Healthcare

Conversational AI for Real-Time Voice Interaction

Kagen AI developed an AI-powered Voice Interaction System for a leading multinational multi-level marketing (MLM) corporation to automate customer support via voice calls. This intelligent system provides real-time responses, maintains conversational context, and streamlines the renewal reminder process for business associates.

40%

Reduction in Response Time

95%

Query Accuracy Rate

100%

Seamless Call Continuity

Business Requirements

Our client needed an efficient voice-based system to automate and enhance customer interactions, particularly for reminding associates about their FSSAI License and Registration Certificate renewals. The primary challenges included:

Providing real-time, natural voice interactions with minimal latency.

Ensuring conversational context is maintained across interactions.

Handling complex queries dynamically while reducing reliance on human agents.

Managing and securing conversational data effectively.

To address these needs, they sought an AI-driven voice solution that would enable seamless call handling, intelligent response generation, and robust session management.

The client collaborated with KaGen AI to enhance and automate its customer interactions. KaGen AI implemented its Conversational AI solution, a voice-first approach powered by real-time communication, contextual intelligence, NLP, and intelligent query handling to optimize and streamline customer interactions seamlessly.

Real-Time Voice Interaction

Implemented Twilio for seamless Speech-to-Text (STT) and Text-to-Speech (TTS) conversion 
Established an automated outbound calling system to initiate reminders and handle inbound queries 
Leveraged WebSocket for real-time audio streaming and communication

Context Management

Integrated Redis to store and retrieve conversation history, ensuring continuity in multi-turn interactions 
Utilized vector databases, such as Weaviate to match user queries with prior conversations, enhancing context awareness

Natural Language Processing (NLP)

Integrated OpenAI’s LLM to process voice-to-text inputs and generate human-like responses 
Fine-tuned AI models for domain-specific conversations, ensuring relevant and accurate responses

Query Processing and Response

Combined vector search with LLM to dynamically generate answers based on prior interactions 
Implemented caching mechanisms for frequently asked questions to reduce response time 
Used response templates to ensure consistency and clarity across scenarios

Story Highlights

Reduced dependency on human agents by handling repetitive queries via AI-powered voice calls.

Redis and Weaviate enabled accurate and meaningful responses based on past interactions.

Twilio’s real-time streaming and robust AI processing ensured seamless, secure, and high-quality customer interactions.

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