AI-Powered E-Commerce App with .NET 9, Angular 20 & RAG

[100% Off] Ai-Powered E-Commerce App With .Net 9, Angular 20 &Amp; Rag

Build a full-stack AI-enabled store with Semantic Search, Chatbot, and RAG integration using .NET 9, Angular 20 & Azure

Added on January 22, 2026 Development 4 min read

What you’ll learn

Build a fully functional
production-grade AI-powered e-commerce application using .NET 9 and Angular 20.
Integrate semantic search with vector embeddings using Azure OpenAI or Ollama and pgvector in PostgreSQL.
Implement a chatbot assistant that understands natural-language queries and recommends products contextually.
Design and structure a modular backend following Clean Architecture principles and repository pattern.
Build dynamic
responsive Angular components using standalone architecture and the new Signals API.
Add hybrid search functionality combining traditional catalog search with semantic intelligence.
Containerize backend
database
and frontend services using Docker Compose for easy local deployment.
Configure Ocelot API Gateway for routing
service orchestration
and environment-based configuration.
Prepare your system for Retrieval-Augmented Generation (RAG) to combine retrieval and generative reasoning.
Gain real-world experience in connecting microservices
AI models
and cloud infrastructure into one cohesive solution.

Requirements

Basic understanding of C# and the .NET ecosystem.
Familiarity with Angular
TypeScript
or any frontend framework.
Knowledge of RESTful APIs
JSON
and HTTP methods.
A working knowledge of databases such as SQL Server or PostgreSQL.
Basic Git/GitHub familiarity for project versioning.
No prior AI or OpenAI experience is required — all concepts are covered step by step.

Description

Disclaimer:- This course requires you to download “Docker Desktop” from Docker website. If you are a Udemy Business user, please check with your employer before downloading software.

Welcome to “AI-Powered E-Commerce App with .NET 9, Angular 20 & RAG”

Have you ever imagined transforming a standard e-commerce store into an intelligent, AI-enabled platform that understands your users’ intent?
In this course, you’ll learn to build a modern, semantic search and chatbot-powered online store that’s ready for Retrieval-Augmented Generation (RAG) — using .NET 9, Angular 20, Azure OpenAI, and PostgreSQL (pgvector).

In this hands-on course, you’ll go far beyond theory. You’ll build, run, and integrate AI capabilities step by step — from foundational architecture to advanced generative intelligence — all within a clean, scalable, production-ready system.

Course Phases

Phase 1 – Building the AI-Enabled Foundation (Completed)

In this phase, you’ll develop a fully functional, AI-ready e-commerce system powered by .NET 9 and Angular 20.
This is not a toy project — you’ll build real, production-grade components and integrate intelligent features end to end.

You will:

Design a modular backend using Clean Architecture principles and the repository pattern.
Implement semantic search by generating and storing embeddings using Azure OpenAI or Ollama, backed by PostgreSQL + pgvector.
Create an AI chatbot assistant capable of natural language understanding and contextual product recommendations.
Integrate multiple search modes — Catalog, Semantic, and Hybrid — that deliver smart, intent-based results.
Develop a dynamic Angular 20 frontend using standalone components and Signals API for responsive data binding.
Add a complete basket and checkout flow with persistent data management.
Configure Ocelot API Gateway for service routing and Docker Compose for containerized deployment.

By the end of Phase 1, you will have a fully operational AI-driven store capable of handling real-time chat queries, intelligent product discovery, and hybrid semantic search — ready for the next phase of true RAG integration.

Phase 2 – Advancing to RAG-Powered Intelligence (Coming Soon)

In Phase 2, you’ll take your AI assistant to the next level by introducing Retrieval-Augmented Generation (RAG), Voice Assistant Integration, and Web Search Augmentation.

You will:

Implement a RAG pipeline that combines vector search, document retrieval, and generative AI for context-aware answers.
Add voice input and output, enabling users to interact naturally through speech.
Integrate context memory, allowing the assistant to maintain awareness across multiple turns in the conversation.

By the end of Phase 2, your application will evolve into a fully RAG-powered conversational shopping assistant that can reason, retrieve, and respond like a true AI companion.

Tech Stack

Backend: .NET 9, ASP.NET Core Minimal APIs, C#
Frontend: Angular 20 with Standalone Components & Signals API
AI Integration: Azure OpenAI, Ollama, pgvector (PostgreSQL)
Gateway: Ocelot API Gateway
Containerization: Docker & Docker Compose
Hosting: Local or Cloud-based deployment (Azure-ready)

Who Is This Course For

Developers who want to integrate AI capabilities into real-world applications.
.NET and Angular engineers looking to master semantic search and RAG-based intelligence.
Architects designing next-generation, AI-enabled microservices and e-commerce platforms.
Learners eager to gain hands-on experience in building full-stack, AI-powered systems.

Course Stats

10+ hours of in-depth, project-based learning (Phase 1).
95+ practical coding sessions, all demonstrated step-by-step.
Lifetime access, free updates, and new features with every phase.
Real-world architecture you can extend, deploy, and showcase.

Why This Course

This isn’t a basic chatbot tutorial. By the end of this course, you’ll have:

Built a production-grade AI e-commerce system powered by .NET 9 and Angular 20.
Implemented semantic search, vector-based intelligence, and chatbot interaction.
Deployed a containerized AI stack ready for RAG, voice, and web-integrated intelligence.
Gained the expertise to design and scale AI-first enterprise applications.

Your journey to building an AI-Powered E-Commerce Platform starts here.Enroll today and learn to combine software engineering, AI integration, and full-stack development — all in one real-world project.