diff --git a/learn/fine_tuning_results/sorting.mdx b/learn/fine_tuning_results/sorting.mdx
index 84f7dd0cca..dc6cfce034 100644
--- a/learn/fine_tuning_results/sorting.mdx
+++ b/learn/fine_tuning_results/sorting.mdx
@@ -313,6 +313,6 @@ The following query will sort results based on how close they are to the Eiffel
 
 ## Example application
 
-Take a look at our demos to see how sorting is implemented. The source code is available on Github for both demos:
-- **Ecommerce search**: [Preview](https://ecommerce.meilisearch.com/?utm_campaign=oss&utm_source=docs&utm_medium=sorting) • [Repository](https://github.com/meilisearch/ecommerce-demo/)
-- **In-app search**: [Preview](https://saas.meilisearch.com/?utm_campaign=oss&utm_source=docs&utm_medium=sorting) • [Repository](https://github.com/meilisearch/ecommerce-demo/)
+Take a look at our demos for examples of how to implement sorting:
+- **Ecommerce demo**: [Preview](https://ecommerce.meilisearch.com/?utm_campaign=oss&utm_source=docs&utm_medium=sorting) • [Github repository](https://github.com/meilisearch/ecommerce-demo/)
+- **CRM SaaS demo**: [Preview](https://saas.meilisearch.com/?utm_campaign=oss&utm_source=docs&utm_medium=sorting) • [Github repository](https://github.com/meilisearch/saas-demo/)