Open Horizons Open Call #3 brings together startups and leading corporates to address real-world technological challenges across multiple industries. The programme focuses on supporting deep-tech innovators who can deliver scalable solutions with strong market potential. The challenges published in Open call #3 originate directly from industry partners and reflect urgent needs in areas such as artificial intelligence, sustainability, advanced materials, energy efficiency, supply chains, and digital transformation. Startups applying to the programme are invited to select the challenge that best aligns with their technology and demonstrate how their solution can create measurable impact in that sector.
Below is an overview of the twelve corporate challenges.
More details are available in the Guidelines for Applicants.
1. Reliable and Data-Efficient Machine Learning for Engineering Simulation
Engineering simulations are critical in industries such as aerospace, automotive, and energy. However, current machine learning approaches often require massive datasets and may lack the reliability required for industrial deployment.
This challenge seeks solutions that can develop data-efficient and trustworthy machine learning models capable of accelerating engineering simulations while providing clear accuracy and confidence metrics.
Key focus areas include:
ML models requiring limited high-fidelity training data
Built-in uncertainty quantification
Improved generalisation across different simulation scenarios
Integration with existing engineering workflows
2. Secure and Efficient Model Exchange in Simulation
Engineering ecosystems increasingly rely on collaboration across multiple organisations. However, exchanging simulation models raises concerns about intellectual property protection, compatibility, and data security.
This challenge aims to develop technologies that enable safe, standardised, and efficient model sharing between partners while protecting proprietary information.
Potential solutions may include secure model formats, interoperability tools, or controlled data-sharing environments.
3. Smart Tax Strategy Design for International Business Expansion
As companies expand globally, tax strategy becomes increasingly complex due to varying regulations, jurisdictions, and reporting requirements.
This challenge seeks digital solutions that can help organisations design optimal international tax strategies, ensuring compliance while improving efficiency in global operations.
Innovations may include AI-driven modelling, automated tax analysis, and decision-support systems for multinational expansion.
4. Intelligent Tax Compliance and Optimisation for Global Operations
Beyond strategic planning, corporations must also manage ongoing tax compliance across multiple jurisdictions. This process is often resource-intensive and prone to errors.
This challenge focuses on intelligent tools that can support:
Automated tax compliance monitoring
Real-time regulatory updates
Data-driven optimisation of corporate tax processes
Solutions should reduce manual work and improve transparency in international tax management.
5. Ultra-Rapid Biodegradable Polymer Materials for Controlled Applications
Sustainable materials are essential for reducing environmental impact across industries such as packaging, healthcare, and agriculture.
This challenge invites innovations in biodegradable polymer materials that can degrade rapidly while maintaining performance during use.
Solutions should explore advanced material science, novel polymer structures, and scalable manufacturing methods.
6. AI-Powered Optimisation of Last-Mile Delivery
The rapid growth of e-commerce has placed enormous pressure on last-mile delivery systems, particularly for multi-channel retail operations.
This challenge seeks AI-driven solutions capable of optimising logistics processes, including:
Delivery route planning
Real-time demand management
Integration across multiple sales channels
Reduction of operational costs and emissions
7. Upcycling Food Loss into High-Value Circular Products
Food waste is a major environmental and economic issue across global supply chains. Large volumes of food are lost before reaching consumers.
This challenge focuses on innovative technologies that convert food loss into valuable products, contributing to the circular economy.
Examples may include:
New processing technologies
Bio-based ingredients
Alternative materials derived from food waste streams
8. Innovative Energy Solutions for Retail Stores and Distribution Centres
Retail operations and logistics facilities consume large amounts of energy. Improving efficiency in these environments can significantly reduce both costs and emissions.
This challenge invites innovative energy solutions that can support more sustainable retail operations, such as:
Smart energy management systems
Renewable energy integration
Energy storage technologies
AI-driven optimisation of energy consumption
9. AI-Assisted Sustainable Formulation for Barrier Materials
Developing sustainable barrier materials for packaging requires extensive testing and experimentation. Traditional development cycles are slow and resource-intensive.
This challenge aims to accelerate innovation by using AI-assisted formulation tools that help researchers identify sustainable material combinations more efficiently.
Solutions should support faster R&D cycles while maintaining high performance and sustainability standards.
10. AI-Powered Onboarding and Employee Journey Assistant
Employee onboarding and professional development processes are often fragmented and inefficient.
This challenge seeks AI-powered tools capable of guiding employees throughout their journey within an organisation, from onboarding to ongoing career development.
Potential solutions include:
Personalised onboarding assistants
AI-driven training recommendations
Employee engagement platforms
Knowledge management systems
11. Automated Demand and Market Forecasting Using Public Data
Accurate forecasting is essential for business agility, but traditional models often rely heavily on internal data and may struggle to capture broader market signals.
This challenge focuses on building automated forecasting systems that leverage public data sources, including economic indicators, market trends, and open datasets.
Solutions should enable companies to improve strategic planning and respond more effectively to market changes.
12. AI for Production Bottleneck Detection and Workflow Optimisation
Manufacturing operations often suffer from hidden inefficiencies and bottlenecks that limit production capacity.
This challenge invites solutions that apply AI-driven process analysis to identify bottlenecks, optimise workflows, and improve overall production throughput.
The goal is to enable manufacturers to make faster decisions and increase operational efficiency.
Driving Industry Innovation Through Collaboration
The Open Horizons challenges represent real market opportunities for deep-tech startups to collaborate directly with corporate partners. By addressing these challenges, selected startups will have the opportunity to:
Pilot their technologies in real industrial environments
Validate solutions with corporate partners
Accelerate market adoption and scalability
Open Horizons aims to bridge the gap between cutting-edge research and industry deployment, supporting the next generation of transformative technologies.