Problem Statement :
A global technology company required a Conversational AI System that could enhance customer understanding and respond intelligently.
Solution:
A platform for Conversational AI systems was created using domain-specific language models, allowing the system to understand the entities and intents of customers. The machine learning algorithm was used to ingest client data and create the first version of the Conversational AI System. Active learning and deep learning algorithms were used to refine the system.
The platform for Conversational AI systems uses a variety of technologies and tools, including Conditional Random Fields, Sentiment Analysis, Topic Modeling, Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM). These advanced language models allow the system to recognize various domain-specific entities and intents, enhancing its ability to understand and respond appropriately to customers.
The platform also uses active learning and deep learning algorithms to continuously improve the Conversational AI System through simulated interactions with the author. Overall, the platform enables the creation of Conversational AI Systems with a deeper ability to understand and converse with humans.
Benefits :
The platform reduced manual effort and created Conversational AI Systems with a deeper understanding of customers, resulting in intelligent responses. The iterative approach improved the system continuously.
