Inspiration

Have you or a family/friend ever been in an automotive accident? Regardless of fault, it's a bad time for everyone involved and we would all like to have that incident behind us and over with.

However, then comes dealing with insurance and getting the damages accessed and that takes a while. whether it be waiting for calls or emails and thus the unfortunate event continues to linger in your lives.

As someone who has experienced the trauma of a car accident firsthand, and had a lengthy insurance process drag on, I wanted to create something to directly address a lived problem.

What it does

autoassist enables users to upload a photo, the photo is stored in CockroachDB and submitted to Google Cloud Vision to be processed. We trained our own machine learning models to detect surface flaws (dents) in images of cars.

How we built it

We used React to scaffold the frontend of this application. The backend API which connects Cockroach DB and Google Cloud Vision AutoML was written with Node/Express. Our ML/AI capabilities leverage a model we trained ourselves.

Challenges we ran into

Deployment was time-consuming due to the restriction of only one Google Cloud App Engine being allowed within a particular Project. We solved this problem by deploying our front end through AWS.

Accomplishments that we're proud of

We completed and deployed our MVP from end to end!

What we learned

We would not recommend learning React within a day, but it's quite heartening how quickly we were able to pick up new frameworks and technologies! We were super impressed by the accessibility of GCP AI platforms, though we learned the importance of a pre-labelled training dataset, as we spent several hours labelling training images by hand.

What's next for autoassist

We plan to continue working on this project, features we hope to add in the coming weeks are user authentication, complex (multi-source) damage detection and auto-connection to an insurance agent through text or social channels.

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