FastDispatch

Emergency dispatcher training comes with many problems relating to cost, time, resources, etc. To combat this, we created an application that provides quality training for free.

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Inspiration

We were inspired by the difficult and daring work that emergency dispatch services take on. We saw that the training to become an emergency dispatcher is costly and time-consuming, so we decided to take on the project of creating a training application to make the training process easier and cheaper.

What it does

We programmed a training simulator that quizzes you on your knowledge and expertise of how to handle emergency situations and integrated a chatbot that mimics a real-life distress call.

How we built it

Using Gemini API and Visual Studio Code (python), we generated an accurate list of questions using AI to test trainees on their knowledge. The Gemini API then provides constructive feedback based on your answer. Additionally, after completing your conversation with the ChatBot, you will receive an in-depth review of your performance.

Challenges we ran into

We ran into many challenges considering our limited experience with the Python coding language. However, with the help of the hackathon employees and mentors as well as using the resources available to us, we were able to overcome these challenges and create something we were proud of.

Accomplishments that we're proud of

We are proud that we've built a strong bond as former strangers to now bonded teammates. Furthermore, we learned how to communicate and effectively produce a high-quality program with real-world implications under extreme time constraints.

What we learned

We have learned how to create a functioning program using a language that was previously unfamiliar to us (Python) as well as utilizing Gemini as an API to optimize the features of our application.

What's next for FastDispatch

Optimizing our AI responses to be more accurate would be #1 on our list. After that, we aim to fine-tune the UI/UX aspects with further Python and HTML knowledge that we will hopefully gain in the future.

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