"Working closely with Found Factory allowed us to develop a platform that not only matched users based on personality but also leveraged professional skills to foster real, actionable connections. This agile, collaborative approach brought their vision to life, creating a tool that is now ready to scale."
Etka Serhan Uslu
COO
"Working closely with Found Factory allowed us to develop a platform that not only matched users based on personality but also leveraged professional skills to foster real, actionable connections. This agile, collaborative approach brought their vision to life, creating a tool that is now ready to scale."
Etka Serhan Uslu
COO
The Found Factory project aimed to solve the challenge of finding the right co-founder or team member for students and alumni, offering a streamlined, scalable solution. Our collaboration with Found Factory led to the development of a platform that integrates personality-matching algorithms with AI-powered professional skills matching. The result was an MVP that has been tested and refined with client feedback, delivering a practical, user-friendly platform for entrepreneurs to find their ideal teams.
The Found Factory project aimed to solve the challenge of finding the right co-founder or team member for students and alumni, offering a streamlined, scalable solution. Our collaboration with Found Factory led to the development of a platform that integrates personality-matching algorithms with AI-powered professional skills matching. The result was an MVP that has been tested and refined with client feedback, delivering a practical, user-friendly platform for entrepreneurs to find their ideal teams.
Understanding the Client’s Pain Points
The client’s main challenge was a manual and inefficient process for matching users with potential co-founders or team members. The existing system lacked scalability and failed to deliver relevant matches consistently, leading to poor user engagement. The client wanted to automate this process and make it easier for users to find suitable partners for their projects.
Through detailed discussions with the client, we understood the need to create a more streamlined and user-friendly experience. Our team worked closely with Found Factory to prioritize features that would improve the user experience and ensure that the platform was scalable and impactful.
Understanding the Client’s Pain Points
The client’s main challenge was a manual and inefficient process for matching users with potential co-founders or team members. The existing system lacked scalability and failed to deliver relevant matches consistently, leading to poor user engagement. The client wanted to automate this process and make it easier for users to find suitable partners for their projects.
Through detailed discussions with the client, we understood the need to create a more streamlined and user-friendly experience. Our team worked closely with Found Factory to prioritize features that would improve the user experience and ensure that the platform was scalable and impactful.
Technical Challenges and System Integration
One of the biggest technical challenges was integrating the R script provided by the client for personality-matching into the platform's Node.js-based backend seamlessly. Additionally, building a custom AI module to match users based on professional skills and integrating academic email verification, social login, and user role management posed significant hurdles.
We solved the R script integration by treating it as an external microservice, ensuring smooth communication with the backend. For the AI module, we tested several model approaches and chose the one that balanced performance and complexity best. We prioritized core features like the matching algorithm and user verification, postponing some secondary features to maintain timeline and budget.
Technical Challenges and System Integration
One of the biggest technical challenges was integrating the R script provided by the client for personality-matching into the platform's Node.js-based backend seamlessly. Additionally, building a custom AI module to match users based on professional skills and integrating academic email verification, social login, and user role management posed significant hurdles.
We solved the R script integration by treating it as an external microservice, ensuring smooth communication with the backend. For the AI module, we tested several model approaches and chose the one that balanced performance and complexity best. We prioritized core features like the matching algorithm and user verification, postponing some secondary features to maintain timeline and budget.
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