Currently the insurance claims process is quite labour intensive. A person has to investigate the car to approve or deny a claim, and so we aim to make the alleviate this cumbersome process smooth and easy for the policy holders.

What it does

Quick Quote is a proof-of-concept tool for visually evaluating images of auto accidents and classifying the level of damage and estimated insurance payout.

How we built it

The frontend is built with just static HTML, CSS and Javascript. We used Materialize css to achieve some of our UI mocks created in Figma. Conveniently we have also created our own "state machine" to make our web-app more responsive.

Challenges we ran into

I've never done any machine learning before, let alone trying to create a model for a hackthon project. I definitely took a quite a bit of time to understand some of the concepts in this field. -Jerry

Accomplishments that we're proud of

This is my 9th hackathon and I'm honestly quite proud that I'm still learning something new at every hackathon that I've attended thus far. -Jerry

What we learned

Attempting to do a challenge with very little description of what the challenge actually is asking for is like a toddler a man stranded on an island. -Jerry

What's next for Quick Quote

Things that are on our roadmap to improve Quick Quote:

  • Apply google analytics to track user's movement and collect feedbacks to enhance our UI.
  • Enhance our neural network model to enrich our knowledge base.
  • Train our data with more evalution to give more depth
  • Includes ads (mostly auto companies ads).
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