Over the past few days, I’ve been working on a small customer-support automation project using FastAPI and LangChain.
The idea was straightforward: build a lightweight backend service that can handle common support questions, classify the user’s intent, and generate a clear response without needing a large, complex infrastructure.
I started by structuring simple FastAPI endpoints that receive a user message and pass it through an intent-classification step. From there, the workflow routes the request to the right handler-general FAQ, troubleshooting, order-related questions, or fallback support. LangChain handles the reasoning layer, so each reply stays consistent and follows a controlled format.
One of the goals was to keep the project useful for both local testing and production environments. To make that easier, the system can run in mock mode (no API calls) or switch to real LLM responses using OpenAI. This helped a lot during debugging and made the pipeline more predictable.
What I like most about this setup is how small businesses can use something like this to reduce repetitive support work. Even a simple intent classifier + structured response generator can save a team hours each week.
If you’re exploring ways to automate support workflows or want to see how this kind of pipeline works behind the scenes, I’m happy to share more details.
Here’s the repo for anyone who wants to look at the code:
https://github.com/izharhaq1986/chatgpt-customer-support-fastapi
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