Imagine a giant matchmaking app for the job market that doesn't just look at resumes, but understands exactly why people can't find jobs and where the gaps are. It uses AI to turn messy government reports into a clear map of what skills are actually needed. This helps countries stop guessing and start training people for the jobs that are actually open.
Companies and governments face a paradox where high unemployment exists alongside unfilled vacancies. This is caused by a lack of accurate data and poor coordination between training programs and actual market needs.
An AI-supported knowledge infrastructure and a validated analytical tool for designing labor market interventions. It includes a repository of machine-readable policy evidence and skills ecosystem maps.
If you are an HR software provider dealing with inaccurate skill-gap data — this project developed AI-powered repository tools that turn complex policy evidence into machine-readable datasets. This allows for more precise automated matching between candidate skills and employer needs.
If you are a training center dealing with outdated course catalogs — this project developed a mapping of national skills ecosystems. This helps you align your curriculum with real-time labor market needs to increase student employment rates.
If you are an agency dealing with high unemployment despite many vacancies — this project developed a decision-support infrastructure. This provides a validated way to design interventions that avoid wasting resources on ineffective training.
Based on available project data, no pricing or cost structure is mentioned as it is an EU-funded research project.
The project is currently in the development and experimentation phase, with a focus on building the knowledge infrastructure and testing through experiments.
Based on available project data, there is no specific information regarding IP or licensing terms for the developed software.
The project runs from 2025-01-01 to 2027-12-31.
The project focuses on transforming heterogeneous documentation into machine-readable datasets to improve integration with data-informed analysis tools.
The consortium is composed of 12 partners across 6 countries, showing a strong European reach. It has a balanced mix of 3 universities, 2 research organizations, and 4 'other' entities, with a 25% industry ratio (3 industrial partners). The presence of 4 SMEs suggests a focus on translating research into agile, scalable digital tools.
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