Capacity optimization for SMEs and the self-employed through predictive matching

Starting situation

The volatility in the project market caused by the corona virus poses a major challenge for medium-sized companies: Many companies have to find new projects for numerous employees at the same time. The long duration of review, application and selection processes leads to liquidity constraints threatening the existence of the company, although numerous projects are still advertised, economic potential remains untapped. Therefore, it is particularly important to bring the supply and demand sides together seamlessly through efficient and transparent matchmaking. Clients are often uncertain about the credibility of the stated expertise of a contractor and are reluctant to award contracts. In addition, matchmaking algorithms often only take into account a few factors, that are necessary for suitable and relevant matching. It is mostly a matter of pure keyword matching, in which qualitative factors (e.g. the duration of experiences) and soft data are not taken into account.

Aim of the project

The OptoPred project aims to fill projects more quickly and to help small businesses and self-employed persons to keep their workload high. For this purpose, an AI-based prediction model is being developed, which specifically predicts the success probabilities of project staffing. The model identifies credible evidence of skills and competencies, as well as other quality indicators in profiles and tenders.

The self-employed and SMEs receive support from this model in order to control their profile design and competence development in an informed manner, and to improve their prospects of success in acquisition. By integrating the prediction model into a matching algorithm, it is expected to achieve a significantly higher matching quality and enable projects to be placed more quickly.


Project Team


This project (project number 20_0086_2A) is funded by the Ministry for Digital Strategy and Development as part of the Distr@l funding program from the state of Hesse.