Enterprise Europe Network

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

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Partner keyword: 
Information Technology/Informatics
Knowledge Management, Process Management
Other communications (not elsewhere classified)
Other information service activities n.e.c.
Other professional, scientific and technical activities n.e.c.


A German start-up SME is offering a digital Machine Learning collaboration platform to help research-driven organisations, initiatives and industry in life sciences, chemistry, or advanced materials to increase the success rate of their interdisciplinary projects. The system is able to map in real time the expertise and peers associated with the knowledge generated. The team is looking for outsourcing agreements, or subcontracting e.g. with corporations or large European research consortia.



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.

Advantages & innovations

Cooperation plus value: 
The solution is the result of more than 100 interviews with researchers in life sciences, chemistry, and physics.The user interface is very simple to favour acceptance by researchers. Based on these interviews, the company has found out that most of the currently available tools on the market do not appeal to interdisciplinary research initiatives. These tools have been designed for a generic usage across industries and fields only as internal repositories. In contrast, the novel solution allows a researcher to find also external expertise and peers he/she needs to collaborate by a single click. The knowledge generated in this way is mapped in real-time (knowledge graph), allowing management level to focus on knowledge transfer, and acceleration of research output for everyone’s benefit.

Technical Specification or Expertise Sought

Cooperation sought: 
The ideal partner should be a research driven organisation, e.g. RTD institute, university, large (multi-national) company, large national or regional network The potential partner should be active in the fields of life sciences (biotechnology, pharmacy, medical technologies), chemistry, or advanced materials technologies.

Stage of development

Cooperation stage dev stage: 
Already on the market

Partner sought

Cooperation area: 
The company is offering outsourcing agreements to RTD corporations, universities, research institutes or scientific alliances/consortia, in the field of life sciences (biotechnology, medical technologies, pharmacy), chemistry, or advanced materials. The partners should be interested in digitalisation of their knowledge transfer activities with a simple and secure software. Ideally, they should be looking for a tool, which is suitable for large groups, and which require comprehensive and complex information transfer processes that cannot be sufficiently managed otherwise. Adaption, updating and maintanance of the tool are included in the service as a technical support. Moreover the company is open for discussing any additional and tailor-made service models. Subcontraction would be another option, in particular for research consortia, which apply for public European or national funding. The responsible project partners obtain the software, its adaption and technical support in order to manage better their knowledge transfer activities.

Type and size

Cooperation task: 
University,R&D Institution,>500 MNE,251-500,SME 51-250,>500