cxm-feedback-agent

🤖 CXM Feedback Agent

An AI-powered Customer Feedback Classification Model fine-tuned on enterprise CXM feedback data using DistilBERT.

The model automatically categorizes customer feedback into business-specific classes, enabling organizations to automate customer feedback analysis, reporting, and future AI-driven insights.

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🚀 Features

✅ Customer Feedback Classification

✅ Enterprise Business Categories

✅ Fine-Tuned DistilBERT

✅ Hugging Face Transformers Compatible

✅ FastAPI Ready

✅ Enterprise Deployment Ready

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🎯 Business Categories

The model predicts one of the following categories:

• 📂 Case Management
• 📢 Complaint Management
• 💡 Feature Request
• 📝 General Feedback
• ⚡ Performance Issue
• 😊 Positive Feedback
• 🔄 Process Improvement
• 🎨 UI/UX Improvement

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🧠 Base Model

distilbert-base-uncased

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📊 Training Dataset

Current POC

• 📄 38 Human-Labeled Customer Feedback Samples
• 🌐 English Language
• 🏢 Enterprise CXM Portal Feedback

Planned Production Version

• 📈 500–1000+ Human Reviewed Feedbacks
• ✅ Balanced Categories
• 🧹 Clean Gold Dataset
• 🔍 Quality Verified Labels

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🎯 Intended Use

This model is designed for:

• 📊 Customer Feedback Analysis
• 📢 Complaint Classification
• 💡 Feature Request Detection
• 📈 Business Intelligence
• 🤖 AI Customer Support
• 📋 Enterprise Reporting
• 📌 CXM Analytics

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💻 Example Usage

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="Tayyab-ilyas/cxm-feedback-agent"
)

result = classifier(
    "Please add bulk closure option."
)

print(result)

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📝 Example Predictions

Input

Please add a bulk closure option.

Prediction

💡 Feature Request

---

Input

The system hangs every few minutes.

Prediction

⚡ Performance Issue

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Input

The portal is very easy to use.

Prediction

😊 Positive Feedback

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⚙️ Training Configuration

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📈 Current Status

✅ Model Successfully Trained

✅ Uploaded to Hugging Face

✅ Inference Working

✅ API Ready

🟡 Proof of Concept (POC)

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⚠️ Current Limitations

This is an initial Proof of Concept.

Current limitations include:

• 📉 Small training dataset (38 samples)
• ⚖️ Class imbalance
• 🌍 English-only feedback
• 🎯 Limited generalization

The next version will be trained using 500+ manually reviewed enterprise feedback records to significantly improve prediction accuracy.

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🛣️ Roadmap

Version 1.0 ✅

• Fine-Tuned DistilBERT
• Enterprise Categories
• Hugging Face Deployment

Version 2.0 🚀

• 500+ Gold Dataset
• Improved Accuracy
• Better Generalization

Version 3.0 🤖

• FastAPI Deployment
• Authentication
• REST API
• Docker Support

Version 4.0 🧠

• RAG Integration
• Feedback Search
• AI Assistant
• Enterprise Analytics

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🏗️ Enterprise Architecture

Customer Feedback
        │
        ▼
🤖 DistilBERT Feedback Agent
        │
        ▼
📂 Business Category
        │
        ▼
⚡ FastAPI REST API
        │
        ▼
📊 Dashboard / CXM / CRM

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👨‍💻 Author

Tayyab Ilyas

AI Engineer | Enterprise AI Solutions | Customer Experience Analytics

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🤝 Contributions

Feedback, suggestions, and contributions are welcome.

Feel free to open an Issue or Pull Request.

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📜 License

MIT License

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⭐ If you find this project useful, consider giving it a Star on Hugging Face!