Summary
- Profile Type
- Technology offer
- POD Reference
- TODE20250429013
- Term of Validity
- 12 May 2025 - 12 May 2026
- Company's Country
- Germany
- Type of partnership
- Research and development cooperation agreement
- Targeted Countries
- All countries
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General information
- Short Summary
- A German research group offers expertise in generative AI, RAG (Retrieval-Augmented Generation) systems, multi-agent LLMs (Large Language Models), and synthetic data generation for healthcare, manufacturing, and robotics. They seek partnerships through Horizon Europe consortia, contributing GenAI technologies, explainable multimodal AI models, and data-driven solutions. They are looking to join projects as a technology provider for GenAI (generative AI)-related tasks.
- Full Description
-
The research group belongs to a German institute for applied science focusing on research and development activities on solving organizational and technological challenges in production, developing and applying components, processes, devices, and complete systems. Within the institute, the Generative AI Group of the Department of Artificial Intelligence and Machine Vision specializes in RAG systems, multi-agentic LLMs, GenAI-based data generation, and benchmarking methods tailored to manufacturing, robotics, and medical applications. Their project idea addresses the growing need for explainable, high-performing, and application-specific generative AI solutions in healthcare and manufacturing. They aim to tackle challenges related to the integration of GenAI technologies with multimodal data and real-world decision-making processes, particularly in sensitive sectors like healthcare and biomedicine.
The research group is specifically interested in participating in the following Horizon Europe calls:
- HORIZON-HLTH-2025-01-CARE-01: End user-driven application of Generative Artificial Intelligence models in healthcare (GenAI4EU)
- HORIZON-HLTH-2025-01-TOOL-03: Leveraging multimodal data to advance Generative Artificial Intelligence applicability in biomedical research (GenAI4EU)
- HORIZON-HLTH-2025-03-DISEASE-04-two-stage: Leveraging artificial intelligence for pandemic preparedness and response
- HORIZON-HLTH-2025-01-IND-01: Optimising the manufacturing of Advanced Therapy Medicinal Products (ATMPs)
- HORIZON-HLTH-2025-01-DISEASE-02: Advancing innovative interventions for mental, behavioural, and neurodevelopmental disorders
Their main technological features include a fully modular, state-of-the-art RAG system, piloted multi-agentic architectures for complex task solving, and GenAI-driven synthetic data generation that has already enhanced medical classification tasks such as the diagnosis of parathyroid adenomas. Their solutions stand out for their robustness, modularity, and domain-specific optimization, particularly for critical applications where reliability, transparency, and adaptability are key.
Potential applications of their technology lie in healthcare (clinical decision support, medical imaging), biomedicine (data-driven research acceleration), and manufacturing (knowledge-based production optimization).
The institute is looking for consortium partners with whom they can collaborate by taking over GenAI-related tasks in projects, including but not limited to developing advanced RAG systems, building multi-agentic LLM architectures, generating synthetic datasets, and designing explainable multimodal AI models for healthcare and industrial contexts. - Advantages and Innovations
- The institute’s modular RAG system is highly versatile, rigorously tested, and optimized for various data types, providing faster and more reliable knowledge retrieval compared to standard RAG baselines. Their multi-agentic LLM architectures improve complex decision-making by dynamically coordinating specialized AI agents. Compared to prevailing single-agent or monolithic GenAI approaches, their systems offer superior scalability, adaptability, and explainability, which are crucial for sensitive domains like healthcare and manufacturing. Their synthetic imaging data generation has enhanced medical classification tasks, such as parathyroid adenoma detection, demonstrating the potential to reduce reliance on large annotated datasets. Their methods support more cost-effective AI deployment and offer clear performance and transparency advantages in real-world industrial and clinical applications.
- Stage of Development
- Available for demonstration
- Sustainable Development Goals
- Not relevant
- IPR status
- Secret know-how
Partner Sought
- Expected Role of a Partner
-
- Type of partner sought:
Industry, academy, and research organisations
- Specific area of activity of the partner: Researcher, manufacturer in medical field
-Tasks to be performed by the partner sought/Expected role of the partner: Complementary research or use case provider - Type and Size of Partner
- SME 11-49
- SME <=10
- University
- R&D Institution
- SME 50 - 249
- Big company
- Type of partnership
- Research and development cooperation agreement
Dissemination
- Technology keywords
- 01003015 - Knowledge Management, Process Management
- 01001001 - Automation, Robotics Control Systems
- 01003012 - Imaging, Image Processing, Pattern Recognition
- 01003003 - Artificial Intelligence (AI)
- 01003016 - Simulation
- Market keywords
- 02007021 - Other Artificial intelligence related
- 05002005 - Other medical imaging
- 05010003 - Patient rehabilitation & training
- 05007006 - Computer-aided diagnosis and therapy
- 02007016 - Artificial intelligence related software
- Targeted countries
- All countries