Problem Statement
The main challenge addressed is the extraction of critical information from customer service call transcripts. This involves identifying customer identities, categorizing lead statuses, extracting CSR names, and understanding reasons behind unclosed leads, leveraging the advanced capabilities of OpenAI’s Davinci model.
Solution Overview
The solution involves a two-step process: first, utilizing OpenAI’s Whisper model to accurately transcribe spoken language from call recordings into text, and second, employing the Davinci model to meticulously extract key information from these transcripts. This includes fine-tuning Davinci to identify specific data points such as customer names, lead statuses, CSR names, and generative notes for uncategorized leads.
Tech Stack Leveraged
The approach integrates two OpenAI models: Whisper for transcription and Davinci for data extraction. The methodology includes acquiring call recordings, transcribing them, and then processing the transcripts through Davinci to extract and categorize pertinent information.
Benefits Delivered
This AI-driven method significantly enhances the precision and time efficiency of call analysis compared to manual methods. The case study demonstrates the effectiveness of this approach in real-world scenarios, showcasing how AI models can streamline processes and potentially scale within call-centres. The conclusion anticipates future advancements in AI-driven call analysis and highlights areas for further refinement.