The focus here is on networking – by means of AI-based matching, social concept sponsors can use a digital platform to network with facilitators, knowledge owners and project partners, become more professional and initiate projects for the common good. The association Reflecta e.V., the education and research institution Ibugi e.V./CeSEB and the SME zauberware technologies GmbH & Co. KG intend to promote social entrepreneurship in this way and support entrepreneurs.
Why are you a strong team?
Using its current social-graph-based matchmaking platform, Reflecta e.V. has already calculated over 500,000 matches and created over 15,000 relationships. The AI experts at zauberware technologies GmbH & Co. KG have not only an academic background in AI development, but also experience in application and feasibility. Ibugi e.V. , an educational and research institution, brings along experience in the needs of the target audience and research findings on social innovation.
Explain your idea in three sentences.
reflecta.network – needs-based, constantly learning AI matchmaking that solves society’s challenges. Using AI-based matching, social-concept sponsors use a digital platform to network with facilitators, knowledge owners and project partners; make their work more professional and initiate projects. It encourages social entrepreneurship and helps initiate projects for the common good, which ultimately leads to more participation, integration and inclusion.
What makes your idea special?
The platform uses the SDGs for orientation and matches members in categories called Search/Offer, Skills, Topics, SDGs and Megatrends. So far there has not been any platform that brings together the shapers of the future with knowledge owners and supporters in a needs-based way. Offering such matching within a free, secure and digital network of supporters is also innovative. It is complemented with voluntary advice and areas for presenting personal ideas.
What are the next steps?
We are analysing the current matching algorithm as well as the results and findings we have obtained, and examining how the current algorithm can be supported by or replaced with machine learning. Our focus is on resource conservation, efficiency, feasibility and non-discrimination.