A German start-up SME is looking for outsourcing agreements with research-driven organisations for its Machine Learning collaboration platform

Working as an international team of 13 staff members and supported by a committee of scientific experts, the young German company has its core competence in software development. The company has successfully raised regional and private investment and has set their goal to boost cross-linking of science and industrial technology. They offer an innovative digital machine learning platform, which is able to link information, knowledge and personal expertise etc. specifically in large work groups, such as European research consortia or internationally active corporates (“MNEs”). It can be also applied e.g. in national or regional research and innovation networks. The platform shall help research driven organisations to ignite and accelerate interdisciplinary projects within a corporation, a university or research institute, or alliances/consortia. With the increase of interdisciplinary projects between research fields, sharing early results of research projects enhances the collaborative nature of science and accelerates research processes and, hence, innovation in the scientific community. However, with the status quo early research findings are hard to find and to share which often results in the repetition of experiments, delay of research results, and ultimately waste of time and money. The problem is the so-called "Black Box Science”, by which research-driven organisations and scientific alliances fail to achieve their goals of interdisciplinary collaboration. A lack of willingness to cooperate and a lack of transparency are the main causes of this inefficient transfer of scientific knowledge. 70% of alliances and other scientific collaboration projects do not achieve their objectives. In order to break these boundaries, the company`s innovative tool simplifies the documentation, thus makes research findings easier available for research stakeholders from academia and industry. The user interface of the novel tool is very simple. Examples of functions within the operating surface: • "Gallery”: to find relevant documents by keyword • "Experts": to find the right peers to launch/unlock a project • "Comments”: ask questions about colleagues' work and share the answers within the community • "1 click": upload the state of work. The novel Machine Learning (NLP) tool takes care of mapping the knowledge and delivers an automatic summary and identification of keywords and topics (beta). • "Project”: with the Canvas tool, teams can quickly share the progress of their work or their collaboration needs in a standardised way. • "Visibility and Groups”: form interdisciplinary groups and decide what information to share with the community The company seeks to present the software tool to corporations, academia, EU research consortia, alliances and communities. A service, i.e. outsourcing agreement, which includes the installation, maintenance, updating and adaption of the tool to specific rights is possible. The company provides tailor-made solutions, and therefore is open for further service models. Subcontracting would be possible, e.g. within public funded research projects.