diff --git a/nemo/data-flywheel/embedding-finetuning/README.md b/nemo/data-flywheel/embedding-finetuning/README.md
index 8845f551e..26a438ff9 100644
--- a/nemo/data-flywheel/embedding-finetuning/README.md
+++ b/nemo/data-flywheel/embedding-finetuning/README.md
@@ -24,7 +24,11 @@ The tutorial covers the following steps:
 
 ### About NVIDIA NeMo Microservices
 
-The NVIDIA NeMo microservices platform provides a flexible foundation for building AI workflows such as fine-tuning, evaluation, running inference, or applying guardrails to AI models on your Kubernetes cluster on-premises or in cloud. Refer to [documentation](https://docs.nvidia.com/nemo/microservices/latest/about/index.html) for further information.
+NVIDIA NeMo is a modular, enterprise-ready software suite for managing the AI agent lifecycle, enabling enterprises to build, deploy, and optimize agentic systems.
+
+NVIDIA NeMo microservices, part of the [NVIDIA NeMo software suite](https://www.nvidia.com/en-us/ai-data-science/products/nemo/), are an API-first modular set of tools that you can use to customize, evaluate, and secure large language models (LLMs) and embedding models while optimizing AI applications across on-premises or cloud-based Kubernetes clusters.
+
+Refer to the [NVIDIA NeMo microservices documentation](https://docs.nvidia.com/nemo/microservices/latest/about/index.html) for further information.
 
 ### About the SPECTER dataset
 
@@ -90,4 +94,4 @@ Ensure you have access to:
    uv run jupyter lab --ip 0.0.0.0 --port=8888 --allow-root
    ```
 
-5. Navigate to the [data preparation notebook](./1_data_preparation.ipynb) to get started.
\ No newline at end of file
+5. Navigate to the [data preparation notebook](./1_data_preparation.ipynb) to get started.