How We Improved Agent Chat Efficiency with Machine Learning

This post details the development and implementation of SmartChat, a machine learning model designed to improve agent chat efficiency by providing contextual sentence completion suggestions. It covers the problem discovery, design iterations (including UI/UX considerations inspired by Gmail's Smart Compose), key metrics for tracking progress (characters saved, model effectiveness, acceptance rate, latency), the system architecture involving the Catwalk ML serving platform and API gateway, and the design of the ML model itself. The model uses a GRU-based Encoder-Decoder architecture with specific features like past conversations, time of day, and user type as inputs. The UI implementation uses a typeahead solution in ReactJS.