We were inspired by the inefficiency of our last conversation with a bank. When I called Chase bank, I spent 38 minutes trying to reach an operator to talk about a fraudulent charge on my credit card until the machine hung up on me. From there, I realized there is great room for improvement in the way we talk to customer service from large companies. Say goodbye to the horrible hold music and say hello to TapQ!
What it does
It automates the process of customers going through a company’s recorded messages to reach an operator and serves as a visual app to aid this process.
How we built it
Hours of git pulls and git clones, coupled with caffeine and compilation errors. But, we completed it using the MEAN stack and Twilio’s API, along with the database service Firebase.
Challenges we ran into
Structuring the data in firebase and calling Twilio's api was challenging because the documentation was sometimes confusing. Getting to the swag on time was also quite difficult.
Accomplishments that we're proud of
The name, logo, and functionality. We are definitely going to use this service ourselves to save us time and give us more time to hack and less time to waste on recorded messages.
What we learned
How to better use the MEAN stack, how to make API calls to Twilio’s API and how to connect it all with Firebase.
What's next for Qtap
Using voice recognition software for QTap to automatically run through every company’s recorded messages and make a database of it all. This way our customers have a greater variety to choose from.