Multilingual Conversational Dataset for Chatbot Training

Client's Challenge & Our Solution

A global technology company aimed to train an NLP-powered chatbot for customer support across diverse domains, including BFSI, Retail, E-commerce, and Real Estate. The challenge was to create high-quality, multilingual conversational datasets that represented realistic agent-customer interactions in 12+ languages. The datasets needed to include intent classification at each conversational turn, with chats varying significantly in size, tone, topic, and outcomes (positive, negative, neutral).

FutureBeeAI crafted 10,000 diverse chats with lengths ranging from 15 to 150 conversational turns. Each conversation was annotated with intent labels at every step, ensuring the dataset’s usability for training intent-driven chatbots. To ensure robust training, we also delivered guardrail chats, to ensure the chatbot effectively handles edge cases.

Outcome & Features:

Delivered 10,000 multilingual conversational datasets across 12+ languages, including realistic scenarios across multiple domains.

Chats varied in length, ranging from 15 to 150 turns, mimicking real-world interactions and improving chatbot adaptability.

Completed the project in just 12 weeks, providing high-quality, domain-specific chat datasets with annotation to meet the client’s tight timeline.