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

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

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  • 0 Students
  • 95 Lessons
  • Rahul Sahay

Course Overview

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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.

Happy Learning

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Course curriculum

Requirment

  • 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.

Outcomes

  • 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.

Instructor

...
Rahul Sahay

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