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CX‑360 Insight Engine

AI‑Powered Customer Feedback Analysis with RAG, Clustering, Sentiment & Business Insights

The CX‑360 Insight Engine is an end‑to‑end customer‑experience analytics system built using:

  • LLM‑powered RAG (Groq + Llama 3.1)
  • Semantic embeddings
  • Topic clustering (t‑SNE + KMeans)
  • Sentiment analysis
  • Cluster naming using LLMs
  • Interactive Streamlit dashboard
  • Business‑ready insight generation

This project transforms raw customer feedback into actionable insights, root‑cause analysis, and prioritized recommendations — similar to enterprise VoC platforms like Medallia, Qualtrics, and Clarabridge.

Key Features

1. Intelligent RAG‑Based Insights

Ask any question about customer feedback and the engine will:

  • Retrieve the most relevant comments
  • Analyze sentiment distribution
  • Summarize themes
  • Provide executive‑ready insights
  • Recommend business actions
  • Generate a Customer Recovery Plan if negative sentiment is high

2. Topic Clustering (with Stable LLM‑Generated Names)

The system automatically:

  • Embeds all feedback
  • Reduces dimensionality (t‑SNE)
  • Clusters comments into topics
  • Uses an LLM to name each cluster
  • Saves names in cluster_names.json so they never change
  • Visualizes clusters in a 2D scatter plot

You also get:

  • A dropdown to inspect each cluster
  • All feedback belonging to that cluster
  • Clear explanation of what X/Y axes mean

3. Sentiment Analysis

Each comment is labeled as:

  • Positive
  • Neutral
  • Negative

Sentiment distribution is used in:

  • Insight generation
  • Cluster summaries
  • Business recommendations

4. Business Insight Engine

For every query, the system generates:

  • Executive summary
  • What customers are saying
  • Sentiment breakdown
  • Root‑cause analysis
  • Business impact
  • Prioritized recommendations
  • Customer recovery plan (if needed)
  • Strengths (if sentiment is positive)

How It Works

1. Data Processing

  • Clean text
  • Compute embeddings
  • Compute sentiment
  • Store results

2. RAG Retrieval

  • Retrieve top 300 semantically similar comments
  • Compute sentiment distribution
  • Pass context to LLM

3. Insight Generation

LLM produces:

  • Themes
  • Patterns
  • Recommendations
  • Recovery plan
  • Executive summary

4. Topic Clustering

  • t‑SNE → 2D coordinates
  • KMeans → cluster labels
  • LLM → cluster names
  • JSON → persistent naming

Cluster Visualization

The scatter plot shows:

  • Each point = one customer comment
  • Colors = cluster groups
  • X/Y axes = semantic dimensions (not sentiment)
  • Negative/positive values = position in embedding space

Interpretation:

  • Points close together → similar meaning
  • Points far apart → different topics

Tech Stack

  • Core
  • Python
  • Streamlit
  • Groq LLMs (Llama‑3.1‑8B‑Instant / Llama‑3.3‑70B‑Versatile)
  • SentenceTransformers
  • scikit‑learn
  • UMAP / t‑SNE
  • Pandas
  • LLM
  • Groq API
  • RAG retrieval
  • Insight generation
  • Cluster naming

Project Structure

Code CX-360/ │ ├── app.py ├── requirements.txt ├── README.md │ ├── src/ │ ├── rag.py │ ├── prompts.py │ ├── clustering.py │ ├── sentiment.py │ ├── utils.py │ └── cluster_names.json │ └── data/ └── sample_feedback.csv 🔧 Setup Instructions

  1. Install dependencies Code pip install -r requirements.txt
  2. Set your Groq API key Code export GROQ_API_KEY="your_key_here" (Linux/Mac syntax) set GROQ_API_KEY=your_key_here (Windows)
  3. Run the app Code streamlit run app.py

Future Improvements

  • Add topic‑level sentiment dashboards
  • Add SHAP explanations for sentiment model
  • Add multi‑language support
  • Add PDF export for insights
  • Add automated weekly CX reports

Why This Project Stands Out

This is not a basic NLP project — it is a full customer‑insight engine combining:

  • Machine learning
  • LLM reasoning
  • RAG
  • Clustering
  • Visualization
  • Business analytics