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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:
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Design a modular backend using Clean Architecture principles and the repository pattern.
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Implement semantic search by generating and storing embeddings using Azure OpenAI or Ollama, backed by PostgreSQL + pgvector.
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Create an AI chatbot assistant capable of natural language understanding and contextual product recommendations.
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Integrate multiple search modes — Catalog, Semantic, and Hybrid — that deliver smart, intent-based results.
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Develop a dynamic Angular 20 frontend using standalone components and Signals API for responsive data binding.
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Add a complete basket and checkout flow with persistent data management.
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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:
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Implement a RAG pipeline that combines vector search, document retrieval, and generative AI for context-aware answers.
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Add voice input and output, enabling users to interact naturally through speech.
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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
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Backend: .NET 9, ASP.NET Core Minimal APIs, C#
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Frontend: Angular 20 with Standalone Components & Signals API
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AI Integration: Azure OpenAI, Ollama, pgvector (PostgreSQL)
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Gateway: Ocelot API Gateway
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Containerization: Docker & Docker Compose
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Hosting: Local or Cloud-based deployment (Azure-ready)
Who Is This Course For
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Developers who want to integrate AI capabilities into real-world applications.
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.NET and Angular engineers looking to master semantic search and RAG-based intelligence.
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Architects designing next-generation, AI-enabled microservices and e-commerce platforms.
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Learners eager to gain hands-on experience in building full-stack, AI-powered systems.
Course Stats
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10+ hours of in-depth, project-based learning (Phase 1).
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95+ practical coding sessions, all demonstrated step-by-step.
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Lifetime access, free updates, and new features with every phase.
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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:
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Built a production-grade AI e-commerce system powered by .NET 9 and Angular 20.
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Implemented semantic search, vector-based intelligence, and chatbot interaction.
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Deployed a containerized AI stack ready for RAG, voice, and web-integrated intelligence.
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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
