AID4SME — Open Call 2 (AI a dátové riešenia pre MSP)

Skip to content Project About Consortium Resources Open Calls Open Call 1 Open Call 2 Expression of Interest for External Evaluators Pilots Community of Practice News Contact Open Call 2 Open Call #2 This call is CLOSED Attractive funding for your next product & service development Up to 200k € funding per project for SMEs and start-ups Focus on AI and Data driven solutions for: Data CollectionCreation of insightsDecision supportAutomation Comprehensive insights into industrial needs Closed Read the Guidelines for Applicants first. Deadlines 15 May 2026 Opening 15 July 2026 Submission deadline (17:00 CEST) What we offer Up to 12 projects tailored to real-world innovation challenges will be funded with a maximum of €200,000 each Selected applicants will have up to 14 months to deploy their innovative solutions Selected applicants will receive additional support for maximising 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 See full description The selected third party is expected to contribute to (one of) the following points (in applying to this challenge, please specify which point you would like to focus on):Collaboration with Arçelik to develop an augmented sensing solution for its refrigerator production facility. The solution will enable accurate monitoring of forces, stresses, or other relevant physical quantities during plastic mold changes in the refrigerator production line. It will provide real-time feedback and predictive insights to reduce manual intervention and iterative adjustments during mold changes, minimize polyurethane waste, and reduce facility downtime. Collaboration with KU Leuven to develop and test augmented sensing solutions, specifically for mecha(tro)nic system level dynamics, and model-based augmented sensing in electro-thermo-mechanical applications in the manufacturing industry. The focus will be on the technological development of the augmented sensing solution (e.g., targeting force, stress, and torque estimation), with the availability of several industrially relevant laboratory setups and measurement equipment. Combined AI and Data solutions for creation of INSIGHTS 2.1 - Product-production Digital Twins See full description The selected third party is expected to contribute to (one of) the following points (in applying to this challenge, please specify which point you would like to focus on):Collaboration with Arçelik to develop a 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 (virgin and recycled plastic)Collaboration with Arçelik to combine augmented sensing (cf. challenge 1.1) with a product-production digital twin solution to optimise End-of-Life product refurbishment processes by assessing the health and predicting the remaining lifespan of componentsCollaboration with KU Leuven to link the manufacturing process to product quality in mecha(tro)nic applications. The focus will be on the technological development of a dedicated product-production simulation framework for injection moulding manufacturing processes, with the availability of a fully equipped injection moulding laboratory facility. 2.2 - Automated monitoring hygiene inspection for refurbishment See full description 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 See full description 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 TwinDevelop a microgrid grid energy control algorithms using predictions of energy needs and productionImplement 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 3.2 – Automated machine selection for parts production See full description 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. 3.3 - Battery production digitalwork instructions & skill capturing See full description 3.4 - Automated warehouse and internal logistics management See full description Combined AI and Data solutions for AUTOMATION 4.1- Smart energy management of unplanned machine downtime See full description 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 modesDevelop a module for monitoring and management of high energy intensive machinesDemonstration of the prototype on the industrial setup 4.3 - Semi-automated EV battery disassembly for recycling: introducing digital work instructions See full description 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 See full description The selected third party is expected to contribute by providing:Develop a co-bot-assisted gasket installation solutionIntegrate 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 via F6S platform Who can apply Single legal entities or consortia of up to two, though only SMEs and Start-ups are eligible for financial support. SMEs and Start-ups may apply alone or lead a consortium, with a limit of one application per entity. The following organisations may apply as a second partner (Industry and Integrators share the one-application limit, while Research/Non-profits are exempt): Industry organisations providing the SMEs / Start-ups use-cases and high-TRL playgrounds for solution testing and validationIntegrators / engineering services providers that support the SM