Iris: High-Performance Social Platform for Scientists

The problem

The existing platform struggled with performance and scalability issues that directly impacted usability. Pages were slow to load, interactions felt delayed, and the overall experience lacked responsiveness. A major contributing factor was the inefficiency of backend APIs, which were not optimized for frontend consumption. Additionally, the platform had weak SEO foundations, limiting its visibility, and the outdated user interface made it harder for users to navigate and engage effectively.

The approach

Rather than applying incremental fixes, the decision was made to rebuild the frontend from the ground up. The focus was on creating a clean and scalable architecture that could handle complex data flows more efficiently. Special attention was given to how data was fetched, processed, and rendered, ensuring that the frontend could compensate for backend limitations. At the same time, the structure of the application was rethought to improve SEO and overall accessibility.

The solution

A new frontend was developed using Next.js, enabling improved rendering strategies and better performance. The UI was redesigned with a modern approach to enhance clarity and usability across the platform. API integration was carefully optimized to handle inefficient endpoints more effectively, reducing delays and improving data consistency. The application structure was also refined to support better SEO practices, ensuring that content could be indexed and discovered more easily.