Use
Cases
The OptimAIse framework is built upon high-impact use cases in three sectors critical to the AI landscape: AI Factories, robotics, and healthcare.
Driven by active end-user engagement and close technical collaboration, these solutions will be deployed as pilots in relevant industrial environments.
AI Factory validation
and potential adoption
Motivation
AI Factories are dynamic ecosystems designed to lower the high entry barriers facing SMEs and research centres, such as prohibitive infrastructure costs and complex data curation.
By providing a framework that automates intricate processes while ensuring full compliance with EU AI regulations, OptimAIse directly addresses these challenges.
Objectives and approach
Through a collaborative co-development model, OptimAIse will establish a secure, self-optimising framework for deploying trustworthy AI systems. Key components from the framework will be adapted to the AI Factory ecosystem to benefit third parties at the Barcelona Supercomputing Center (a hub hosting one of Europe’s thirteen AI Factories).
Main expected impacts
Through the integration and adoption of OptimAIse tools, we will expand the AI Factory ecosystem. The approach will lower major entry barriers for SMEs and small RTOs regarding data management and the model lifecycle.
It will decrease operational costs and drive sustainable solutions. Ultimately, OptimAIse will democratise access to AI tools, empowering teams to focus on their core solutions rather than the underlying infrastructure.
Intelligent priority
based robot support
Motivation
Robots operating in human-centred spaces must navigate shifting priorities and unpredictable constraints. To address this, our second pilot, OFFIS, is developing an AI-driven solution that allows mobile robots to interpret spoken commands, autonomously prioritise tasks, and plan efficient routes.
By using multimodal large language models for visual identification and package assignment, the system adapts dynamically to user-defined constraints, providing intuitive and resilient support in complex environments.
Objectives and approach
Leverage the OptimAIse framework to implement a hardware-agnostic AI-driven capability that translates user requests into context-aware, priority-sensitive action plans. The system will securely execute these tasks while clearly explaining its decision-making process, ensuring transparent and priority-aware robotic support in dynamic settings.
This architecture combines large language models with real time situational awareness to enable intelligent robot decision making. By leveraging OptimAIse tools, the system compresses models for edge deployment and optimises workloads to maximize energy efficiency.
Main expected impacts
The OptimAIse framework directly enhances model development by accelerating training and tuning, while reducing infrastructure costs and environmental impact through advanced compression and intelligent orchestration. Ultimately, it improves performance and usability, ensures safety through policy aligned actions, and builds trust through embedded transparency and accountability.
Healthcare
chatbot
Motivation
Cancer patients often find themselves in an information vacuum, left with complex questions and no immediate way to get answers between appointments.
Existing resources frequently lack the interactive, empathetic touch patients need, or are too vague and difficult to navigate, adding burden to an already exhausting time.
Objectives and approach
OptimAIse tools will enable the creation of a dependable, 24/7 GenAI companion for prostate cancer patients that delivers medically accurate support. Operating in a highly sensitive domain, this chatbot relies on RAG technology, prioritizing ethical AI, robustness, explainability, and the mitigation of bias.
Built on the OptimAIse Reference Architecture, the system uses intelligent orchestration and model compression to ensure high performance, low latency, and energy efficiency. The framework natively integrates rigorous ethical checks and a comprehensive audit trail to guarantee fair, transparent, and regulatory-compliant responses.
Main expected impacts
Patients will gain reliable, around-the-clock access to clear and personalized medical guidance. Healthcare providers will benefit from a sustainable, scalable solution that minimizes risk through built-in traceability and continuous validation, ensuring AI-driven care remains safe, resilient, and human-centered.