SupportPilot AI is a project I built to explore how AI agents can make customer support workflows faster and more organized.
The idea is simple: instead of manually going through support requests, finding relevant information, and drafting responses, SupportPilot brings these steps together in one workflow.
- 📩 Processes incoming customer support requests
- 🔎 Searches relevant company knowledge
- ✍️ Helps draft support responses
- 🎫 Supports engineering ticket creation
- 🔗 Provides a structure for connecting internal tools
- 🖥️ Includes a Streamlit interface for interacting with the project
At a high level, SupportPilot follows a workflow like this:
Customer Request
↓
SupportPilot Agent
↓
Knowledge Search
↓
Relevant Information
↓
Draft Response / Action
↓
Internal Tools
The project is structured around an agent layer and connector modules so that different services can be integrated as the project evolves.
🛠️ Tech Stack
Language
Python
AI / ML
AI Agents
NLP
LLM-based workflows
Application
Streamlit
Integrations
Gmail
Notion
Jira
Resend
GitHub
📁 Project Structure
SupportPilot-AI/
│
├── supportpilot/
│ ├── agent.py
│ ├── app.py
│ └── connectors/
│
├── .env.example
├── .gitignore
├── requirements.txt
└── README.md
Key files
supportpilot/agent.py — core agent orchestration
supportpilot/app.py — Streamlit interface
supportpilot/connectors/ — connector modules for external services
.env.example — example environment configuration
Getting Started
1. Clone the repository
git clone https://github.com/tahameeahsan/SupportPilot-AI.git
cd SupportPilot-AI
2. Create a virtual environment
python -m venv venv
Activate it on Windows:
venv\Scripts\activate
On macOS/Linux:
source venv/bin/activate
3. Install dependencies
pip install -r requirements.txt
4. Configure environment variables
Create a .env file using .env.example as a reference.
Add your own credentials locally.
Never commit your .env file or API keys to GitHub.
5. Run the Streamlit app
streamlit run supportpilot/app.py
What I'm learning through this project
This project is helping me get more hands-on experience with:
AI agent architecture
LLM-powered workflows
Knowledge retrieval
API integrations
Building practical AI applications
Structuring Python projects for real-world use
🔮 What's next
Some areas I plan to explore further:
Better knowledge search and ranking
More robust response generation
Deeper integrations with external tools
Automated testing
CI/CD workflows
Improved agent evaluation
👨💻 About
I'm a B.Tech CSE (AI/ML) student interested in building practical AI and machine learning applications.
I'm using projects like SupportPilot AI to move beyond tutorials and learn by actually building, testing, and improving things.
⭐ If you find this project interesting, feel free to explore the code or connect with me.
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