RootCause-AI-Powered Customer Complaint Analyzer

💡 Inspiration

Every business collects tons of customer feedback — but most never get analyzed. We wanted to create an 🤖 AI tool that turns those unread complaints into actionable insights, revealing real problems hidden in the noise.

⚙️ What it does

RootCause Radar 🧠 automatically clusters similar complaints, summarizes their root causes, and visualizes key pain points. It also detects 😠 sentiment 😐 trends 😊 emotions — helping businesses understand and prioritize fixes.

🏗️ How we built it

We used Python 🐍, Streamlit 🧱 for UI, SentenceTransformers for embeddings, KeyBERT 🧩 for keyword-based summaries, Scikit-learn 🔍 for clustering, Plotly 📊 for data visualization, and NLTK 💬 for sentiment analysis — all open-source and running locally for privacy.

🚧 Challenges we ran into

Balancing ⚖️ clustering accuracy with performance on large datasets was tricky. We also had to tune embeddings and generate meaningful summaries without relying on paid APIs or large cloud models.

🏆 Accomplishments that we're proud of

We built a fully offline, explainable, and beautiful AI system that gives businesses real insights instantly. The dashboard feels intuitive 🎯, visual 👀, and practical 💼 — no code, no cloud, just clarity.

📚 What we learned

We learned how to merge unsupervised learning 🤓 with explainable NLP techniques, optimize embeddings for clustering, and design an interactive AI tool that bridges technical innovation with business usability.

🚀 What's next for RootCause-AI-Powered Customer Complaint Analyzer

We plan to add 📈 trend tracking, 🕒 real-time complaint monitoring, 🤝 CRM integration, and 💡 automated fix recommendations — making RootCause Radar the go-to AI co-pilot for customer experience teams worldwide. 🌍

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Built With

• k-means-clustering
• keybert
• python
• streamlit