Partner FAQs 

The AI maturity assessment helps us understand the overall level of the organisation (data, governance, infrastructure, skills). The operational survey captures how AI is used in day-to-day activities and where the main challenges are from a user perspective. The use case template is where we define a concrete use case, including value, feasibility, risks, and implementation aspects.

The AI Light Maturity Assessment questionnaire offers an initial view of your organisation's current capabilities. For a more in-depth analysis, companies can apply for a Fit4AI or Skills Plang programme, which helps identify skills gaps and map them to appropriate training paths.

The process typically begins with Fit 4 Digital or Fit4AI, which serves as a structured diagnostic entry point. This assessment clarifies your business objectives, evaluates your readiness, and produces a practical roadmap aligned with your strategy. Once the problem is clearly framed, joint workshops are organised with AI experts from LNDS, LuxProvide, the University of Luxembourg, LIST, and the broader AI Factory ecosystem. These sessions refine the use case by identifying relevant data sources, suitable AI methods, technical feasibility constraints, and regulatory considerations. The outcome is not a vague concept note, but a concise and decision-ready use-case document. It describes the expected business value, required data, performance metrics, risks, compliance aspects, resource needs, and a concrete execution plan.

The AI Factory onboarding process and services are available to both national and European users. However, eligibility and prioritisation criteria may apply depending on the type of service and resource allocation. For non-Luxembourg companies interested in entering the Luxembourg ecosystem, dedicated Soft Landing support is available. This service helps international organisations explore opportunities, understand local requirements, and connect with relevant partners.

Support applicants from shaping the initial idea to defining the data set required to build a valuable, effective model. At the same time, we assist data holders by assessing and exploring the availability of relevant data sets on the market, strengthening our role in data discovery. Through the AI Factory one-stop-shop, you are guided in framing your needs and connecting with the appropriate data services and partners.

Access is possible, but always via secure environments, privacy safeguards, and controlled processes such as Secure Processing Environments (SPEs) (e.g., SPE built on top of Meluxina-AI), and our IPMS service that tackles data pseudonymization and identity matching to ensure data privacy — never through direct access to raw sensitive data. A specially tailored service for the preparation of data requests and support in the process is Data Access Request Review.

In case of request for re-use of public sector data, expect a formal response within 2–3 months under the Data Governance Act (DGA). For other cases, it depends on the rules set by the data holder.

We help you determine whether your data is fit for AI training by combining quality checks and relevance assessments through our dedicated data quality and curation service.

Yes, we can support anonymisation, pseudonymisation, and data minimisation through our IPMS service, which provides the controlled workflows and governance needed to apply these privacy-enhancing techniques securely and in compliance with regulatory requirements.

Yes, we can support data sharing across multiple partners through our Identifier and Pseudonym Management Service (IPMS), which enables secure, controlled data exchange. The IPMS provides pseudonymisation in a decentralised manner, allowing data holders to pseudonymise and securely manage personal data at the source. It supports both individual and collaborative pseudonymisation projects, facilitating the secondary use of data in a secure environment for downstream analysis and related purposes. The AI Factory one-stop-shop facilitates coordination between stakeholders, ensuring an efficient and structured collaboration setup. For projects involving multiple partners, we can support the establishment of a joint Non-Disclosure Agreement (NDA) to streamline legal and compliance processes and protect all parties' interests.

Yes, our Identity Matching and Pseudonym Management Service is designed to facilitate privacy-preserving, record-linkable data sharing in Luxembourg, while Data Extraction, Enrichment and Merging service supports the secure provision and mapping of data from multiple providers while ensuring that data minimisation principles are respected.

Yes, we support clients in preparing and transforming their data for use in infrastructures such as MeluXina-AI through our data quality and curation service. This service ensure that datasets are properly cleaned, structured, validated, and optimised so they can be efficiently ingested and processed in high-performance computing environments.

 Introducing and maintaining the Data Governance mechanism is a permanent process that lives and changes following all the changes and functioning of the organization. Application of the AI model changes the view on data in organizations and requires greater focus, which is expected to be supported by adequate methods and tools. Accordingly, permanent work on improving the Data Governance process is expected even after the deployment of the AI model to ensure the application of remediation procedures and facilitate the regular review of the model. Data Management and Stewardship Support service helps organizations in achieving, improving and maintaining the Data Governance mechanism through consultations, trainings and co-development of policies, procedures, and work instructions.

Yes, we can continue to support organisations as their AI system develops, helping them stay aware of evolving requirements and pointing them toward suitable resources whenever new questions arise.

Timelines vary depending on the complexity of the project, the maturity of the company, the availability and quality of data, and whether external funding or consortium partners are involved. In straightforward cases, where the problem is well defined, data is readily available, and no external funding is required, companies can move from initial contact to pilot launch within a few months. More complex projects, for example those requiring significant data preparation, regulatory analysis, multi-partner coordination, or EU funding applications, naturally take longer. The approach is designed to remain flexible: the pathway adapts to the ambition, risk level, and operational constraints of each project.

The AI Sandbox is a controlled environment designed to test the trustworthiness of AI systems. It enables organisations to assess risks, evaluate performance, and validate AI solutions in a safe and structured setting before deployment. Through the AI Factory one-stop-shop, organisations are guided in accessing and leveraging sandbox capabilities based on their specific use case.

A sandbox can be used at any stage of the AI lifecycle, from early experimentation to pre-deployment validation. It is particularly valuable before production, as it allows organisations to test performance, robustness, and compliance aspects in a controlled environment. Using a sandbox also supports continuous monitoring requirements under the EU AI Act, helping ensure that systems meet regulatory expectations before and after deployment.

Yes. The sandbox is modular and adaptable by design, allowing validation under a wide range of real-world and sector-specific constraints. It can be deployed on different infrastructures, including local environments, enabling organisations to test solutions in conditions close to their operational context. Ongoing developments, such as the Sandbox Configurator (in collaboration with the University of Luxembourg), further enhance the ability to tailor testing environments to specific use cases.

This depends on the specific use case and system architecture. Integration of multiple AI models within a unified framework can be explored and structured as part of the overall solution design, depending on technical and operational requirements.

Robustness and reliability are assessed through comprehensive testing across multiple dimensions, including bias, accuracy, and factual consistency. Particular attention is given to multilingual performance, reflecting Luxembourg's diverse and multilingual environment, to ensure consistent quality of service across different user groups.

We are actively involved in the design of documentation and reporting which is user firendly across a diverse range of stakeholders including technical experts, risk anagers, legal experts, ethic experts, etc.

Yes. Testing processes are designed to generate evidence that can support certification and compliance activities. Documentation and reporting are aligned with evolving AI regulations and standards, ensuring consistency with current compliance requirements, including those related to the EU AI Act.

Yes. Sustainability can be supported through approaches such as frugal AI, which focus on optimising computational efficiency and reducing the energy consumption of AI models. This helps lower environmental impact while maintaining performance. Such approaches enable organisations to align AI development with broader sustainability and efficiency objectives.

AI and digital twins support each other in complementary ways. AI models can be embedded within digital twins to enhance simulation, forecasting, and decision-making capabilities. At the same time, data generated by digital twins can be analysed using AI to extract insights, identify patterns, and improve system performance over time. In addition, AI can support the design and configuration of digital twins, enabling more tailored and adaptive analytical and simulation services.

You should begin using these resources as early as the design and prototyping phase. This allows organisations to validate use cases earlier and make more informed decisions regarding further development and scaling

Most AI workloads can benefit from HPC environments. The main limitation relates to the training of extremely large models, which may require a significant share of available resources. Through the AI Factory one-stop-shop, organisations are supported in identifying the appropriate stage to integrate such infrastructure into their AI journey.

No. You do not need in-house HPC expertise to benefit from these services. LuxProvide offers dedicated consultancy and technical support throughout your journey, helping you define, implement and optimise your use of high-performance computing according to your specific needs. Through the AI Factory one-stop-shop, you are guided toward the appropriate expertise and support, ensuring a smooth and accessible experience regardless of your starting level.

The MeluXina-AI environment includes a range of pre-installed tools designed to support different stages of the AI development lifecycle, from experimentation to deployment. These include: Inference: vLLM and Ollama (for running and serving AI models); Interface: OpenWebUI and JupyterHub (for user interaction and development); Workflow orchestration: n8n (for automating processes and pipelines); Storage and vector databases: Qdrant and Milvus (for managing and querying data, including embeddings). This integrated environment enables organisations to efficiently develop, test and deploy AI solutions without the need to configure tools from scratch. Access to this environment is facilitated through the AI Factory one-stop-shop, ensuring alignment with your project needs and technical requirements.

Optimization is managed through three main areas: Job Scheduling — we use SLURM for job scheduling where users request only the resources they need. Accurate resource requests reduce costs by avoiding unnecessary node allocations. Storage Tiers — data movement is optimized using different tiers, such as "scratch" for temporary I/O heavy tasks versus permanent long-term storage. Data Movement — unlike hyperscalers, LuxProvide generally does not charge for downloading or uploading data, which is a significant cost differentiator.

Yes. Integration between HPC infrastructure and existing cloud or on-premise environments is possible and can be implemented through various approaches, including direct connections, VPN connectivity, or more customised configurations depending on your requirements. This flexibility enables organisations to design hybrid architectures that best fit their technical and operational needs. Through the AI Factory one-stop-shop, you are supported in defining and implementing the most suitable setup based on your use case.

Access to computing resources is managed through SLURM-based scheduling queues, which allocate capacity based on job priority, resource availability, and principles of fair usage. Priority is defined according to project allocations, with options for both dedicated nodes and on-demand resources depending on project requirements. This ensures an efficient and balanced distribution of resources across users while maintaining performance and reliability. Through the AI Factory one-stop-shop, organisations are supported in understanding and planning resource allocation in line with their project needs.

We support the training of medium to small-sized models as well as fine-tuning of existing models.

This is currently a work in progress.

Funding opportunities depend on your company size, project scope, and level of maturity. Luxinnovation supports you in navigating this landscape by identifying the most relevant national and European funding instruments aligned with your business objectives and project ambition — whether at an early exploration stage or for more advanced development and innovation projects.

Luxinnovation plays a central role in aligning your project with applicable funding frameworks and supporting its positioning in line with programme requirements and expectations. This includes structuring your initiative, clarifying its value proposition, and ensuring coherence with evaluation criteria and strategic priorities. Through its close interaction with funding programmes and the broader innovation ecosystem, Luxinnovation facilitates a more streamlined and informed approach to funding. This enables you to better understand expectations, anticipate requirements, and position your project accordingly. Throughout the process, you benefit from dedicated guidance that helps clarify requirements, reduce complexity, and ensure a well-prepared, consistent, and credible application approach.

Certification depends on the training provider. Most courses include certificates of completion, while academic partners may award formal academic credits when the training is part of an accredited programme. Through the AI Factory one-stop-shop, organisations are guided toward the most relevant training options based on their objectives and desired level of recognition.

Yes, the programmes are tailored for different audiences, including managers, business users, and technical profiles.

Some training providers offer modular or stackable learning paths, while others provide standalone courses. Our catalogue is designed to give clear visibility across all available training options so that each company or individual can combine the programmes that best suit their needs and progressively build their own learning path over time.

Certification depends on the training provider. They may issue certificates of completion, and academic partners can award academic credits for courses taken as part of an accredited programme.

Training can be adapted through tailored recommendations and curated learning paths aligned with your sector and business context. While the core content remains the responsibility of individual training providers, the AI Factory supports the selection and combination of programmes to best fit your organisational needs. Industry feedback is continuously integrated to ensure that training offerings remain relevant and aligned with real-world use cases.

 Courses are available online, on-site, or in hybrid formats, depending on the provider's offer.

Ensuring alignment with the latest AI standards and regulations is primarily the responsibility of each training provider. However, we curate the catalogue to maintain the highest level of quality, taking into account participant feedback and the evolving regulatory landscape. All AI Factory partners are committed to meeting rigorous quality standards, and we review the catalogue regularly to ensure it reflects current best practices and compliance requirements.

Impact is assessed through follow-up with participating organisations, including structured feedback and satisfaction questionnaires after each training. In addition, the AI Factory places emphasis on how training contributes to practical outcomes, such as improved internal capabilities, identification of relevant use cases, and readiness to move toward implementation. Where relevant, this can be further discussed and monitored in the context of ongoing AI initiatives.

Yes. We support the principles of explainable and trustworthy AI across our training activities. These themes are reflected in the programmes delivered by AI Factory partners, and we prioritise them when curating the broader training catalogue. While external providers are responsible for their own content, we include only courses that align with our overall focus on responsible and transparent AI.

The AI Factory facilitates connections at both national and European levels. Matchmaking goes beyond networking events. It covers experts, datasets, computing resources, compliance support, and co-innovation partners, with the objective of accelerating trusted AI adoption.

Yes. We actively work with both industrial partners and public sector organizations. As a public research and technology organization, LIST engages in collaborative research, technology transfer and innovation projects that support industry development, strengthen technological capabilities and address societal challenges.

Any request related to access, installation of new software or packages, or other technical inquires must go through Service Desk.

Listing your datasets typically takes approximately 15-45 minutes per dataset, depending on the current documentation level, and you can rely on the support of our dedicated team that will guide you through the process.

Publishing datasets can help you increase strategic data visibility, improve data governance in your organisation, unlocke new revenue streams, and enable new collaborations.

No - as you onlu publish metadata and not the dataset itself, the underlying data remains fully under your control. You retain full ownership of your data as well as its access and usage rights, allowing you to make decisions on whether to grant access or now on a case-by-case basis.