Project Vision: Why 49 Billion Euros Are Not Enough
Current Situation
Spending on integration and social assistance in Germany has risen continuously for years: in 2024 it reached nearly 49 billion euros, of which 28.7 billion euros alone went to disability integration support for over one million people. Yet matching people with suitable services is often slow: social workers research mostly manually, and existing platforms are maintenance-heavy and barely adopted. Data-driven approaches to identify needs early and close service gaps are missing. People seeking help usually receive information only via service providers — so available offerings often go unused despite high demand.
Project Goals
KIDaS is developing an AI-powered data platform that matches, analyses, and systematically fosters innovation in social services. The platform connects people seeking support, service funders, and service providers, and balances supply and demand more effectively. AI agents automate the collection and maintenance of service data; a Service Sonar detects trends and unmet needs from public data sources and generates proposals for new social services. A matching service brings people seeking help together with suitable services. On the research side, the project produces design principles for agentic AI on platforms, a methodology for data-driven service innovation, and a transferable process model for digital service platforms.
Methodology
Four consortium partners work together iteratively and with a strong practical focus: FAU Erlangen-Nuremberg and the University of Kassel as scientific partners, and ANLEI-Service GmbH and Dataciders PRODATO as implementation partners — complemented by the associated practice partners LWV Hessen, Bathildisheim e.V., LVR, and LWL. The approach combines Action Design Research with Value-Sensitive Design, so that technical innovation and ethical principles are given equal weight. During e-Van tours, the team visits social institutions on-site to gather requirements and build a competence network.
Results and Application Potential
The project delivers an extensible data platform with AI components that significantly reduces maintenance effort, matches people seeking help with precisely suited services, and supports decision-makers with data-driven proposals. Transferable scientific methods together with a targeted opening strategy position the platform long-term as a public-interest hub and incubator for social-tech innovations.