Dr.Paw

Dr. Paw is your complete pet-care companion — track your pet's growth over time, get feeding and vet-visit reminders straight from Home Screen widgets, find nearby vets, and identify breeds on the go.

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Inspiration

I have been an animal lover since childhood, especially because of the National Geographic programs I used to watch growing up. Those programs made me curious about animals and their lives, and over time I wanted to build something that could contribute to their well-being in some way.

That idea eventually led me to pets. Taking care of a pet involves keeping track of their growth, health, appointments, and daily activities, while choosing the right pet can also be difficult for new or potential pet owners. I wanted to create something that could bring some of these things together in one simple application.

That is how Dr.Paw was born — an app built around the idea of making pet care more organized while also helping people learn more about animals.

What it does

Dr.Paw is an iOS pet-care application designed to help users better understand and manage their pets.

Some of its key features include:

  • 📈 Growth Tracking — Track a pet's weight and height and visualize their growth through graphs.
  • 🏥 Vet Clinic Map — Find nearby veterinary clinics using MapKit.
  • 🐾 Animal Library — Explore information about different animals and breeds.
  • 🔔 Notifications & Reminders — Keep track of important pet-care activities.
  • 📱 Widgets — Access useful pet information directly from the Home Screen.
  • 🤖 AI-powered Animal Recognition — Core ML models trained using Create ML to recognize supported animals.
  • 💳 Subscriptions — RevenueCat-powered subscription and paywall functionality.

The goal is to make pet care feel less complicated and more accessible while encouraging people to learn more about the animals they care for.

How we built it

Dr.Paw was built as an iOS application using Swift and SwiftUI.

The main technologies and frameworks used were:

  • Swift & SwiftUI — Application development and user interface.
  • Supabase — Backend and authentication.
  • MapKit — Nearby veterinary clinic discovery.
  • WidgetKit — Home Screen widgets.
  • Core ML & Create ML — Training and integrating animal recognition models.
  • AVFoundation — Camera and media-related functionality.
  • RevenueCat — Subscription management and paywall implementation.
  • Notifications — Reminders for important pet-care activities.

I built the project with the help of AI as a development assistant, while designing, integrating, testing, debugging, and learning the different technologies throughout the process.

Challenges we ran into

One of the biggest challenges was learning technologies that I had never worked with before.

WidgetKit was completely new to me, and building widgets that interact correctly with the main application required me to understand a different part of the iOS ecosystem.

I also wanted Dr.Paw to have its own AI functionality, which meant learning Core ML and Create ML. Instead of simply using an existing model, I learned how to prepare data and train models myself before integrating them into the application.

Another challenge was working with AVFoundation for camera and media functionality, which introduced another layer of complexity compared to normal SwiftUI development.

Finally, implementing RevenueCat and the subscription/paywall flow was something I had never done in an application before, so understanding how the subscription state should interact with the rest of the application took a lot of experimentation and debugging.

Accomplishments that we're proud of

The biggest accomplishment for me was getting Dr.Paw working end-to-end as a complete application rather than just a collection of individual features.

I am also proud of submitting the project to Shipaton 2026 and taking the application from an idea to something that can actually be demonstrated and used.

But the most meaningful accomplishment is much more personal.

I have wanted to do something for animals since I was a child. Building Dr.Paw gave me a way to turn that childhood interest into something tangible.

For me, that is the most important accomplishment of this project.

What we learned

Dr.Paw pushed me far beyond the SwiftUI concepts I was already familiar with.

During the project, I learned:

  • How to build and work with WidgetKit.
  • How to create and display graphs for tracking pet growth.
  • How to integrate RevenueCat and implement a subscription/paywall system for the first time.
  • How to implement notifications and reminders.
  • How to train machine-learning models using Create ML and integrate them with Core ML.
  • How to work with AVFoundation for camera and media functionality.
  • How to manage and debug a much larger iOS project with many interconnected features.

More importantly, I learned how much there is to explore within the Apple ecosystem and how much I can learn by building a real application instead of only following tutorials.

What's next for Dr.Paw

Dr.Paw is still just the beginning.

One of the main things I want to improve is the Core ML animal recognition system by expanding it to support a much broader range of animals and breeds.

I also want to introduce better health analytics so that users can get more meaningful insights from the information they track over time.

Another feature I would love to build is a Live Activity designed to encourage distraction-free time with your pet — something simple that helps users put their phone aside and actually spend quality time with their companion.

The long-term goal is to continue expanding Dr.Paw into a more complete pet-care ecosystem while keeping the experience simple, useful, and genuinely focused on animals.

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