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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
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:
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Providing real-time, natural voice interactions with minimal latency.
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Ensuring conversational context is maintained across interactions.
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Handling complex queries dynamically while reducing reliance on human agents.
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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.
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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.
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Real-Time Voice Interaction
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  • 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


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Context Management
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  • 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)
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  • 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


4
Query Processing and Response
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  • 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
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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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