Inspiration
This project is an intelligent call center agent designed to enhance customer service efficiency by providing real-time AI-driven recommendations during customer interactions. The system listens to customer queries, processes the conversation context, and suggests relevant responses, solutions, or next steps to the human agent or directly interacts with the customer via an automated voice/text interface.
What it does
This features include:
Real-time transcription and sentiment analysis of customer speech.
AI-powered recommendation engine suggesting relevant FAQs, troubleshooting steps, or escalation paths.
Seamless integration with backend systems to fetch customer data and update tickets.
Scalable, serverless architecture using AWS Lambda and API Gateway.
Utilization of AWS Bedrock to leverage foundation models for natural language understanding and generation.
How we built it
Data Collection & Preparation We gathered historical call transcripts, customer support tickets, and knowledge base articles to train and fine-tune AI models. This data was anonymized and cleaned for privacy and accuracy.
AI Recommendation Engine Using AWS Bedrock, we accessed foundation models (such as GPT or other LLMs) to build a semantic understanding layer. The models were fine-tuned to generate context-aware recommendations based on the ongoing conversation.
Serverless Backend with AWS Lambda AWS Lambda functions were developed to handle API requests, process input data, call AI models via Bedrock, and return recommendations. This ensured a scalable, cost-effective backend without managing servers.
Challenges we ran into
Real-time Processing Latency: Ensuring AI recommendations were generated quickly enough to be useful during live calls required optimizing Lambda cold starts and API Gateway configurations.
Contextual Understanding: Maintaining conversation context over multiple turns was challenging, requiring careful prompt engineering and state management.
Data Privacy and Compliance: Handling sensitive customer data demanded strict encryption, anonymization, and compliance with regulations like GDPR.
Accomplishments that we're proud of
Successfully deployed a fully serverless, scalable AI-powered call center agent with near real-time recommendations.
Achieved significant reduction in average call handling time and improved customer satisfaction scores during pilot testing.
What we learned
Serverless architectures using AWS Lambda and API Gateway are highly effective for building scalable AI-powered applications.
Leveraging AWS Bedrock foundation models accelerates development but requires thoughtful prompt design and fine-tuning.
What's next for Ursus Agent
Multi-modal Interaction: Incorporate voice synthesis and emotion detection to make interactions more natural and empathetic.
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