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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

In this Story
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.
Solution
1. 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.
2. Context Management
- Integrated Redis stores and retrieves conversation history, ensuring continuity in multi-turn interactions.
- Utilized vector databases (Weaviate) to match user queries with prior conversations for enhanced context awareness.
3. 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 to associates.
4. Query Processing and Response Generation
- 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 different 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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