Problem Statement:
The primary challenge addressed is the need for an effective tool to analyse and categorize customer interactions. This includes classifying lead statuses, understanding reasons behind unclosed leads, and extracting essential data like customer names, CSR details, and concise notes. The transition from OpenAI to Cohere is driven by the need for improved performance and cost efficiency.
Solution Overview:
The solution involves integrating Cohere’s AI models, specifically tailored for classification and extraction tasks, into the customer interaction analysis process. This includes selecting and fine-tuning Cohere’s advanced language models using custom datasets and deploying these models for various data analysis tasks within customer service interactions.
Tech Stack Leveraged:
The tech stack centres around Cohere’s AI models for classification and extraction. These models are integrated into the existing customer service framework, with a focus on data processing workflows that can efficiently parse and analyse interaction data.
Benefits Delivered:
The adoption of Cohere’s AI models results in significant improvements in the accuracy of classification and extraction tasks. This leads to more efficient processing times and better resource utilization. Additionally, the solution provides a comparative advantage over previous OpenAI-based methods in terms of performance, cost, and customization capabilities. The case studies demonstrate practical applications and benefits, such as improved lead categorization and effective extraction of relevant information from customer interactions.
