🐾 Pawfect Match: Our Story

💡 Inspiration

Every year, thousands of incredible shelter pets are overlooked—not because they're unlovable, but because they don't get the chance to be truly seen. We were inspired by this disconnect. So many people want to adopt, but struggle to find a pet that genuinely fits their life. We thought: what if we could change that? What if finding your furry companion felt more like finding a soulmate?

📚 What We Learned

How emotional connection often drives adoption more than just filters or checkboxes.
The importance of user experience in building trust between adopters and shelters.
How to integrate behavioral and lifestyle of various pet data to create meaningful matches between humans and animals.
How to use Generative AI to match pet owners based on user preferences and emotional cues.
How to rapidly create a full-stack application from idea to execution.

We also dove into ethical considerations—how to avoid bias, how to handle sensitive data responsibly, and how to represent animals as individuals, not just listings.

🛠️ How We Built It

The idea started with a simple goal: make adoption more heartfelt and human. We brainstormed ways to use technology to create emotional matches and began sketching ideas for the user experience. The design phase went through several iterations—we changed our minds a few times before arriving at a direction that felt just right: personal, warm, and intuitive. We built the front end using React and Next.js to deliver a fast, responsive, and modern user experience. For the backend, we used Express.js, giving us flexibility to handle matching logic and third-party APIs. To enhance our matching process, we used Gemini AI to analyze pictures of pets—detecting features that may emotionally resonate with the user. This helped surface pets that users would be more likely to connect with at first glance. We then integrated the Petfinder API to search for nearby shelters and display real-time data on adoptable pets—helping users find their Pawfect Match.

🚧 Challenges We Faced

Balancing depth and simplicity: We wanted deep matches without overwhelming users with long forms or questions.
Integrating AI and APIs: Ensuring AI image analysis and Petfinder data worked smoothly together required careful tuning and testing.
Emotional complexity: Designing a product that navigates both joy and grief (many adopters come from loss) was emotionally challenging but crucial.

❓Plans for the Future

Advanced Feedback Loop: One feature we planned on implementing, but were unable to accomplish within the time frame, was an advanced feedback loop. Once the user has matched with the pet, they are prompted with a question "Not to your liking?", and when pressed, gives an optional feedback form. This feedback is then fed back into Gemini, which would prompt it to tailor the remaining matches even further, to end up with the perfect match for the user.