RescueLink

Inspiration The inspiration for RescueLink came from recognizing the critical need for efficient emergency response. The team saw the challenges faced by Toronto's 911 call center, particularly the delays in handling a large volume of calls. The goal was to create a solution that not only streamlined communication but also utilized advanced technologies for real-time assistance.

What it does RescueLink integrates various technologies to enhance emergency response. It begins with Voice-to-Text transcription using AssemblyAI, ensuring that incoming calls are quickly processed. A real-time map, powered by the Google Maps API, helps pinpoint locations and identify the nearest first responders. Direct communication with those in need is facilitated through the Twilio API and Infobib-Two-Way SMS. The system also incorporates AI models from OpenAI for summarizing call topics.

How we built it The team divided responsibilities based on individual strengths and interests. Alistar focused on Voice-to-Text transcription and the Google Maps API. Lisa led the artistic design and front-end development using Figma, HTML, CSS, JS, and Tailwind. Sarim contributed to back-end development with a focus on Twilio API and Two-Way SMS, while also delving into front-end work with Vue, JS, and Rust. Hamza took the lead in Twilio API for customer communication and OpenAI Fine Tuning for AI models.

Challenges we ran into Several challenges were encountered during the development process. Alistar faced difficulties in real-time transcription setup, and Hamza navigated the complexities of OpenAI Fine Tuning. Lisa grappled with the learning curve of Tailwind, while Sarim juggled responsibilities between back-end and front-end development, exploring multiple technologies.

Accomplishments that we're proud of The team successfully overcame challenges and created a comprehensive solution. Alistar achieved real-time transcription, Lisa mastered Tailwind for front-end development, Sarim effectively utilized Twilio API and Two-Way SMS, and Hamza led customer communication and AI model fine-tuning.

What we learned The project provided valuable learning experiences for each team member. Alistar gained insights into Voice-to-Text transcription challenges, Lisa improved her coding practices with Tailwind, Sarim explored multiple technologies, and Hamza honed skills in customer communication and AI model training.

What's next for RescueLink The next steps for RescueLink involve further refining the system based on user feedback and expanding its capabilities. This could include additional features for AI-driven assistance, integration with more emergency services, and ongoing improvements to ensure a seamless and efficient emergency response platform.