Our team focuses on complex, niche challenges that require tailored solutions— built through close collaboration and a deep understanding of each client’s unique processes.
We developed advanced AI architectures for real-time fault detection in additive manufacturing processes of critical components. These systems are designed to analyze high-frequency sensor streams and detect anomalies such as thermal instabilities, or material discontinuities during the build process. Their deployment enhances in-situ quality assurance, enabling earlier fault isolation and reducing costly post-production inspections.
In addition to performance, the models incorporate explainability to ensure transparency and support certification in safety-critical environments. By allowing engineers to understand the rationale behind alerts and predictions, the AI system reinforces trust and facilitates integration into regulated manufacturing workflows where reliability and traceability are essential.
Conventional food production systems often depend on fixed, expertise-driven parameters. Our approach enhances this paradigm by applying Explainable AI (XAI) to autonomously regulate key process variables in real time, guided by continuous sensor feedback. This enables factories to instantly adapt to variability in product characteristics and environmental conditions.
Such an approach marks a pivotal advancement for the food sector, where AI strengthens human oversight with greater precision and responsiveness. By ensuring consistent quality while minimizing resource waste, the technology delivers both economic and sustainability benefits. In addition, it provides full traceability across production batches, supporting transparency and regulatory compliance.
In the field of medical devices, we designed an Explainable AI system capable of predicting and adjusting oxygen flow for patients with respiratory insufficiency. The model is embedded in Emily AI, a smart therapy assistant that continuously monitors patient data to deliver optimal dosing in real time.
Crucially, the system is designed with interpretability at its core. Healthcare providers can visualize how and why dosing recommendations are made, ensuring clinical trust and enabling fine-tuned intervention when needed. This integration of real-time adaptation with transparency makes it suitable for critical care settings and scalable across diverse patient profiles.
We developed an Explainable AI system capable of identifying which genes contribute to the onset and development of specific diseases. By analyzing complex genomic datasets, the model highlights key genetic markers linked to pathological processes, offering actionable insights for both research and clinical applications.
Transparency is central to the system’s design. Scientists and healthcare professionals can explore clear, interpretable explanations showing which genes are influencing model predictions and to what extent. This clarity facilitates trust, accelerates biomarker discovery, and enables informed decision-making in personalized medicine and targeted treatment strategies.
From airports to packaging lines and recycling plants, we design explainable AI systems that optimize complex operations and accelerate sustainability.