According to a study done across 1.8 million past emergency service calls, the average response time was 14 minutes. In situations where the difference between life and death can be a mere second, we recognize it would be in the best interest of society as a whole if we could allocate the most time possible, to the people who need it most, when they need it most.

What it does

Utilizes machine learning and a serverless infrastructure to streamline the emergency services calling process so that emergency services can get where they need to be and provide assistance as fast as possible.

How we built it

HTML/CSS/Ember.js, Serverless, Python, Machine Learning, NLP, AWS Lambda, Twilio API

Challenges we ran into

Getting the Twilio API to function as intended (inbound call receiving, call recording, etc)

Accomplishments that we're proud of

All the progress we made in such a short timeframe.

What we learned

A lot.

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