All of the devastating stories about customers being burned by policies in terms of service, that was signed but never read.

What it does

The natural machine processing highlights the most relevant parts of the Terms of Service Policy.

How we built it

We created a demo website in Html, CSS, and Javascript, but completed the final website in Flask. The machine learning model was also created in Python, and finally, we web scraped the sites in C#.

Challenges we ran into

We actually scrapped our idea to our first product so we lost a couple of hours. We also attended workshops which limited our overall time.

Accomplishments that we're proud of

Completing a project we believe solves an actual real-world common problem brings us satisfaction. Also proving the hypothesis complaints associated with the companies can be found and rated against the terms of service was validating.

What we learned

Through this app, we learned about web scraping and regular expressions to extract the data.

What's next for Term Of Service Highlighter (T.O.S.H for short)

We want to present this to companies to generate more user-friendly and readable Terms of Service policies along the side of their more legal policy.

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