Open Call #1
Attractive funding for your next product & service development
- Up to 150k € funding per project for SMEs and start-ups
- Focus on AI and Data driven solutions for:
- Data Collection
- Creation of insights
- Decision support
- Automation
- Comprehensive insights into industrial needs
- Easy application
Deadlines
30 June 2025
Opening
11 September 2025
Submission deadline (17:00 CET)
What we offer
- Up to 13 projects tailored to real-world innovation challenges will be funded with a maximum of €150,000 each
- Selected applicants will have up to 14 months to deploy their innovative solutions
- Selected applicants will receive additional support for maximizing impact and growth (training sessions, coaching, mentoring)
- Become part of the AID4SME Community of Practice and benefit from the opportunities and the wide network of professionals in the field
Benefits for SMEs/start-ups
- Funding for innovative companies to pilot their solutions
- Access to early customers
- Helping SMEs/start-ups overcome market entry barriers
- Domain knowledge and expert advice to scale up
Open Call Challenges
Combined AI and Data solutions for DATA COLLECTION
1.1 - Augmented sensing solution
The selected third party is expected to contribute by providing:
- A working prototype or demonstrator of an augmented sensing system that can monitor and analyze relevant process parameters during mold changes without requiring intrusive sensor installation.
- Sensor integration plans and any custom sensing hardware (if applicable), with emphasis on low cost, robustness, and ease of installation.
- Data processing and AI algorithms are capable of detecting misalignments, stress variations, or anomalies that indicate suboptimal mold setup.
- Performance validation showing measurable reductions in waste, downtime, and adjustment time (ideally quantified improvements over current KPIs).
- Deployment guidelines and documentation, including a clear strategy for industrial scaling and integration into other lines or facilities.
- Contribution to sustainability objectives, such as reducing material waste and energy consumption in line with the European Green Deal.
1.2 - 2D/3D image analysis for large Energy Infrastructure predictive maintenance
The selected third party is expected to contribute by providing:
- Vegetation Recognition in Transmission Line Corridors: Utilizing multispectral/hyperspectral cameras, it shall be made possible to recognize vegetation types from imagery. This is important because different types of vegetation grow at different rates, making it easier to predict when intervention will be necessary.
- Assessing Vegetation Health: With multispectral/hyperspectral cameras, it shall be made possible to assess the health of vegetation from imagery. This is crucial because healthy vegetation poses significantly less risk to infrastructure compared to diseased or damaged specimens.
- Assessing Infrastructure Condition: Using multispectral/hyperspectral imagery, it shall be made possible to determine the condition of infrastructure. Discussions indicate that it is possible to detect the amount of biofilm and dirt on insulation, identify corrosion, detect overheating, and more.
- Other Use Cases: Additional applications may also be considered.
Combined AI and Data solutions for DATA COLLECTION
2.1 - Product-production Digital Twins
The selected third party is expected to contribute by providing:
Product-production digital twin solution to optimise material mixing ratios based on real-time measurement of visual and mechanical properties of the different plastic source materials.
2.2 - Digital Twin based lifespan analysis tool
The selected third party is expected to contribute by providing:
Product-production digital twin solution to optimise material mixing ratios based on real-time measurement of visual and mechanical properties of the different plastic source materials.
2.3- Energy system Digital Twin decision support tool
The selected third party is expected to contribute by providing:
Develop an optimization algorithm for optimal sizing of a microgrid components using a microgrid Digital Twin
Develop a microgrid grid energy control algorithms using predictions of energy needs and production
Implement and test the developed solutions in a digital environment with real world data and for different scenarios
Combined AI and Data solutions for DECISION SUPPORT
3.1 – Digital Twin-Enabled Smart Production Process Planning Tool
The selected third party is expected to contribute by providing:
Develop a solution that uses historical demand patterns and future demands to build more robust, flexible, and data-driven planning strategies.
The tool developed through this challenge will analyze customer demand trends over time to uncover patterns and enable the grouping and classification of customers based on their demand characteristics.
Combined AI and Data solutions for AUTOMATION
4.1- Automated energy management for parts production
The selected third party is expected to contribute by providing:
Develop a smart energy management strategy and algorithm based on the energy consumption models of the machine modes
Develop a module for monitoring and management of high energy intensive machines
Demonstration of the prototype on the industrial setup
4.2 - Automated energy management for battery production
The selected third party is expected to contribute by providing:
Provide an Energy Management System, based on energy consumption models and machine models.
Provide a monitoring and management module for the most energy intensive machines. Be able to demonstrate the prototype at pilot scale.
4.3 - Semi-automated EV battery disassembly for recycling
The selected third party is expected to contribute by providing:
Battery Assessment: Evaluation of battery performance, capacity, and overall condition.
Safety Measures for Battery Handling: Protocols and best practices to ensure safe handling, storage, and transportation of batteries.
Battery Defect Detection: Identification of manufacturing or operational defects in batteries using diagnostic tools and inspection methods.
Battery Health Monitoring: Assessment of battery state-of-health (SoH) using sensors and diagnostic data (e.g., temperature, voltage, internal resistance).
Battery Charging and Discharging Methods: Procedures and technologies for safe and efficient charging and discharging of batteries.
Decision-Making for Second Life, Reuse, or Recycling: Criteria and processes for determining whether a battery should be reused, repurposed for second-life applications, or sent for recycling.
4.4 - Co-bot refrigerator door assembly solutions
The selected third party is expected to contribute by providing:
Develop a co-bot-assisted gasket installation solution
Integrate AI-based perception and control systems to allow the co-bot to adapt to different gasket shapes and models.
Ensure compliance with safety and ergonomic standards, by reducing physical strain on human workers and improving workplace safety with co-bot working.
Demonstrate human-robot collaboration capabilities, where the co-bot supports rather than replaces the human operator, ensuring intuitive interaction and task sharing.
How to apply
Get informed
Read the Guidelines for Applicants
Register
Access F6S platform
Submit
Submit your application through the F6S platform
Who can apply
- Single legal entities or in a consortium of 2 entities
- Micro, small and medium-sized enterprises working on related technologies (SMEs) and Start-Ups
- The following organisations may apply as the second partner in a consortium:
- Industry organisations providing the SMEs / Start-ups use-cases and high-TRL playgrounds for solution testing and validation
- Integrators / engineering services providers that support the SMEs / Start-Ups in integration of the solutions
- Research institutions, research infrastructures, non-profit organisations and charitable (scientific) foundations and public research centers
Open Call Documentation Kit
NOTE: Please read carefully the Guidelines for Applicants.
Open Call Evaluation Criteria
Novelty/ Innovation
Impact
Project planning
Expertise
