Monitoring Company Proves the Power of AI in Call Center Automation

This monitioring company envisioned giving customers the ability to call in and interact with a highly capable voice assistant - one that doesn’t follow scripts but instead understands natural questions, adapts to different situations, and helps users accomplish tasks without needing intervention from human call center agents. They hoped to allow agents to focus their time on more complex, valuable tasks while still providing customers with a fully interactive, intelligent voice experience.

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The AI Voice Assistant should:

• Handle real customer support questions
• Personalize responses using caller context
• Complete transactions (e.g. check account balance, place orders)

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For this proof of concept, the goal was to validate that such a system could work in practice using the company's existing AWS Connect interface for call flow orchestration; Amazon Lex for voice input, intents, and routing; and Amazon Bedrock for data retrieval via LLM.

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They wanted a functional demo showing a caller interacting with an AI assistant that could retrieve knowledge and trigger real actions within a single, smooth voice interaction.

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Specifically, the project aimed to:

• Demonstrate a working end-to-end voice assistant powered by AI
• Show that the assistant could handle real-time questions and transactions
• Prove that AWS-native tools could support such a system securely and scalably
• Lay the groundwork for for broader buy-in and future investment in a long-term solution

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The company's current customer support model is heavily reliant on live agents, resulting in high costs and limited scalability. Despite having a large and active customer base, their existing IVR system provides minimal self-service, leading to a high volume of calls for simple, repeatable tasks. At the same time, the organization wants to innovate – especially with emerging technologies like AI – but must do so within the bounds of compliance and security requirements.

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Much of the call center volume consists of routine, easily automated interactions, yet these calls require live agent support due to limited digital and self-service capabilities. Morse has existing digital assets (such as a customer-facing app) that are underleveraged, and they’d like to evolve their platform with modern cloud-native tools.

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To address challenges and set the foundation for scalable, AI-powered support, we designed a proof-of-concept that brings together AI, voice automation, and backend integration within a secure and compliant architecture.

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Our focus was to demonstrate how routine, high-touch calls could be handled intelligently by a voice agent in order to free up live agents for more complex calls.

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This POC solution showcased the potential of a conversational AI experience powered by Amazon Bedrock and Lex, while proving the system’s ability to retrieve relevant knowledge and perform real-time transactions using backend APIs.

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