After hearing that 5500 animals are put down every year, our team deconstructed the biggest problems that people face when considering to adopt a dog: how to find a dog to adopt and how to find a dog that will be a perfect fit for their lifestyle. First, people struggle to find a dog in their area that meets their desires simply and efficiently. Most shelters lack solid technology, so users have to go out of their way to find what dogs are available in their area and background information about those dogs who are. Further, many times people have a grand difficulty choosing what type of dog if perfect in their life, resulting in many dogs being returned to shelters after an initial adoption.
What it does
We set out to create an app that helps people find dogs based on their own lifestyles, streamlines the adoption process, and ends with dogs in happy households and off the streets forever. To do this, Pupper asks a variety of simple questions about a user's lifestyle, everything from desired cleanliness level to preferred size of companion. After collecting information, Pupper employs machine learning to match a user with various breeds of dogs, and once a user chooses their favorite among the recommended breeds, matching dogs available for adoption in the user's area are displayed. Pupper keeps everything smooth and efficient, allowing users to begin the adoption process right in the app by generating and sending a general application based on their previous answers and requesting an interview with the shelter. Pupper works to help everyone find the perfect dog and to find every dog the perfect home.
How I built it
Pupper is an iOS app built in Swift, utilizing the Petfinder and RescueGroup APIs. The app also employs machine learning.
Challenges I ran into
There were two main difficulties that our team struggled with. First, the APIs had very little documentation in general and especially little for iOS development. We had to figure out how to mend the gap between these APIs and Apple's protocols. Once we were able to successfully utilize the APIs, the photos provided of animals are incredibly low quality, small, and, many times, awkward. We had quite the UI challenge to try to appeal to the everyday dog lover.
Accomplishments that I'm proud of
We're just happy to help save the dogs :)
What I learned
We learned a surprising amount about different breeds of dogs, fine-tuned our iOS dev skills, and added some machine learning to our repertoire.
What's next for Pupper
Onto the App Store!