+  text: 'Introduction'
+  link: '/beta/introduction'
+---
+# {{ $frontmatter.title }}
+## Core Features
+- 👥 User and group management. Share your all your projects and files with other users and groups on the same instance.
+- ⚙️ Wide array of OCR processor, powered by [OCR-D](https://ocr-d.de/) and others.
+- 🗂️ Fully fledged in-app data management. Upload and manage your images, models, workflows and datasets completely through the UI. 
+- 📥 Export all uploaded and generated data, import them into other OCR4all instances or use them wherever you want.
+- 👑 Full data sovereignty. No data leaves your instance unless approved by you or the instance administrator. 
+- 💪 Generate training data and use it to train or fine-tune models.
+- 🆓 OCR4all is and will always stay free and open-source.
+- and much more...
+
+## Next steps
+- [Find out more about OCR4all 1.0](/beta/introduction)
+- [Jump right into setting up your own instance of the OCR4all 1.0 beta](/beta/setup)
diff --git a/docs/beta/introduction.md b/docs/beta/introduction.md
new file mode 100644
index 00000000..8fbdea4e
--- /dev/null
+++ b/docs/beta/introduction.md
@@ -0,0 +1,81 @@
+---
+title: OCR4all 1.0 – Introduction
+next:
+  text: 'Setup Beta'
+  link: '/beta/setup'
+---
+# {{ $frontmatter.title }}
+
+## Motivation and General Idea
+- **Availability of Solutions:** Numerous high-performance open-source solutions for Automatic Text Recognition (ATR) are already available, with new releases emerging continuously.
+- **Diverse Use Cases:** The highly heterogeneous nature of use cases necessitates the targeted deployment of specialized ATR solutions.
+- **Requirement:** There is a need for user-friendly frameworks that facilitate the flexible, integrable, and sustainable combination and application of both existing and future ATR solutions.
+- **Objective:** Our goal is to empower users to perform ATR independently, achieving high-quality results.
+- **Foundation:** This framework is built upon freely available tools, enhanced by our in-house developments.
+
+## OCR-D and OCR4all
+- **OCR-D Initiative:** The DFG-funded OCR-D initiative is dedicated to facilitating the mass full-text transformation of historical prints published in the German-speaking world.
+- **Focus Areas:** OCR-D emphasizes interoperability and connectivity, ensuring a high degree of flexibility and sustainability in its solutions.
+- **Integrated Solutions:** The initiative combines multiple ATR solutions within a unified framework, enabling precise adaptation to specific materials and use cases.
+- **Open Source Commitment:** All results from the OCR-D project are released as completely open-source.
+- **OCR4all-Libraries Project:** The DFG-funded OCR4all-libraries project has two primary goals:
+    - Providing a user-friendly interface for OCR-D solutions via OCR4all, enabling independent use by non-technical users.
+    - Enhancing the ATR output within OCR4all to offer added value to even the most technically experienced users.
+
+## System Architecture
+- **Modularity and Interoperability:** The framework is designed with a strong focus on modularity and interoperability, ensuring seamless integration and adaptability.
+- **Distributed Infrastructure:** The architecture features a distributed infrastructure, with a clear separation between the backend and frontend components.
+    - **Backend:** Built with Java and [Spring Boot](https://spring.io/projects/spring-boot).
+    - **Frontend:** Developed using the [Vue.js](https://vuejs.org/) ecosystem.
+- **Component Communication:** Components communicate via a REST API, enabling efficient interaction between different parts of the system.
+- **Integration of Third-Party Solutions:** Service Provider Interfaces (SPIs) allow for the integration of third-party solutions, such as ATR processors.
+- **Containerized Setup:** The containerized architecture ensures easy distribution and deployment of all integrated components with minimal barriers.
+- **Data Sovereignty:** Users retain full control over their data, with no data leaving the instance without explicit user or administrator consent.
+- **Reproducibility:** Every step in the process is fully reproducible. A "transcript of records" feature stores detailed information about the processors and parameters used, ensuring transparency and repeatability.
+
+## Modules
+
+### Data Management and Processing
+- **Separation of Functions:** Data management and processing are strictly separated to ensure efficient handling and security.
+- **Data Sharing:** Data can be shared with different users or user groups as needed.
+
+### Processors and NodeFlow
+- **Wide Array of Processors:** A diverse range of ATR processors is available, including OCR-D and external options.
+- **Ease of Integration:** New processors can be easily implemented via a well-defined interface, with the user interface generated automatically.
+- **NodeFlow:** The graphical editor NodeFlow simplifies the creation of workflows, making it convenient for users to design and customize processing sequences.
+
+### LAREX
+- **Result Correction and Training Data Creation:** LAREX allows for the correction of all ATR workflow results and the creation of training data.
+- **Visual Workflow Identification:** LAREX helps users identify the most suitable workflows as a visual explanation component.
+
+### Datasets, Training, and Evaluation
+- **Dataset Creation:** Datasets can be created with the option to use tagging and import functionalities.
+- **Dataset Enrichment:** Datasets can be enriched with training data generated and tagged within the application, even across various projects and sources.
+- **Model Training:** Train models on selected datasets or subsets thereof, with options for in-app usage or exporting both models and associated training data.
+- **Model Evaluation:** Evaluate both trained and imported models using curated datasets to ensure quality and accuracy.
+
+### Working with OCR4all 1.0
+#### One Tool, Two Modes
+| Base Mode                                                                                 |                                                                                                                                              Pro Mode |
+|-------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------|
+| Designed for novice users, with reduced complexity and a strongly guided, linear workflow | Tailored for experienced users who require more exploration and complexity |
+| Pre-selected solutions for each processing step                                           | Unrestricted access to all processors, parameters, and features |
+| Pre-filtered parameters and limited access to advanced features                           | Support for identifying the best workflows and models for specific needs |
+::: info
+Currently only pro mode is available in the beta release. The base mode will be added shortly.
+:::
+### Example Use Cases and Application Scenarios
+#### Fully Automatic Mass Full-Text Digitalization
+- **Objective:** Maximize throughput with minimal manual effort.
+- **Users:** Libraries and archives processing large volumes of scanned materials.
+- **Approach:** Use the pro mode (NodeFlow, LAREX, and datasets) to identify the most suitable workflow.
+
+#### Flawless Transcription of Source Material
+- **Objective:** Achieve maximum quality, accepting significant manual effort.
+- **Users:** Humanist researchers preparing text for a digital edition.
+- **Approach:** Utilize the base mode for iterative transcription with continually improving accuracy.
+
+#### Building Corpora for Quantitative Applications
+- **Objective:** Maximize quality while minimizing manual effort.
+- **Users:** Researchers constructing corpora for training and evaluating quantitative methods.
+- **Approach:** Manage data and consistently retrain source-specific or mixed models using datasets and tagging functionalities.
diff --git a/docs/beta/setup.md b/docs/beta/setup.md
new file mode 100644
index 00000000..cfa46e98
--- /dev/null
+++ b/docs/beta/setup.md
@@ -0,0 +1,75 @@
+---
+title: OCR4all 1.0 – Setup
+---
+# {{ $frontmatter.title }}
+
+If you want to try out the beta version of release 1.0 of OCR4all you can simply use the following Docker Compose file or download it [here]().
+
+The prerequisite for this is having both [Docker](https://docs.docker.com/get-started/get-docker/) and [Docker Compose](https://docs.docker.com/compose/install/) installed. 
+
+A more in-depth installation guide will follow with the stable release of OCR4all 1.0.
+
+::: warning
+This will install a beta version of OCR4all 1.0 which may still contain some bugs and many features are yet to come.
+:::
+
+```
+version: "3.9"
+
+services:
+  msa-calamari:
+    hostname: msa-calamari
+    build:
+      context: ocr4all-app-calamari-msa
+      dockerfile: Dockerfile
+      args:
+        - TAG=${CALAMARI_TAG:-20240502}
+        - JAVA_VERSION=${CALAMARI_JAVA_VERSION:-17}
+        - APP_VERSION=${CALAMARI_APP_VERSION:-1.0-SNAPSHOT}
+    user: "${UID:-}"
+    restart: always
+    environment:
+      - SPRING_PROFILES_ACTIVE=${CALAMARI_PROFILES:-logging-debug,msa-api,docker}
+    volumes:
+      - ${OCR4ALL_DATA:-~/ocr4all/docker/data}:/srv/ocr4all/data
+      - ${OCR4ALL_ASSEMBLE:-~/ocr4all/docker/assemble}:/srv/ocr4all/assemble
+      - ${OCR4ALL_WORKSPACE_PROJECT:-~/ocr4all/docker/workspace/projects}:/srv/ocr4all/projects
+    ports:
+      - "${CALAMARI_API_PORT:-127.0.0.1:9092}:8080"
+  msa-ocrd:
+    hostname: msa-ocrd
+    build:
+      context: ocr4all-app-ocrd-msa
+      dockerfile: Dockerfile
+      args:
+        - TAG=${OCRD_TAG:-2024-04-29}
+        - JAVA_VERSION=${OCRD_JAVA_VERSION:-17}
+        - APP_VERSION=${OCRD_APP_VERSION:-1.0-SNAPSHOT}
+    user: "${UID:-}"
+    restart: always
+    environment:
+      - SPRING_PROFILES_ACTIVE=${OCRD_PROFILES:-logging-debug,msa-api,docker}
+    volumes:
+      - ${OCR4ALL_WORKSPACE_PROJECT:-~/ocr4all/docker/workspace/projects}:/srv/ocr4all/projects
+      - ${OCR4ALL_RESOURCES_ORCD:-~/ocr4all/docker/opt/ocr-d/resources}:/usr/local/share/ocrd-resources
+    ports:
+      - "${OCRD_API_PORT:-127.0.0.1:9091}:8080"
+  server:
+     build:
+      context: ocr4all-app
+      dockerfile: Dockerfile
+      args:
+        - TAG=${OCR4ALL_TAG:-17-jdk-slim}
+        - APP_VERSION=${OCR4ALL_APP_VERSION:-1.0-SNAPSHOT}
+    user: "${UID:-}"
+    restart: always
+    environment:
+      - SPRING_PROFILES_ACTIVE=${OCR4ALL_PROFILES:-logging-debug,server,api,documentation,docker}
+    volumes:
+      - ${OCR4ALL_HOME:-~/ocr4all/docker}:/srv/ocr4all
+    ports:
+      - "${OCR4ALL_API_PORT:-9090}:8080"
+    depends_on:
+      - msa-calamari
+      - msa-ocrd
+```
diff --git a/docs/index.md b/docs/index.md
index 599e350a..043f7b5f 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -18,6 +18,9 @@ hero:
     - theme: alt
       text: User Guide
       link: /guide/user-guide/introduction
+    - theme: brand
+      text: ✨ Beta Release 1.0
+      link: /beta
 
 features:
   - title: Fully free and open-source