Seeking industry & research partners for industrial AI grant collaborations
Industrial AI GmbH is looking for industry, technology and research partners to build strong consortia and joint R&D projects around industrial Artificial Intelligence.
Our current focus is on collaboration opportunities related to German funding initiatives (funded partners must be located in Germany, supporting partners can be international):
- “Industrielle, domänenspezifische KI”: proposal deadline is 1 November 2026, larger transfer projects with domain-specific specialization and deep vertical integration, 10 to 20 Mio Euro funding per consortium (https://ki-und-softwaresysteme.de/foerderschwerpunkte/kuenstliche-intelligenz/industrielle-domaenenspezifische-ki/)
- "IPCEI Artificial Intelligence": regular proposal deadlines are 31 October and 30 April, proposals are supposed to complement the European AI Ecosystem Project IPCEI-AI (https://www.bundeswirtschaftsministerium.de/Redaktion/DE/Dossier/ipcei-ai.html)
We are interested in ambitious projects that bring AI from development into real industrial environments. Potential collaboration areas include:
- Domain-specific AI for manufacturing and industrial processes
- Integration of domain and process knowledge into AI systems
- Data and Machine Learning pipelines for industrial applications
- Connecting heterogeneous industrial data sources and overcoming data silos
- Process and energy optimization
- Quality optimization and anomaly detection
- AI-supported engineering and industrial decision-making
- Transferable AI architectures and solutions that can scale across companies and applications
A particular focus for us is making industrial AI development more efficient while ensuring that solutions remain robust, transparent, and sustainable.
What we bring:
Industrial AI GmbH combines deep Data Science expertise with hands-on experience implementing AI in real industrial environments, allowing us to contribute both methodological AI capabilities and practical implementation capacity to a consortium. Our company has emerged from a research project and is currently working in the funded project "AI Toolkit for Digital Twins" together with FZI Research Center for Information Technology.
Our Pipeline Constructor provides a foundation for rapidly building robust and reproducible Data Science and Machine Learning pipelines. The technology has been validated in 11 real-world customer projects and has demonstrated up to 80% reduction in development time.
Applications to date include AI-supported quotation generation in toolmaking, compressor leakage detection, energy optimization of compressor systems and industrial process optimization.
We are particularly interested in partnering with:
- Industrial companies providing relevant use cases, domain expertise data, real-world demonstration environments and scalable application scenarios
- Larger technology and software companies contributing complementary capabilities
- Universities and research institutions with expertise in AI, industrial systems or domain-specific methods
- Existing consortia looking for an experienced industrial AI and Data Science partner
About Industrial AI GmbH
Industrial AI GmbH provides a modular Pipeline Constructor designed to structure industrial data and enable AI model deployment. The company uses preconfigured code building blocks that can be flexibly combined to handle various data types, including time series, process, ECU, and image data. Its technology stack integrates directly into existing IT landscapes—from edge to cloud—without requiring data extraction or external access. The system features a Component Recommender that suggests optimal modules based on target KPIs, facilitating the creation of production-ready pipelines.
This approach allows for the development of prototypes within six weeks and ensures that analysis code runs independently within the client's own infrastructure. By operating on-premise or in private clouds, the company maintains data sovereignty for its partners. Industrial AI's solutions are currently deployed in customer and research projects, with plans to transition into a software-as-a-service model, aiming to standardize the development and scalability of industrial data analysis.
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