{"repo":"ReverendBayes/AI-Powered-Call-Center-Intelligence","free":true,"listed":false,"github":"https://github.com/ReverendBayes/AI-Powered-Call-Center-Intelligence","clone":"git clone https://github.com/ReverendBayes/AI-Powered-Call-Center-Intelligence.git","description":"Real-time behavioral intelligence for call centers. Transcribes support calls, redacts PII, extracts emotional tone, classifies issues, and delivers insight-rich dashboards — powered by GPT-3.5 (cheap tokens), Whisper, DuckDB, and a polished React+TypeScript frontend. No Azure. No Power BI. No vendor lock-in. Just full-stack AI that runs local.","language":"Python","stars":30,"topics":["altair","callcenter-analysis","churn-prediction","duckdb","emotion-recognition","fastapi","huggingface","jupyter-notebook","openai","pii-redaction"],"license":"MIT","category":"ai-agents","readme_excerpt":"NOTE: This project is no longer maintained due to OpenAI's deprecation of Whisper compatibility with GPT-3.5. However, for now, it can still be used by installing Whisper directly via pip install git+https://github.com/openai/whisper.git AI-Powered Call Center Intelligence A full-stack, local-first behavioral intelligence engine for telecom support calls. Combining Whisper transcription, GPT-3.5 insights (affordable token cost), PII redaction, next best offer generation, and visual analytics — this project gives supervisors real-time understanding of what customers feel, need, and signal during calls. It doesn't stop at classification: it helps supervisors act, follow up, and retain. --- Optional: Integration with a Trained Machine Learning Churn Predictor (\\ 95% Accuracy) This platform can be combined with the Telecom Churn Predictor to extend real-time call analysis into actionable retention strategies. When enabled, the pipeline uses PII redaction to extract the customer’s phone number, then passes it into the model to return a churn risk classification — High , Medium , or Low — based on historical behavior patterns. This integration is critical because only 1 in 26 customers will actually inform a company before leaving. The model surfaces silent churn signals that behavioral call analysis alone may miss, providing a more objective and data-grounded view of retention risk. The churn model: Predicts which customers are likely to leave using a stacked ensemble of four clas","default_branch":null,"files":null,"tree":[],"storefront":"/r/ReverendBayes","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/ReverendBayes/AI-Powered-Call-Center-Intelligence/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}