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References, industry experience, number of employees, types of contract, prices, etc. Search for the most suitable IT service provider for your project in the following areas of expertise:
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We have more than 50 data points that we make available to you. Both for IT service providers and for individuals (not only freelancers but also employees) to help you with your project.
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More than 50 skill areas provide a comprehensive market overview, if desired also enriched with own data - for a detailed benchmarking of the IT spend.
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Lyncronize develops a digital assistant based on Artificial Intelligence (AI) and Machine Learning to assemble IT teams based on skill predictions and past experience.
Lyncronize GmbH receives funding from the ProFIT programme of Investitionsbank Berlin. The plan is to develop a new digital tool for putting together competence teams for tasks in the field of digitalisation. The market has so far been dominated by consultants who generate "matches" via relational keyword matches and evaluate them individually. Given the breadth of skills required and the rapidly changing world of work, this is too imprecise and so cost-intensive that the acquisition of personnel often represents an economic barrier to the implementation of a project.
A novel, digital approach is to lead to better matching results with the help of artificial intelligence. For this purpose, three different data models are to be developed and taken into account in the matching process:
A user model that takes individual characteristics and preferences into account.
A skill model that recognises and contextually links terms via an ontological database.
A market model that captures current trends and correlations, e.g. of skills to hourly rates.
This approach, which is more comprehensive than pure keyword matching, is intended to improve "matching" when finding suitable projects and candidates as well as when putting together entire teams. However, the integration of personal characteristics and preferences also requires special consideration in terms of data protection. This approach is to be accompanied by the prediction of trends in competence profiles, which can be derived from job advertisements, for example.