---
title: "Migrating assets between Hyperscience instances"
slug: "migrating-assets"
updated: 2026-06-07T13:35:47Z
published: 2026-06-07T13:35:47Z
canonical: "help.hyperscience.ai/migrating-assets"
---

> ## Documentation Index
> Fetch the complete documentation index at: https://help.hyperscience.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Migrating assets between Hyperscience instances

Assets such as workflows, releases, models, and system settings are essential components of a Hyperscience deployment. Migrating them between development, testing, and production instances helps maintain consistency while reducing the need to recreate configurations and retrain models.

This article explains how to import and export these assets.

## Importance of migration

Asset migration helps organizations move validated configurations and trained assets between instances while maintaining consistency throughout the deployment lifecycle.

**Migration supports:**

- **Consistent configurations** – Keep workflows, models, releases, and system settings aligned across instances.
- **Faster deployment** – Reduce the time and effort required to move validated assets between instances.
- **Testing and validation** – Validate assets in lower instances before deploying them to production.

## Common scenarios for migration

Asset migration is commonly used in the following scenarios:

- **Instance upgrades** – Moving assets during application upgrades or when transitioning between Hyperscience versions.
- **Assets promotion** – Promoting validated assets from lower instances, such as development or testing, to higher instances, such as production.
- **Workflows expansion** – Deploying new workflows, layouts, or models to support additional document types or business processes.
- **Model and configuration updates** – Migrating updated models, releases, or system settings to improve document processing performance.

## Assets available for migration

- **Workflows**
- **Releases**
- **Classification models**
  - To learn more about Classification, see [Structured Document Classification](https://help.hyperscience.ai/latest/docs/structured-document-classification) and [Semi-structured Document Classification](https://help.hyperscience.ai/latest/docs/semi-structured-document-classification).
- **Field Identification models** and **Table Identification models**
- **Transcription Models**
- **Vision Language Models (VLM)**
- **Permission Groups**
- **Field Dictionary**
- **Translations**
  - Learn more in [Providing a Translated User Interface](https://help.hyperscience.ai/latest/docs/providing-a-translated-user-interface).
- **System Settings**
  - The core configurations of the platform. Learn more in [Application Settings Overview](https://help.hyperscience.ai/latest/docs/application-settings-overview).

## Migration process

### Preparation

If you are migrating assets as part of an application upgrade, review the following articles before you begin:

- [How Upgrades Work in Hypercell](/deployment/docs/how-upgrades-work-in-hypercell)
- [Planning Your Upgrade](/deployment/docs/planning-your-upgrade)
- [The Upgrade Process](/deployment/docs/the-upgrade-process)

> [!NOTE]
> Component dependencies
> 
> - You can import or export one component at a time.
> - When migrating models, releases, or configurations, ensure that all required dependencies are included and verified after import.

### (v42.3 and later) Migrating assets from the Assets page

> [!NOTE]
> Assets tab replaces the Import/Export tool in v42.3 and later.
> 
> Starting with v42.3, you can import and export supported components from **Administration** > **Assets**.
> 
> The Assets page groups supported asset types into categories and provides dedicated Import and Export actions for each component.

![](https://cdn.us.document360.io/87894cef-4958-4f3f-be6f-b75a78c82548/Images/Documentation/assets.jpg)

To migrate a component:

1. Go to **Administration** > **Assets**.

2. Locate the component type you want to migrate.

3. Select **Import** or **Export**.

4. Follow the steps in the respective modal.

### Component-specific considerations

The available import/export steps vary depending on the component type.

#### Classification Models

- Ensure that the Classification model is compatible with the target release version.
- Training data is not included when importing or exporting Classification models. To learn more, see [TDM for Classification models](https://help.hyperscience.ai/latest/docs/tdm-for-classification-models).

#### Field & Table Identification Models

- Field & Table Identification models are associated with layouts.
- When importing or exporting a model, select the relevant layout. Learn more in [Identification Models Overview](https://help.hyperscience.ai/latest/docs/identification-models-overview).

#### System Settings

- You can choose which settings to include when exporting.
- Importing system settings replaces the selected configuration values with those from the imported file. To learn more, see [Application Settings Overview](https://help.hyperscience.ai/latest/docs/application-settings-overview).

#### Releases

- Release packages can contain layouts, custom data types, and related configuration.
- When importing a release, choose how to handle duplicate custom data types. Learn more in [Releases](https://help.hyperscience.ai/latest/docs/releases).

#### Field Dictionary

- Field Dictionary entries are imported from and exported to CSV files.
- Download the sample CSV file to review the required format before importing.
- Imported entries must reference existing data types. To learn more, see our [Data Types](https://help.hyperscience.ai/latest/docs/data-types) category.

#### Translations

- Translation files are imported from and exported to CSV files.
- Select the target locale before importing a translation file.
- Use an exported translation file as a template when creating or updating translations. Learn more about translations in [Providing a Translated User Interface](https://help.hyperscience.ai/latest/docs/providing-a-translated-user-interface?highlight=translations).

## (v42.2 and earlier) Import / Export tool

> [!NOTE]
> In v42.2 and earlier, you can import and export supported components from Administration > Import/Export.
> 
> The Import/Export tool allows you to migrate supported components between Hyperscience instances.

### Using the Import/Export tool

1. Go to **Administration** > **Import/Export**.
2. Select the component you wish to import or export.
3. Click **Export** to download a bundle or Import to upload one.
4. Follow any on-screen prompts to complete the action.

![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXcnH4kc1anr3lI7iNcrS_hxBv2wRS2q7oXifVZV_0Yo8F-k2SIs63jh4IUzxeSL7zNBKbURh7K17HPMOZPCEsLcIDMm5f6zcI5oPR9yNBI3OSv4XqcJsimpBOvBm7cPHD4R22rS9Q?key=8TM4k0EvddMnQBomn6Z7qzzY)

## Migrating assets from individual component pages

Assets can also be imported and exported directly from their respective pages. See the drop-downs below for asset-specific migration.

### Exporting and importing flows

#### **Exporting flows**

Follow the steps below to export a flow:

1. Go to the **Flows** page and find the one you want to export.
2. Click on the menu (![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeNh7Z_QLzjWaet27MzgNcQsYnwgSuGFNK5zfZke76Xn2kPMLxPPyij6tGEv_1tk3Xf0yVFA6lSxSaTwyAHV8bO44yYcTs3a2hjuQW0rRjHcwGg67QQegH2mM7IqSfI5RNLGKes?key=8TM4k0EvddMnQBomn6Z7qzzY)) for the flow and click **Export All Flows**.

When exporting a flow from this menu, all connected subflows are included in the export.

![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXf-gbt739T5wIezOSvbQb8yOyFf72rlBXyXBnCpEMn8EGLrHoeSwxMzzDHmtO95D4XhFXhUWWtc_8Ztm_UGnU54tGcNplj8mUTSIcCkEsBDCmZRXgn2tJ2ag1peeA4BmyfNRLIEPA?key=8TM4k0EvddMnQBomn6Z7qzzY)

You can also export your flow from its details page:

1. Go to the **Flows** page and find the flow you want to export.
2. Open the flow’s details page and scroll down to the Flows table.
3. Click **Export** on the Top-Level Flow.

![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeZCrFSe2jRO4WfO3oEs5TgMuA-UXBAWvOdvhPYcuR86IqIR0ITmqLK44K7XxvfWbVjLl33san9s2rvuFD63IV-QlU61v0KXdECoshGUQ7kA4foMkjOsjnrso75XtImYtv8CieTkQ?key=8TM4k0EvddMnQBomn6Z7qzzY)

The exported file is named `Flow-[Flow_name]-[Flow_UUID]-[YYYYMMDD_HHMM].zip`, where:

- `[Flow_name]` is the name of the flow.
- `[Flow_UUID]` is its unique identifier.
- `[YYYYMMDD_HHMM]` represents the export date and time in GMT.

The ZIP file includes:

- A `flows` folder, containing the flow’s JSON file.
- A `manifest.json` file with metadata about the export.
- If the flow contains Code Blocks, their Python files are included.

Note that client secrets and Python package dependencies are **not** included in the export.

#### **Importing flows**

To import your flow:

1. Go to the **Flows** page.
2. Click **Import Flow**.
3. Upload the ZIP file you exported.

![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXcTr0Girn8d4QKbICeau8Y8cGNt2f4nfW6n504IDt_PdysxIY9QH-Rtw3VoZos4G050t4gY9IR1re_xIKoExh42JRXpWemesqqIx95yEeKA-3Memav1M3b3zz_G5T8yyAYiVsrvhg?key=8TM4k0EvddMnQBomn6Z7qzzY)

### Exporting and importing releases

#### **Exporting releases**

Follow the steps below to export your releases:

1. Go to **Library** > **Releases**, and locate the release you want to export.

- Use the search box to find the release by name or click **Filters** to filter the releases by status.

1. Click the name of the release to open its details.
2. Click **Actions**, then select **Download Release**.
  - The system may take a few minutes to prepare the file. You will receive a notification when it’s ready.
3. When notified, go to **Notifications**, find the entry for your file, and click **Download**.

![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXehLx9Av5iZGZy10gyDfxF55tVWfLscIiRzwkO4WJK-zNPri16KGQGoHhw1IMQt_gelkTMFQVcwZlPlS-ZedQ26fTU_rqBIXbPB2b7XrodtI3M-Fdo4Ln9CsK0VokLtCt5yQe_iXw?key=8TM4k0EvddMnQBomn6Z7qzzY)

The downloaded ZIP file contains the following assets:

- A JSON file containing metadata about the release, such as creation and last updated timestamps.
- `manifest.json`, which includes a summary of the release’s contents.
- `field_data_types.json`, containing a list of data types used in the release.
- Individual JSON files for each layout version in the release.
- Individual JSON files for each layout variation with selected field customizations applied.

#### **Importing releases**

To import your release:

1. Go to **Library** > **Releases** and click **Add Release**.
2. Select **Upload Existing**.
3. Choose one of the following options:
  - Drag and drop the ZIP file into the dialog box.
  - Click **Choose File** to locate and upload the file from your machine.
4. Under **How To Handle Duplicate Custom Data Types?** select an option:
  - **Skip if duplicate custom data type exists** - Retains the existing custom data type’s list of values without overwriting.
  - **Update with data from the JSON** - Overwrites the existing custom data type’s list of values with those from the imported file.
5. Click **Upload**.

> [!NOTE]
> When uploading a release, all included layouts are added to the Layout Library if they do not already exist. If the release was assigned to a flow in the testing instance, it will be automatically assigned to the same flow in the Production Instance. Field customizations applied to the release are incorporated into the layout’s variations, but they will not appear in the Fields and Customizations tab on the layout’s details page. To learn more, see [Adding a New Release](https://help.hyperscience.com/latest/docs/adding-a-new-release).
  - The system may take a few minutes to process the release. You will receive a notification when the process is complete.
6. After the import is finished, click **View Release** in the Notification Center to view the release’s details.

### Exporting and importing Classification models

#### **Exporting Classification models**

> [!NOTE]
> You can export Classification models through Training Data Management in v40.1 and later. Learn more in [TDM for Classification](https://help.hyperscience.com/latest/docs/tdm-for-classification).

Follow the steps below to export a Classification model:

1. Go to **Library** > **Models**.
2. Click on **Classification Models** in the drop-down list at the top of the page.
3. Click on the name of the model you want to export.
4. Click the menu (![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXdowkbE26XG3yiXmJ3yDoK9ykJiebsoIeha946jHXf6-qhyJKZDEZgXBvFynhnZB0flwan4Bd2R29cMBThGcXvzYISCv3DLro4ZPQpHOTHgmOUZ-dJinPdb3M-b0QXY1XFi2hDd8Q?key=8TM4k0EvddMnQBomn6Z7qzzY)) in the upper-right corner of the page, and click **Download Classification Model**.
  - The system may take a few minutes to prepare the file. You will receive a notification when it’s ready.
5. When notified, go to **Notifications**, find the entry for your file, and click **Download**.

or

1. On the model’s details page, scroll down to the **Model Activity** table.
2. Click the **Download model** button next to the last-trained version of your Classification model.

The ZIP file contains the following items:

- Metadata for the model.
- The model itself.

Note that the model’s training data is **not** included in the ZIP file.

![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXcYUdeG1pmEcuWndhuPlCIpoHOc-myj42IYBWxqJLthd-wmAK2gWlqN-HS98KsKPazoXEk9P8F12bfkMKJf1c_USsJ4lgQ0mxra_LFVB1SfBofXXn7Xt_N2wA9g1TVVt0LKz3mS?key=8TM4k0EvddMnQBomn6Z7qzzY)

#### **Importing Classification models**

> [!NOTE]
> Classification models can be uploaded only in v40.1 and later and only for live releases. Learn more in [TDM for Classification](https://help.hyperscience.com/latest/docs/tdm-for-classification).

1. Go to **Library** > **Models**.
2. Click **Classification Models** in the drop-down list at the top of the page.
3. Click **Import Model**.

### Exporting and importing Identification models

#### **Exporting Identification models**

Follow the steps below to export your Identification models:

1. Go to **Library** > **Models**.
2. Find your Identification model and click its name.
3. On the model’s details page, scroll down to the Model History table.
4. Click the download button (![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXehtoypB90tlbgmdy5mQz-8gGWr9cp99ohmR85VtJO7qmOIZ0ipyHurqq_1xqjGCNbT_FqNoGekgC8Img8OsA9GRzlWknY3XhCWKpVlBcL_FYruOsTnAqE4T_uM0GM_W93QlhfbfA?key=8TM4k0EvddMnQBomn6Z7qzzY)) in the **Actions** column to export the model.

The ZIP file contains:

- Model metadata.
- The model itself.

Note that the training data is **not** included in the ZIP file.

![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXflYIrEhlrXVCvKq3INew_SlUI6TsGk7AjhCmPYKmGAzI7MM7yRNKGKSlMjf3rf2BdSmw1UeLEYWj2GM-ZW9WSGODk0pxdDdtWqxrfSwI3RD_0n73FILWdLB6nGxIYF1Usmzxzg0Q?key=8TM4k0EvddMnQBomn6Z7qzzY)

#### **Exporting training data**

To export the training data for your Identification model:

1. Go to **Library** > **Models**.
2. Open the model you want to export the training data for.
3. Click **Actions**, and then click **Download training documents…** in the drop-down list.
  - The system may take a few minutes to prepare the file. You will receive a notification when it’s ready.
4. When notified, go to **Notifications**, find the entry for your file, and click **Download**.

> [!NOTE]
> You must annotate your documents before downloading the training data.
  - The export consists of a training data ZIP file with the following elements:
    - A JSON file with training data.
    - Document images.
    - Layout version.

A training data ZIP file contains up to 500 pages. If a model has more than 500 pages of training data, the data is split into multiple ZIP files. For example, if a model has 900 pages of training data, two training data ZIP files will be available for download.

#### **Importing Identification models**

Follow the steps below to import your Identification model:

1. Go to **Library** > **Models**.
2. Click the name of your model to access the model management page.
3. From the **Actions** drop-down click **Upload Model**.
4. Once the model is uploaded, click **Submit**.

> [!NOTE]
> You can upload identification models only if their layouts are live. Make sure to match each model to its layout before uploading.

#### **Importing training data**

> [!NOTE]
> Using submission data in TDM
> 
> In v41.2, we’ve introduced a new setting called **Send documents to Training Data Management** for Identification and Classification models. This setting allows you to control whether submission data is used for model training. It is disabled by default and can be managed from the System Settings (**Administration** > **System** **Settings**).

Follow the steps below to import your training data:

1. After you’ve imported your model, open its details page and click **Actions**.
2. Click **Upload training documents…** in the drop-down list.

![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXe8LmaI60Ay2tmmeJe8d4Cby6DOoSS5f5mANslyrN3yFBeaftT782-hcYfeUYWRyI6XpewCa50O1ssuO-JzXCMEMQKedeFVX2Vt6cXNrY1vBPIsP5AHZ4EDh2VYpFwWVUzmDKwIxA?key=8TM4k0EvddMnQBomn6Z7qzzY)

1. Click **Upload Existing** in the dialog box, and follow the instructions to complete the upload process.

> [!NOTE]
> Clicking **Upload Existing** allows you to upload training data for layouts that have been created in another instance.
2. Once you’re done, click **Submit**.

> [!NOTE]
> Only one training data bundle can be uploaded at a time.

### Exporting Transcription / Finetuning models

You can export all finetuning models in a Transcription model, or you can download individual finetuning models.

#### **Export all finetuning models in a Transcription model**

To export all finetuning models in a Transcription model:

1. Go to **Library** > **Models**, and click on **Transcription Models** in the drop-down list at the top of the page.
2. In the Transcription Models table, click on the name of the transcription model whose fine-tuning models you want to download.
3. Click **Download** all models.

The models are downloaded as a ZIP file with the name `confidence_thresholding_model_export.zip.`

#### **Export an individual finetuning model**

To export a specific finetuning model:

1. Go to **Library** > **Models**, and then select **Transcription Models** in the drop-down list at the top of the page.
2. In the **Transcription Models** table, click on the name of the transcription model that contains the finetuning model you want to download.
3. Click on the tab that matches the type of fine-tuning model you want to download.
4. In the **Available Models** table, find the version of the model you want to download, and click **Download Model**.

The model is downloaded as a ZIP file with the name `confidence_thresholding_model_export.zip`

#### **Importing Transcription models**

> [!NOTE]
> **Importing Transcription models from other versions**
> 
> Transcription models trained on a different version than your current instance will be imported successfully, but will not appear in the model list. The list only includes models that match the current version of your instance.
> 
> Note that you can only import models created in v38 or later. For more information on model compatibility, see [Compatibility Across Application, Flow, and Model Versions](https://help.hyperscience.com/latest/docs/compatibility-across-application-flow-and-model-versions).

To import a model:

1. Go to **Library** > **Models**, and then click **Transcription Models** in the drop-down list at the top of the page.
2. In the Transcription Models table, find the transcription model you want to import a finetuning model for and click on its name.
3. Click **Upload model**.

> [!NOTE]
> You do not need to click the tab for the type of finetuning model you want to import. The system automatically detects the type of model during the import process.

1. Do one of the following:
  - Drag and drop the model’s ZIP file into the Import dialog box.
  - Click **Choose Files**, and find and open the model’s ZIP file on your machine.
2. Click **Import**.

#### **Working with Transcription models**

When importing or exporting Transcription models, keep the following factors in mind:

- **Manual vs. Automatic Training**
  - **Manual Training** does not automatically deploy the newly trained Transcription model. Manual training allows you to:
    - Train the model in your production instance (where QA data resides).
    - Export the trained model, test it in a **lower** instance, and, if satisfied, re-import it into production for manual deployment.
  - **Automatic Training**, on the other hand, deploys the model immediately upon training. To validate the model before deployment, it’s recommended to disable automatic training.
- **QA Data Migration**
  - It is not possible to migrate QA training data from production to a lower instance due to internal safeguards. Instead, you can:
    - Use the manual training method to train and validate models in your lower instance before deployment.

> [!NOTE]
> If you are unable to train a Transcription model, check your flows. Ensure that the **Transcription Automation Training** setting under the **Semi-Structured Document** **Transcription** settings type is enabled. To learn more about editing flow settings, see [Document Processing Subflow Settings](https://help.hyperscience.com/latest/docs/document-processing-subflow-settings).
> 
> Transcription model training logic is based on settings from the last-edited flow, so it’s important to confirm this setting is enabled in all applicable flows.

### Exporting and importing system settings

To move system settings between multiple instances that are on the same major version, follow the steps outlined in this section.

#### **Exporting system settings**

To export system settings:

1. Go to **Administration** > **System Settings**
2. Click **Export** at the top of the page.
3. Deselect the system settings that you do not want to export.
4. Click **Download Settings**.

The system settings are exported in a JSON file. You can then use this JSON file to import these system settings into another instance that uses the same major version of the application.

#### **Importing system settings**

To import system settings:

1. Go to **Administration** > **System Settings**.
2. Click **Import** at the top of the page
3. Drag and drop your settings file and click the **Import** button. You can also click **Browse** to find the settings file on your machine and open it.
4. Deselect the settings you do not want to import. All settings that you choose not to import will keep their current value.
5. Click **Apply Changes now**.

### Exporting and importing permission groups

#### **Exporting Permission Groups**

You can use your custom permission groups in other instances. To do so, export the groups, then import their JSON files into any instances where you would like to use the permission groups.

To export permission groups:

1. Go to **Users** > **Permission Groups**, and select the checkboxes for the groups you want to export.
2. Click **Actions**, and then click **Export**.

The system creates a JSON file for each exported permission group, which is then downloaded to your machine. If you’ve exported multiple groups, the system creates a ZIP file containing a JSON file for each permission group.

#### **Importing permission groups**

To import a permission group:

1. Go to **Users** > **Permission Groups**.
2. Click **Add Permission Group** in the upper-right corner.
3. In the **Add Permission Group** dialog box, click the **Upload Existing** tab.
4. Do one of the following:
  - Drag and drop the permission group’s JSON file to the dialog box.
  - Click **Choose File**, and find and open the permission group’s JSON file on your machine.
5. Click **Upload**.

## Best practices for assets migration

- **Migrate dependent components in the correct order:**
  - Flows → Releases → Classification Models → Identification Models → Training Data
- **Disable Continuous Model Improvement.**
  - Disable settings such as **Continuous Field Locator model improvement** and **Continuous Classification Model Improvement** to prevent migrated models from being retrained automatically.Learn more in [Identification Settings](https://help.hyperscience.com/latest/docs/identification-settings).

> [!NOTE]
> Train models manually in a lower instance before migration.

- **Maintain high-quality training data.**
  - Use validated training datasets when retraining models after migration.
  - Poor-quality annotations or QA data can negatively affect model performance. Review training data quality before reusing it in another instance. Learn more in our [Model Maintenance](https://help.hyperscience.ai/latest/docs/model-maintenance) section.
  - Retain a reliable and validated Dataset in a lower instance for model training.

- **Validate imported artifacts.**
  - Verify that flows are associated with the correct releases.
  - Verify that models are associated with the expected releases, layouts, and training data.
- **Retain older instances until validation is complete.**
  - Keep historical data accessible until the migration has been successfully validated.
  - After migration, validate model performance and automation rates before deprecating older instances. To learn more, see [Monitoring Model Performance](https://help.hyperscience.ai/latest/docs/monitoring-model-performance).

A **Workflow** or **Flow** is a customizable workflow in Hyperscience that automates the processing of documents, including steps like classification, data extraction, validation, and output. Flows streamline operations by handling tasks step-by-step with minimal manual effort. Learn more in [Flows](https://help.hyperscience.ai/latest/docs/flows).

A release is a package of one or more committed layout variation versions. This collection of layouts or layout variations should reflect the various document types that the machine should expect. To use the layouts that you have created to process documents, you will need to deploy a flow that contains a release. Learn more in [Releases](https://help.hyperscience.ai/latest/docs/releases).

A machine learning model that automatically identifies a document’s type—Structured, Semi-structured, or Additional—and matches it to the correct layout. This classification helps Hyperscience process different document types accurately without manual intervention. Learn more in [Classification models](https://help.hyperscience.ai/latest/docs/classification-models).

A machine learning model in Hyperscience that learns where fields are located in Semi-structured documents. It uses examples from training to predict the position of each field on a page so the system can extract the right data. To learn more, see [Identification models](https://help.hyperscience.ai/latest/docs/identification-models).

A machine learning model in Hyperscience that learns where cells and rows are located in Semi-structured documents. It uses examples from training to predict the position of each cell and row on a page so the system can extract the correct data. Learn more in [Identification models](https://help.hyperscience.ai/latest/docs/identification-models).

A machine learning model that automatically extracts text from scanned document images. It supports both printed and handwritten text. When the model’s confidence in the extracted text is low, the system generates a Transcription task for human review to ensure accuracy. Learn more in [Transcription models](https://help.hyperscience.ai/latest/docs/transcription-models).

Models that understand both the text and images in a document. They combine what’s written with where it appears on the page to help the system read and extract information more accurately. Learn more in [Visual Language Models](https://help.hyperscience.ai/latest/docs/vlm-field-extraction-models).

A group of users who share the same set of permissions. Learn more in [User Management](https://help.hyperscience.ai/latest/docs/user-management).

A centralized place in Hyperscience where you define and manage field customizations for Structured layouts. It helps keep field names, data types, and output settings consistent across different documents. Learn more in [Data Types](https://help.hyperscience.ai/latest/docs/data-types).

The main flow that manages the end-to-end document processing, coordinating with subflows to handle specific components of the process. Learn more in [Flows](https://help.hyperscience.ai/latest/docs/flows).

The live Hyperscience system, where real documents and data are processed as part of day-to-day business operations. This instance is used by end users and must meet high standards for performance, stability, and data security. It is distinct from development or testing instances.

When this setting is enabled, the system automatically retrains and updates Field Locator models using newly available QA data. This process allows the model to improve over time without manual intervention. It helps enhance accuracy for identifying field locations in Semi-structured documents.

This setting should *only* be enabled if there’s enough training data in the environment to support it.

A group of documents used to help the system learn or improve. Datasets are used for training, testing, or evaluating how well the system reads and extracts information.