Overview
A company aimed to build a multilingual conversational AI assistant capable of handling customer queries in over 10 languages, including regional languages.
The project focused on increasing customer engagement, reducing call center load, and providing an intelligent self-service experience.
Problems
Existing chatbot systems were language-limited, forcing users to interact only in select languages.
The support center struggled to scale operations to match growing customer queries from diverse language backgrounds.
Lack of contextual understanding in bots resulted in inaccurate responses and customer frustration.
Integration with backend systems and CRMs was limited, preventing automated ticket resolution.
Third-party translation layers had latency issues and poor accuracy for regional dialects.
Solutions
A multilingual NLP engine was implemented and fine-tuned on domain-specific datasets, ensuring accurate language understanding across multiple languages and dialects.
The AI assistant integrated with CRM, ticketing, and order tracking systems to enable automated resolutions and contextual answers.
Real-time translation and intent classification modules ensured smooth language switching without loss of meaning.
A fallback mechanism with live agents ensured zero customer drop-offs.
This resulted in a 60% reduction in support tickets and a 35% improvement in customer satisfaction scores.
