diff --git a/docs/intro.md b/docs/intro.md
index 6c44856..0e30d3f 100644
--- a/docs/intro.md
+++ b/docs/intro.md
@@ -2,11 +2,11 @@ For a minimal working example, see the [Quick Start](#quick-start) section below
 
 This is the documentation for `cadenzaanalytics` version {{version}}.
 
-# disy Cadenza Analytics Extensions
+# Cadenza Analytics Extensions
 
 An Analytics Extension extends the functional spectrum of [disy Cadenza](https://www.disy.net/en/products/disy-cadenza/) with an analysis function or a visualisation type.
-On a technical level an Analytics Extension is a web service that exchanges structured data with disy Cadenza via the Cadenza API.
-A user can integrate an analysis extension into disy Cadenza via the Management Center and manage it there (if sufficient rights have been granted).
+On a technical level an Analytics Extension is a web service that exchanges structured data with Cadenza via the Cadenza API.
+A user can integrate an analysis extension into Cadenza via the Management Center and manage it there (if sufficient rights have been granted).
 
 As of disy Cadenza Autumn 2025 (10.4), the following types and capabilities of analysis extensions are officially supported:
 
@@ -17,7 +17,7 @@ As of disy Cadenza Autumn 2025 (10.4), the following types and capabilities of a
 
 ## Communication
 
-An Analytics Extension defines one endpoint that, depending on the HTTP method of the request, is used to supply the Extension's configuration to disy Cadenza, or exchange data and results with Cadenza respectively.
+An Analytics Extension defines one endpoint that, depending on the HTTP method of the request, is used to supply the Extension's configuration to Cadenza, or exchange data and results with Cadenza respectively.
 
 <!--- Beware: when building documentation locally, path to image must not be relative to this document, but relative to the one that includes this md file!
              (in this case: src/cadenzaanalytics/__init__.py  ->  <img src="../../docs/communication.png"... )
@@ -26,10 +26,10 @@ An Analytics Extension defines one endpoint that, depending on the HTTP method o
 <img src="communication.png" alt="(Image: Communication between disy Cadenza and Analytics Extension)" width="800">
 
 When receiving an `HTTP(S) GET` request, the endpoint returns a JSON representation of the extension's configuration.
-This step is executed once when registering the Analytics Extension from the disy Cadenza Management Center GUI and does not need to be repeated unless the extension's configuration changes.
+This step is executed once when registering the Analytics Extension from the Cadenza Management Center GUI and does not need to be repeated unless the extension's configuration changes.
 
 By sending an `HTTP(S) POST` request to the same endpoint and including the data, metadata and parameters as specified in the extension's configuration as payload, the extension is executed.
-This step is executed each time that the Analytics Extension is invoked from the disy Cadenza GUI and Cadenza takes care of properly formatting the payload.
+This step is executed each time that the Analytics Extension is invoked from the Cadenza GUI and Cadenza takes care of properly formatting the payload.
 
 The `cadenzaanalytics` module provides the functionality to abstract the required communication and easily configure the Analytics Extension's responses to the above requests.
 
@@ -38,11 +38,11 @@ The `cadenzaanalytics` module provides the functionality to abstract the require
 
 ## Requirements and Dependencies
 
-For each disy Cadenza version, the correct corresponding library version needs to be used.
-The disy Cadenza main version is reflected in the corresponding major and minor version of `cadenzaanalytics` (e.g. 10.4.0 for Cadenza 10.4), while the last version segment is increased for both bugfixes and functional changes.
+For each Cadenza version, the correct corresponding library version needs to be used.
+The main version is reflected in the corresponding major and minor version of `cadenzaanalytics` (e.g. 10.4.0 for disy Cadenza 10.4), while the last version segment is increased for both bugfixes and functional changes.
 
-For Cadenza 10.2 and earlier versions, `cadenzaanalytics` used a semantic versioning scheme.
-The first version of disy Cadenza that supported Analytics Extensions is disy Cadenza Autumn 2023 (9.3).
+For disy Cadenza 10.2 and earlier versions, `cadenzaanalytics` used a semantic versioning scheme.
+The first version of Cadenza that supported Analytics Extensions is disy Cadenza Autumn 2023 (9.3).
 
 The `cadenzaanalytics` package has the following dependencies:
 
@@ -103,9 +103,9 @@ my_table = ca.Table(name="table", attribute_groups=[my_attribute_group])
 
 # 4. Configure the extension
 my_extension = ca.CadenzaAnalyticsExtension(
-    relative_path="echo-extension",
     print_name="Echo Extension",
     extension_type=ca.ExtensionType.DATA,
+    relative_path="echo-extension",
     tables=[my_table],
     analytics_function=echo_function
 )
@@ -130,7 +130,8 @@ The key components are:
 
 5. **CadenzaAnalyticsExtensionService**: Registers extensions and runs the web server.
 
-Save this as `my_extension.py` and run it with `python my_extension.py`. The extension will be available at `http://localhost:5000/echo-extension`.
+Save this as `my_extension.py` and run it with `python my_extension.py`.
+The extension will be available at `http://localhost:5000/echo-extension`.
 
 More complete examples can be found in the [`examples` folder of the module's GitHub repository](https://github.com/DisyInformationssysteme/cadenza-analytics-python/tree/main/examples).
 
@@ -141,7 +142,8 @@ The following sections explain each component in detail, following the same orde
 
 ## The Analytics Function
 
-The analytics function is the core of your extension. It receives an [`AnalyticsRequest`](cadenzaanalytics/request/analytics_request.html) and returns a response object.
+The analytics function is the core of your extension.
+It receives an [`AnalyticsRequest`](cadenzaanalytics/request/analytics_request.html) and returns a response object.
 
 ```python
 def my_analytics_function(request: ca.AnalyticsRequest):
@@ -165,6 +167,15 @@ The return type depends on the extension type:
 - **Visual extensions**: Return [`ImageResponse`](cadenzaanalytics/response/image_response.html), [`TextResponse`](cadenzaanalytics/response/text_response.html), or [`UrlResponse`](cadenzaanalytics/response/url_response.html)
 
 
+## Data, Metadata and Parameters
+
+Access to request data is split into three objects: **data** (the pandas DataFrame), **metadata** (a [`RequestMetadata`](cadenzaanalytics/request/request_metadata.html) mapping of column descriptions), and **parameters** (user-configured inputs).
+
+The reason metadata is separate from the DataFrame is that a pandas DataFrame only tracks column names and dtypes, but Cadenza carries more: 
+each column has a user-visible display name (`print_name`) that is not necessarily unique and thus independent of its internal name in the DataFrame.
+Other information includes a role (dimension or measure), a declared data type, or for geometry columns a spatial reference system.
+Column names in the DataFrame may also differ from what users see in Cadenza — when multiple columns belong to the same attribute group they receive a numeric suffix (`_1`, `_2`, …).
+Because [`ColumnMetadata`](cadenzaanalytics/data/column_metadata.html) describes a column regardless of direction, the same objects can be passed directly into a `DataResponse` — which is exactly what the echo extension in the Quick Start section above does.
 
 ### Reading Data
 
@@ -178,25 +189,31 @@ metadata = table.metadata  # RequestMetadata object
 
 ### Reading Metadata
 
-The `metadata` object contains information on the columns in the `data` DataFrame, such as their print name and type in disy Cadenza, their column name in the pandas DataFrame, or additional information like a `geometry_type`, where applicable.
-
-The metadata supports pythonic access patterns:
+[`RequestMetadata`](cadenzaanalytics/request/request_metadata.html) supports pythonic access patterns — iteration, membership testing, and key-based lookup all work as expected.
+Each lookup returns a [`ColumnMetadata`](cadenzaanalytics/data/column_metadata.html) object with properties for everything Cadenza knows about the column:
 
 ```python
-# Get all columns as a list
-all_columns = metadata.columns
-
-# Access a specific column by name
+# Access a column's metadata by its DataFrame column name
 column = metadata["column_name"]
+print(column.print_name)   # user-visible display name in Cadenza
+print(column.data_type)    # e.g. DataType.FLOAT64
+print(column.role)         # e.g. AttributeRole.MEASURE
 
 # Check if a column exists
 if "my_column" in metadata:
     column = metadata["my_column"]
 
+# Get all columns as a list
+all_columns = metadata.columns
+
 # Iterate over column names
 for column_name in metadata:
     print(column_name)
 
+# Iterate over ColumnMetadata objects
+for column in metadata.columns:
+    print(column.print_name)
+
 # Get columns grouped by attribute group
 columns_by_group = metadata.groups
 if "my_data" in columns_by_group:
@@ -215,7 +232,7 @@ flag_value = request.parameters["flag"]
 # Get parameter with default if not set
 value = request.parameters.get("optional_param", 42)
 
-# get full paramter object to retrieve additional info about the parameter
+# get full parameter object to retrieve additional info about the parameter
 # e.g. the srs for geometry parameters
 srs = request.parameters.info("geom").srs
 ```
@@ -236,7 +253,7 @@ These values are `None` when the extension is invoked outside of a workbook view
 
 ## Defining Expected Data
 
-To specify what data can be passed from disy Cadenza to the Analytics Extension, you define a [`Table`](cadenzaanalytics/data/table.html) containing one or more [`AttributeGroup`](cadenzaanalytics/data/attribute_group.html) objects.
+To specify what data can be passed from Cadenza to the Analytics Extension, you define a [`Table`](cadenzaanalytics/data/table.html) containing one or more [`AttributeGroup`](cadenzaanalytics/data/attribute_group.html) objects.
 
 ### Table
 
@@ -249,7 +266,8 @@ my_table = ca.Table(
 )
 ```
 
-The `name` parameter (here `"table"`) is the key you use to access the table in your analytics function via `request["table"]`. Currently, at most one table per extension is supported.
+The `name` parameter (here `"table"`) is the key you use to access the table in your analytics function via `request["table"]`.
+Currently, at most one table per extension is supported.
 
 ### Attribute Groups
 
@@ -319,10 +337,11 @@ geometry_group = ca.AttributeGroup(
 )
 ```
 
-Geometry columns are automatically parsed from WKT strings into [Shapely](https://shapely.readthedocs.io/) geometry objects. You can use them directly with Shapely operations or convert to a GeoDataFrame:
+Geometry columns are automatically parsed from WKT strings into [Shapely](https://shapely.readthedocs.io/) geometry objects.
+You can use them directly with Shapely operations or convert to a GeoDataFrame:
 
-DataType.GEOMETRY must not be combined with other data types. Since Cadenza users can select attributes that are linked to a geometry attribute, mixing data types would 
-create ambiguity about whether the intended output is the geometry object or the linked non-geometry attribute.  
+DataType.GEOMETRY must not be combined with other data types.
+Since Cadenza users can select attributes that are linked to a geometry attribute, mixing data types would create ambiguity about whether the intended output is the geometry object or the linked non-geometry attribute.
 Other data types may be mixed freely, but processing them may require inspecting their actual data type.
 
 ```python
@@ -434,7 +453,7 @@ area_param = ca.Parameter(
 
 The geometry parameter value will be a Shapely geometry object.
 
-**Note:** Parameters for Analytics Extensions of the type `visual` can currently *not* yet be assigned on the disy Cadenza side when displaying the result as a Cadenza view.
+**Note:** Parameters for Analytics Extensions of the type `visual` can currently *not* yet be assigned on the Cadenza side when displaying the result as a Cadenza view.
 
 
 ## Configuring the Extension
@@ -443,9 +462,9 @@ The [`CadenzaAnalyticsExtension`](cadenzaanalytics/cadenza_analytics_extension.h
 
 ```python
 my_extension = ca.CadenzaAnalyticsExtension(
-    relative_path="my-extension",
     print_name="My Extension",
     extension_type=ca.ExtensionType.DATA,
+    relative_path="my-extension",
     tables=[my_table],
     parameters=[my_param],
     analytics_function=my_analytics_function
@@ -453,9 +472,9 @@ my_extension = ca.CadenzaAnalyticsExtension(
 ```
 
 Parameters:
-- `relative_path`: URL path where the extension will be available
 - `print_name`: Display name in Cadenza
 - `extension_type`: One of `ExtensionType.DATA`, `ExtensionType.ENRICHMENT`, or `ExtensionType.VISUAL`
+- `relative_path`: URL path where the extension will be available
 - `tables`: List of Table objects (currently at most one table is supported) (optional)
 - `parameters`: List of Parameter objects (optional)
 - `analytics_function`: The function to invoke when the extension is called
@@ -488,7 +507,9 @@ def data_function(request: ca.AnalyticsRequest):
     return ca.DataResponse(result, columns)
 ```
 
-The `column_metadata` parameter specifies how Cadenza should interpret each column. If metadata for a column is missing, it will be auto-generated by default. You can change this behavior with `missing_metadata_strategy`:
+The `column_metadata` parameter specifies how Cadenza should interpret each column.
+If metadata for a column is missing, it will be auto-generated by default.
+You can change this behavior with `missing_metadata_strategy`:
 
 - `MissingMetadataStrategy.ADD_DEFAULT_METADATA` (default): Auto-generate metadata for missing columns
 - `MissingMetadataStrategy.REMOVE_DATA_COLUMNS`: Remove columns without metadata from the response
@@ -517,11 +538,17 @@ def enrichment_function(request: ca.AnalyticsRequest):
     return ca.EnrichmentResponse(data, [new_column])
 ```
 
-The library automatically handles Cadenza ID columns which are required to connect input and output data - they are taken from the request metadata and added to the response. You only need to specify metadata for the new columns you're adding, or you can use the default missing_metadata_strategy that handles adding column metadata heuristically.
+The library automatically handles Cadenza ID columns which are required to connect input and output data - they are taken from the request metadata and added to the response.
+You only need to specify metadata for the new columns you're adding, or you can use the default missing_metadata_strategy that handles adding column metadata heuristically.
 
-The library also supports automatically adding data for Cadenza ID columns if they are missing in the provided DataFrame. This requires the input and output DataFrames to have identical indexes. We therefore recommend modifying the input DataFrame directly and adding new columns to it. When you explicitly specify the desired output columns, index alignment is guaranteed, and you have full control over the output columns.
+The library also supports automatically adding data for Cadenza ID columns if they are missing in the provided DataFrame.
+This requires the input and output DataFrames to have identical indexes.
+We therefore recommend modifying the input DataFrame directly and adding new columns to it.
+When you explicitly specify the desired output columns, index alignment is guaranteed, and you have full control over the output columns.
 
-Currently, only one-to-one mappings between input and output rows are supported. The output may omit entries for some ID tuples present in the input. However, each input ID tuple can be mapped to at most one output ID tuple.
+Currently, only one-to-one mappings between input and output rows are supported.
+The output may omit entries for some ID tuples present in the input.
+However, each input ID tuple can be mapped to at most one output ID tuple.
 
 To return only specific columns, use `REMOVE_DATA_COLUMNS`:
 
@@ -716,8 +743,8 @@ docker run -p 8000:8000 my-extension
 ## Advanced Configuration
 
 ### Running behind Reverse Proxy
-
-When running behind a reverse proxy (like nginx), you may need to configure Flask to trust proxy headers. Use Werkzeug's `ProxyFix` middleware:
+When running behind a reverse proxy (like nginx), you may need to configure Flask to trust proxy headers.
+Use Werkzeug's `ProxyFix` middleware:
 
 ```python
 from werkzeug.middleware.proxy_fix import ProxyFix
@@ -730,11 +757,17 @@ analytics_service.app.wsgi_app = ProxyFix(
 
 ### Adjusting Maximum Request Size
 As of Werkzeug 3.1, the setting for `max_form_memory_size` is 500,000 bytes. 
-Since Cadenza sends the payload as `multipart/form` data, this default setting may prove to be too low to accomodate the data sent from Cadenza.
+Since Cadenza sends the payload as `multipart/form` data, this default setting may prove to be too low to accomodate the data sent from Cadenza, resulting in an _HTTP 413 Payload Too Large_ error.
 
-The setting can be adjusted using
+The setting can be adjusted via the Flask app properties:
 ```python
-from flask.wrappers import Request  # do NOT use the werkzeug.wrappers Request
-Request.max_form_memory_size = 100 * 1024 * 1024
+# Example setting the max form data to 100 MB
+analytics_service.app.config['MAX_FORM_MEMORY_SIZE'] = 100 * 1024 * 1024
 ```
 
+### Adjusting gunicorn worker timeouts
+In case of long running analyses that may cause a gunicorn worker to be silent for more than the standard of 30 seconds, increase the timeout by adding an appropriate parameter when starting gunicorn.
+```console
+gunicorn --bind 0.0.0.0:8000 --workers 4 --timeout 120 echo_extension:app
+```
+Also see [Gunicorn documentation](https://gunicorn.org/reference/settings/#timeout).