Real-Time AI Coach for Live Customer Calls

CallMind turns live calls into smart interactions by transcribing speech, analyzing context with GenAI, and offering real-time suggestions to agents for faster, smarter customer support.

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💡 Inspiration

We were inspired by the limitations faced by customer service agents during live calls—missed upsell moments, delayed responses, and lack of real-time guidance. With the rise of GenAI and real-time speech processing, we saw an opportunity to build a solution that acts like a smart assistant during calls, guiding agents with relevant insights exactly when needed.

🚀 What it does

CallMind listens to live customer calls, converts speech to text, analyzes the conversation context using pre-trained LLMs, and offers real-time suggestions to agents. These include sentiment detection, next-best-action, policy reminders, and tailored replies based on the company’s knowledge base.

🛠️ How we built it

  • Frontend: Streamlit interface to show transcription and suggestions live.
  • Speech-to-text: AWS Transcribe processes audio in near real-time.
  • AI insights: Amazon Bedrock (Claude/Titan) interprets text and context.
  • Backend: Python on AWS Lambda and Step Functions handle orchestration.
  • Knowledge base: FAQs and documents embedded and indexed using LangChain + Amazon S3.
  • Deployment: Hosted entirely on AWS using serverless architecture.

🧱 Challenges we ran into

  • Reducing latency to ensure real-time responses.
  • Avoiding hallucinations from the LLM when generating suggestions.
  • Tuning the prompt templates for better context awareness.
  • Handling multiple accents and call quality variations in live audio.

🏆 Accomplishments that we're proud of

  • Achieved under 3-second response time end-to-end.
  • Created a dynamic GenAI prompt system that adapts to each conversation.
  • Built a plug-and-play system that can be extended to different domains like finance, healthcare, or e-commerce.
  • Clean and intuitive UI that doesn’t distract the agent.

📘 What we learned

  • Practical application of AWS Bedrock and Transcribe in real-time pipelines.
  • How to align GenAI outputs with business goals (compliance, efficiency, upselling).
  • Importance of prompt engineering and vector embeddings in real-time systems.

🔮 What's next for Real-Time AI Coach for Live Customer Calls

  • Integrate voice-based feedback loop to let the agent speak back to the system.
  • Extend multi-language support for global teams.
  • Add call summarization and CRM auto-updates after the call ends.
  • Explore integration with Amazon Connect for full call center support.
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