An advanced AI-driven chatbot for a USA-based telecommunications provider to manage FAQs, keyword search, and input-output processes in customer service to nurture user satisfaction and minimize maintenance costs.
About the client
The client, a significant US-based telecom company handling over 1 million customer inquiries monthly, needed cutting-edge AI and ML technologies. They expected to modernize their customer service operations, reduce response times, and handle increasing query volumes while maintaining service quality.
About the
Product:
An AI-powered customer support chatbot has obtained vast functionality. Based on machine learning components, it offers FAQs upon request, keyword search, and so on. As a result, the solution enhances customer satisfaction with accurate and correct answers 24/7 while reducing maintenance costs.
Introduction:
As the client intended to streamline customer support with the AI-powered features on multiple platforms, we used Python and Docker as the main tech stack to develop and maintain a scalable, efficient chatbot within a limited time.
Moreover, we were expected to improve UX and UI components and refactor some code to ensure a smooth, downtime-free performance regardless of peak workloads.
Project
Team:
A dedicated team of five specialists, including a backend developer, a frontend developer, a machine learning expert, an AI integration specialist, and a QA expert, pushed the project to a new level of efficiency.
Challenges:
Integrating AI features into the existing system revealed several technical challenges: complex legacy system documentation, compatibility issues between existing components and new AI models, and the need to maintain uninterrupted service during the upgrade process.
The intricacy of several system components and the substantial amount of continuous maintenance required to keep the system functioning properly.
Tech
Stack:
Solution:
We’ve improved the existing chatbot solution, adapting the code platform and implementing specific AI capabilities: a natural language processing engine for query understanding, a machine learning model trained on 50,000+ historical customer interactions, and databases for integration. Plus, the chatbot’s enhanced functionality included:
Additionally, ongoing development, maintenance, and refactoring were carried out to ensure the system’s scalability. As a result, the AI-driven system is capable of multiple functions, all of which contribute to a seamless customer experience.
Results:
*according to the internal metrics, as compared to the same quarter of the last year.
Sum Up:
We are proud of this project since we’ve managed to combine a creative approach and solid ML knowledge. The client has reached the business goals as the chatbot has successfully boosted customer service and cut operating expenses. And we have acquired additional practical expertise in the AI field.
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