Inspiration During emergencies, every second counts. We were inspired by the potential of AI to assist in disaster management by streamlining communication, detecting critical emotions, and routing calls intelligently. With traditional call centers overwhelmed during crises, we saw the opportunity to leverage AI agents, sentiment analysis, and automated workflows to provide quick and efficient assistance to those in need.
This project draws inspiration from real-world scenarios where delays in communication cost lives, and AI-powered services could make a difference by offloading repetitive tasks and augmenting human agents.
What it does AI Assisted Emergency Call Service is a multi-channel call center platform that:
Receives and processes calls of victims. Uses sentiment analysis with Hume AI to detect stress and urgency levels. Forwards calls dynamically to agents or supervisors based on the urgency. Employs Crew AI to coordinate between multiple AI agents for task distribution. Provides a real-time dashboard with GIS mapping to monitor ongoing incidents.
How we built it Backend Setup:
We used Python to create the backend, with APIs for handling call flows. Configured Hume AI to perform sentiment analysis on audio inputs. AI and Workflow Management:
Used Crew AI to coordinate tasks across multiple AI agents. Real-Time Dashboard:
Developed a web dashboard for live monitoring of incidents. Integrated GIS mapping tools to display affected regions visually. Audio Processing:
Applied GAN models to enhance audio quality for better sentiment detection. Data Management:
Designed automated ticket generation for incident tracking. Ensured data integrity using blockchain for secure records. Challenges we ran into Call Routing and Forwarding:
Setting up dynamic call forwarding using Twilio and ensuring smooth transitions between AI and human agents. Model Integration:
Encountered API connection issues with models like MistralAI, requiring troubleshooting and adjustments. Sentiment Accuracy:
Ensuring accurate emotion detection during noisy or unclear calls required extensive audio processing with GAN models. Performance Optimization:
Optimizing the system to handle high volumes of calls during peak disaster scenarios without delays. Accomplishments that we're proud of End-to-End AI-Powered Call Routing System:
Successfully built a system that can automatically route calls based on sentiment and urgency. Real-Time Monitoring Dashboard:
Developed a GIS-integrated dashboard to monitor live incidents and track agent status. Seamless Multi-Agent Coordination:
Leveraged Crew AI to effectively distribute tasks between multiple agents, ensuring efficient collaboration. Proactive Alerts and Notifications:
Implemented predictive analytics using IoT data to send alerts before a disaster escalates. Audio Enhancement for Better Detection:
Applied GAN models to improve audio quality, enhancing sentiment detection accuracy. What we learned AI’s Role in Crisis Management:
We learned how AI tools and workflows can augment human agents and improve response times during disasters. Importance of Workflow Orchestration:
Managing complex workflows with multiple agents and APIs required us to focus on system synchronization.
Optimizing Call Routing:
Understanding the intricacies of dynamic call forwarding and routing in real-world applications was a key takeaway. Building Resilient Systems:
Developing a system that can scale during emergencies helped us grasp the importance of performance optimization.
What's next for AI Assisted Emergency Call Service Expand Language Support:
Add real-time language translation to support multi-lingual conversations during international crises. Integrate with More IoT Devices:
Enhance proactive alerts by integrating additional IoT sensors and satellite data feeds. Improve Sentiment Analysis Models:
Fine-tune sentiment models to better detect subtle emotions and handle noisy environments more effectively. Develop Psychological Support Chatbots:
Introduce post-disaster support chatbots to provide emotional assistance to victims. Open-Source the Project:
Make the project open-source to invite contributions and collaborate with the community for continuous improvement. Deploy Blockchain-Based Data Management:
Implement blockchain to ensure tamper-proof records and secure data sharing across agencies.
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