diff --git a/.docs/why.md b/.docs/why.md
index 1e6e52cc7f2d0c932fd5640cdfde752d48862e30..2a269cdadb5032d5b482f1bda9b1e0c6804d1756 100644
--- a/.docs/why.md
+++ b/.docs/why.md
@@ -2,8 +2,6 @@
 author: Martin Weise
 ---
 
-## Why use DBRepo?
-
 Digital repositories see themselves more frequently encountered with the problem of making databases accessible in their
 collection. Challenges revolve around organizing, searching and retrieving content stored within databases and
 constitute a major technical burden as their internal representation greatly differs from static documents most digital
@@ -21,28 +19,29 @@ evolving, allows reproducing of query results and supports findable-, accessible
 
 DBRepo makes your dataset searchable without extra effort: most metadata is generated automatically for data in your 
 databases. The fast and powerful OpenSearch database allows a fast retrieval of any information. Adding semantic mapping
-through a suggestion-feature, allows machines to properly understand the context of your data. [Learn more.](../system-services-search/)
+through a suggestion-feature, allows machines to properly understand the context of your data
+[Learn more](../concepts/search).
 
 ### Citable datasets
 
 Adopting the recommendations of the RDA-WGDC, arbitrary subsets can be precisely, persistently identified using
 system-versioned tables of MariaDB and the DataCite schema for minting DOIs. External systems i.e. metadata harvesters
-(OpenAIRE, Google Datasets) can access these datasets through OAI-PMH, JSON-LD and FAIR Signposting protocols.
-[Learn more.](../system-services-metadata/)
+(OpenAIRE, Google Datasets) can access these datasets through OAI-PMH, JSON-LD and FAIR Signposting protocols
+[Learn more](../concepts/pid).
 
 ### Powerful API for Data Scientists
 
 With our strongly typed Python Library, Data Scientists can import, export and work with data from Jupyter Notebook or
 Python script, optionally using Pandas DataFrames. For example: the AMQP API Client can collect continuous data from
-edge devices like sensors and store them asynchronous in DBRepo. [Learn more.](../usage-python/)
+edge devices like sensors and store them asynchronous in DBRepo [Learn more](../api/python).
 
 ### Cloud Native
 
 Our lightweight Helm chart allows for installations on any cloud provider or private-cloud setting that has an
 underlying PV storage provider. DBRepo can be installed from 
 the [Artifact Hub](https://artifacthub.io/packages/helm/dbrepo/dbrepo) repository. Databases are managed as MariaDB
-Galera Cluster with high degree of availability ensuring your data is always accessible.
-[Learn more.](../deployment-helm/)
+Galera Cluster with high degree of availability ensuring your data is always accessible
+[Learn more](../kubernetes).
 
 ## Demo Site