CallMate AI

AI-powered voice assistant for handling customer support calls with real-time responses, feedback collection, and performance insights.

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Inspiration

We noticed that most customer service calls are either slow or frustrating for users, and often expensive for businesses to handle manually. With the rise of generative AI and real-time speech recognition, we saw an opportunity to build a solution that could automate customer call handling while delivering fast, intelligent, and personalized responses.

What it does

CallMate AI is a voice-based virtual assistant that:

  • Accepts real-time audio input from users
  • Transcribes speech to text instantly
  • Uses an LLM to generate natural language responses
  • Displays agent confidence scores
  • Stores chat history and feedback for analysis
  • Provides insights on call quality and customer experience It helps companies reduce load on human agents while keeping customer interactions smart and friendly.

How we built it

  • Frontend: Streamlit using streamlit-webrtc for real-time audio capture and display
  • Backend: Python FastAPI deployed on Render for LLM query processing
  • Speech Recognition: Python speech_recognition and pydub for handling microphone input and audio conversion
  • LLM: OpenAI GPT API integration
  • Storage: SQLite (can scale to DynamoDB) for chat logs and feedback
  • Visualization: Altair for confidence scores and user analytics
  • Deployment: Streamlit Cloud (Frontend) and Render (Backend)

Challenges we ran into

  • Handling real-time audio input and converting it to usable text
  • Managing dependency conflicts (especially between aiortc and av)
  • Syncing frontend audio streaming with backend query processing
  • Deploying two services (frontend + backend) smoothly

Accomplishments that we're proud of

  • Full working voice-based assistant integrated with OpenAI
  • Deployed successfully with real-time voice handling
  • UI includes history, scores, and feedback tools
  • Backend processes and responds to queries instantly

What we learned

  • Real-time voice and AI integration is powerful, but complex
  • Handling audio in Python is tricky without careful FFmpeg setup
  • Streamlit can be stretched beyond its typical text-based limits
  • Audio-to-AI pipelines can be optimized for low-latency responses

What's next for CallMate AI

  • Multilingual support (Hindi, Spanish, etc.)
  • Integrate with Zoho or CRM tools for real use cases
  • Add authentication & user dashboards
  • Use Whisper API for higher speech accuracy
  • AWS Lambda + S3 deployment for scale

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