Instanimals

Thousands of animals bounce between shelters because there aren't enough foster homes. The problem? Every shelter has its own application. Instanimals fixes that!

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Growing up, most of us has always been around animals in some way. Their always a source of unconditional love that asks for nothing in return. Because of this we have seen what helped animals and what destroys them. It started as a simple question: How can we make the biggest impact with animals? Then we came to the foster care system. Every shelter had its own application. Its own process. It's own paperwork. Potential fosters would start the journey, get overwhelmed, and give up. Animals stayed in shelters longer. The system was broken, not because people didn't care, but because the process made it too hard to care. Instanimals was born from that frustration. From the belief that the love animals give us should be met with a system that makes it easy to love them back. We built the website mainly in Figma. But when it came time to export, nothing worked. So we pivoted, rebuilding and salvaging what we could. The hardest part, ended up being mashing everything together what each of us built into one project that actually functioned. Different pieces, different approaches, different expectations, getting them to work together took longer than building them in the first place. We learned that tools don't build projects. People do. And the real challenge isn't the design, it's making everything fit.

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As the Backend Lead for Instanimals, I engineered a multi-dimensional recommendation engine using Python, Pandas, and Scikit-learn, implementing weighted scoring and Cosine Similarity to match users with pet shelters. I led the architectural migration from local storage to a cloud-based Firebase Firestore infrastructure, developing data seeding scripts to populate core collections and managing secure service account integrations. To facilitate frontend communication, I built a Flask REST API with CORS support, creating dynamic endpoints that process real-time cloud data through the recommender logic. Throughout the project, I maintained a professional Git workflow on the feature/recommender-system branch, ensuring project traceability and high code quality through structured commit practices.

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