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The video follows one project from start to finish: an AI customer support agent that answers messages arriving in a Gmail inbox. The backend is written with FastAPI, the agent with Pydantic AI and Claude, and data is stored in Supabase, with a React admin dashboard on top.
Python developers who want to see an end-to-end AI application, including deployment, will get the most from it. Viewers new to Python may want the introductory material the creator links in the description first.
In this full AI Engineering course, I show you how to build an AI customer support agent from start to finish.
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📘 Course Handbook: https://learn.datalumina.com/docs/customer-support-automation
New to Python? Watch this first: https://youtu.be/ygXn5nV5qFc
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⏱️ Timestamps
0:00:00 Introduction
0:06:57 Project setup
0:20:37 Gmail API access
0:36:23 Gmail client
0:45:27 The poller
0:53:48 Database
1:10:36 First reply
1:18:14 Knowledge base
1:23:21 AI pipeline
1:55:31 Evals
2:08:41 Server setup
2:23:41 Deploy with Docker
2:44:54 Admin dashboard
3:05:19 Wrap up
📌 Description
In this full AI Engineering course, we connect a Gmail inbox, build the backend with FastAPI and the agent with Pydantic AI and Claude, store everything in Supabase, test it with evals and add a React admin dashboard. Then we deploy the whole system to a real server with Docker on Hostinger, so you end up with a project you can show in your next job interview.
👋🏻 About Me
Hi! I'm Dave, AI Engineer and founder of Datalumina®. On this channel, I share practical tutorials that teach developers how to build production-ready AI systems that actually work in the real world. Beyond these tutorials, I also help people start successful freelancing careers. Check out the links above to learn more!
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