Opolo - HorizonFit

Inspiration

While the idea of studying abroad is exciting, we found that the process often brings uncertainty and anxiety. Important information is scattered across different websites, videos, and personal experiences, making it difficult for us to compare destinations and understand what life will actually be like after the move. Although globally accepted exchange networks like ISEP provide access to opportunities around the world, we often struggle to find the personalized and practical insights needed to make confident decisions. As a result, what should be an exciting opportunity often ends up feeling overwhelming, causing many students including ourselves to give up on pursuing a study abroad experience. We created Opolo to simplify this process. We envisioned it as a single platform where students can compare cities based on factors that matter most to them such as cost of living, safety, and lifestyle preferences. By combining reliable data, AI-powered insights, authentic student experiences, and exchange program information, Opolo helps students identify destinations that align with their goals and understand what to expect before making one of the biggest decisions of their university journey.

What it does

Opolo-HorizonFit is an AI-powered decision-support platform designed to help students navigate one of the most complex decisions of their academic journey: choosing where to study abroad. Every year, students spend weeks researching universities, comparing destinations, and trying to understand life in a new country. However, this information is scattered across university websites, housing platforms, forums, and social media, making the process time-consuming and fragmented. Opolo-HorizonFit brings this information together into a single platform. It begins by building a detailed student profile through an interactive assessment covering academic interests, budget, lifestyle preferences, and personal constraints. This allows the system to understand not only where a student wants to go, but the environment in which they are most likely to thrive. The platform then evaluates destinations across key factors such as cost of living, housing, safety, transportation, healthcare, culture, social life, and career opportunities. Instead of treating these as isolated metrics, it analyses how they align with each student’s priorities and trade-offs. A core feature is the destination matching engine, which does not simply rank cities but highlights trade-offs between options. For example, a city may offer strong career opportunities but higher living costs, while another may be more affordable but limited in internships. The platform helps students understand these trade-offs clearly before deciding. To improve transparency, HorizonFit explains every recommendation by showing why a destination was suggested and which factors influenced it. This ensures students understand the reasoning instead of receiving a black-box result. The Budget Matchmaker compares living costs between a student’s home city and a chosen destination, including rent, food, transport, and daily expenses, helping them understand financial feasibility in a clear and practical way. The Visa Predictor helps students assess their readiness for visa applications by identifying requirements they meet and gaps they may need to address. The Relocation Guide provides a structured checklist for moving abroad, covering tasks such as accommodation, documentation, banking, healthcare, and other essential steps before and after arrival. The Community Hub allows students to connect with others heading to the same destination or already studying there, helping them gain real insights and build early connections.

How we built it

Opolo was built iteratively using Claude as a development assistant, which we used to generate, refine, and debug the application across multiple stages. The frontend is a React single-page application built with Vite and Tailwind CSS, structured around a multi-step assessment that builds a detailed student profile. The matching system uses a Euclidean-distance model where each city is represented by weighted dimension scores. These are compared against user preferences to generate ranked recommendations. While simple, this approach is transparent and effective for demonstrating the concept. We integrated Gemini via Google AI Studio to translate numerical outputs into human-readable insights, explaining affordability, trade-offs, and city suitability in a contextual way. It also supports features like visa guidance, budget breakdowns, and relocation checklists. We focused heavily on transparency by ensuring AI-generated insights remain explainable and grounded in structured data rather than acting as a black box.

Challenges we ran into

Finding objective data was difficult due to biased and inconsistent user reviews across platforms. We also had to scale down our initial technical vision due to time constraints, moving from a complex multi-model AI system to a more practical Gemini-based integration and a simpler recommendation model. Using AI-assisted coding introduced significant issues, including broken imports, redundant code, and missing features after updates, which required repeated debugging. Additionally, limited experience with some of the tools made debugging slower and more challenging for the team.

Accomplishments that we're proud of

One of our biggest accomplishments is successfully bringing Opolo-HorizonFit to life despite continuous technical and design challenges. At multiple points, we had to rethink features, fix unexpected bugs, and adapt our approach as new issues emerged. Instead of getting stuck, we iterated constantly, redistributed tasks based on strengths, and supported each other through debugging and rebuilding parts of the system. What we are most proud of is not just the final product, but the fact that we were able to stay consistent, adapt quickly, and turn a complex idea into a working platform.

What we learned

We learned a lot about building iteratively, working with AI-assisted development tools, and adapting when technical plans don’t go as expected. We also learned the importance of breaking large systems into smaller, manageable parts and testing continuously as we build.

What's next for Opolo - HorizonFit

We see HorizonFit evolving into a broader ecosystem that supports students throughout their entire study abroad journey. We plan to expand our destination database to include major student hubs across the UK, Germany, Canada, Australia, and beyond, allowing more students to find matches based on their preferences. We also aim to strengthen the community aspect by enabling students to directly connect with peers, upperclassmen, and alumni before they move abroad, helping them build support networks early.