Conversational AI for Web Chats Case Study

Existing chatbot systems were too rigid—relying on scripted flows that quickly broke down outside predefined paths. They couldn’t handle unexpected queries, lacked conversational memory, and delivered robotic responses that discouraged users from engaging further. Additionally, infrastructure limits made it hard to support large-scale concurrent sessions with dynamic topic switching. Without a robust and adaptive AI system, the platform risked low retention, poor user satisfaction, and high operational overhead from live support.

The team developed a custom conversational AI model fine-tuned for dynamic, multi-topic dialogue, backed by a scalable infrastructure capable of supporting real-time interactions at scale. Using transformer-based architecture and retrieval-augmented generation (RAG), the system responded contextually to diverse user inputs while maintaining a consistent tone and memory across sessions. A custom frontend component was integrated into the client’s web interface, enabling seamless chat experiences without lag or drop-off. The result: significantly higher engagement metrics, reduced dependency on live agents, and a more immersive user experience across the platform.