Update README.md

README.md

@@ -18,16 +18,16 @@ We make it easier to deploy AI models in real-world applications by bridging the
 ## 🚀 What We Build
 
 ### Vector Search Applications
-Semantic search engines, recommendation systems, and similarity-based retrieval using MongoDB Atlas Vector Search with embeddings from transformer models.
+Semantic search engines, recommendation systems, and similarity-based retrieval using Hugging Face transformer models for embeddings and MongoDB Atlas Vector Search for scalable storage and retrieval.
 
 ### RAG Systems
-Retrieval-augmented generation pipelines that combine large language models with MongoDB as the knowledge base for accurate, context-aware responses.
+Retrieval-augmented generation pipelines combining Hugging Face large language models with MongoDB as the knowledge base for accurate, context-aware responses.
 
 ### Multimodal Applications
-Image search, audio processing, and cross-modal retrieval systems leveraging Hugging Face's diverse model ecosystem.
+Image search, audio processing, and cross-modal retrieval systems leveraging Hugging Face's diverse model ecosystem with MongoDB for data management.
 
 ### Production ML Workflows
-End-to-end pipelines from data ingestion and embedding generation to model serving and result ranking at scale.
+End-to-end pipelines from data ingestion, embedding generation with Hugging Face models, to model serving and result ranking at scale with MongoDB Atlas.
 
 ## 📦 What You'll Find Here