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.
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This intelligent system provides real-time responses, maintains conversational context, and streamlines the renewal reminder process for business associates.

40
%

Reduction in Response Time

40
%

Query Accuracy Rate

40
%

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.

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:

Solution

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 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.

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.

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.

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

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

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

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

More Customer Stories

We're working on adding more insights. Stay tuned for updates!

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