Sustain AI is an engineering company who develops tailored, explainable and auditable AI solutions for complex industrial processes, specialized in crafting personalized, explainable AI models for industrial processes.
Through AI, we enhance the sustainability of processes and technologies, focusing on Circular Economy initiatives.
Sustain AI was created to bridge the gap between advanced AI and real industrial operations.
We understand factories, production constraints, quality requirements, operational risk and the pressure to deliver measurable results. We do not approach industrial AI as a theoretical exercise.
We design AI systems that not only generate outputs, but also help explain why a recommendation, prediction or diagnosis has been produced. This is essential for trust, adoption and responsible deployment.
We work with external knowledge networks, technology centers, universities and strategic partners when a challenge requires complementary capabilities. Sustain XP acts as the industrial orchestrator of the solution.
Industrial companies generate more data than ever, but much of that data remains underused. At the same time, many operations teams face increasing pressure to improve efficiency, reduce waste, lower emissions and make better decisions in uncertain environments.
Traditional optimization has often reached diminishing returns. The next step requires a different approach: combining industrial know-how, process data, advanced sensing and Explainable AI to identify what is really happening inside the process and what to do next.
Defect risk prediction
Inline quality controll
Al models must be understandable by engineers, operations teams and decision-makers.
We prioritise transparency, traceability and auditability.
Every project must show how sustainability creates business value through better material efficiency, process efficiency and measurable ROl.
Advanced AJAX search functionality with multiple layout options that are capable of bringing up results in real-time.
Industrial sectors and real-world applications where explainable AI creates measurable impact.
We work with industrial sectors where process complexity, quality requirements, resource efficiency and sustainability are critical.
AI for process optimisation, quality control, defect prediction and operational decision support.
AI and sensing solutions for quality, contamination risk, waste reduction, process efficiency and product consistency.
AI to classify, characterise, reuse and reintroduce materials into industrial value chains.
Optimisation of materials, defects, resource use, logistics assets and circularity models.
AI for textile waste characterisation, recycled fibre quality prediction, blend prescription and traceability.
Explainable AI for high-responsibility processes, defect prediction, process control and manufacturing quality.
Our work focuses on industrial use cases where AI can improve decisions, reduce uncertainty and support measurable operational impact.
AI-assisted diagnosis and repair prescription for logistics elements, helping teams identify defects and define corrective actions.
Predictive models for high-responsibility parts, supporting earlier detection of potential failures and improving manufacturing reliability.
Explainable models to anticipate defects in cast components and support better control of complex manufacturing parameters.
AI applied to packaging lines to support process optimisation, product consistency and waste reduction.
AI and sensing applied to earlier quality assessment in food processes, supporting faster and more reliable decision-making.
AI for identification, prediction and prescription of textile blends to improve textile-to-textile recycling quality and reduce downcycling.
Sustain AI collaborates with technology partners, research organisations and innovation ecosystems to develop AI solutions that are practical, explainable and scalable.
Sustain XP and Argit AI collaborate to develop intelligent solutions that help industrial companies become more sustainable, efficient and competitive.
This collaboration combines Sustain AI’s industrial experience in process optimisation, sustainability and circular economy with Argit AI’s expertise in explainable and high-performance artificial intelligence.
Together, we address complex industrial challenges where data, process knowledge and advanced AI can create measurable environmental and business impact.
Sustain AI and the Aragon Institute of Technology (ITA) maintain a strategic collaboration focused on applying advanced technologies to industrial sustainability challenges.
Sustain AI contributes its experience in industrial processes, circular economy and sustainability-driven transformation, while ITA brings its capabilities as a technology centre in R&D&I, digital transformation, advanced engineering and technological innovation.
This collaboration enables both organisations to explore and develop solutions that help companies improve efficiency, reduce environmental impact and accelerate their transition towards more sustainable industrial models.
Co-founder and CEO of Nitid AI. He obtained his Ph.D. in eXplainable Artificial Intelligence applied to Aerospace Engineering at the University of Cincinnati and spent his last doctoral year at MIT. He then did his PostDoc in AI at MIT. He comes from an Aerospace and Mechanical engineering background, and he has developed tailored AI solutions for different organizations such as Boeing, Aurora Flight Sciences, Satlantis Microsatellites, NASA, ESA, and the CVG Northern Kentucky International Airport.
AWARDS
MEDIA
Insightful and seasoned CEO & MD, with a proven track record of leadership, and excellent results through strategic skills in driving revenue and profit growth through brand development.
Demonstrated ability to identify and troubleshoot critical issues impacting productivity, cost, distribution, marketing, sales and financial operations. This is archived through inspired and motivated high-performance teams.
Madrid 2019 – 2023
Managing Director Dairy – Madrid
Managing Director of Industrial Business, new Businesses and Internationalization – Madrid
Madrid & Düsseldorf & Paris 2007 – 2019
Head of Cluster Europe South – Madrid & Düsseldorf
Head of Market France & Iberia / GAM Lactalis
Madrid & Paris & Bilbao & Pamplona, From 1990 to 2007
General Manager Smurfit Nervión – Bilbao
Industrial Operations Director Smurfit Paper Sack European Division – Paris & Madrid
General Manager Smurfit Ibersac – Bilbao
General Manager Smurfit Kappa Navarra – Pamplona
Co-founder and CEO of Nitid AI. He obtained his Ph.D. in eXplainable Artificial Intelligence applied to Aerospace Engineering at the University of Cincinnati and spent his last doctoral year at MIT. He then did his PostDoc in AI at MIT. He comes from an Aerospace and Mechanical engineering background, and he has developed tailored AI solutions for different organizations such as Boeing, Aurora Flight Sciences, Satlantis Microsatellites, NASA, ESA, and the CVG Northern Kentucky International Airport.
AWARDS
MEDIA
Insightful and seasoned CEO & MD, with a proven track record of leadership, and excellent results through strategic skills in driving revenue and profit growth through brand development.
Demonstrated ability to identify and troubleshoot critical issues impacting productivity, cost, distribution, marketing, sales and financial operations. This is archived through inspired and motivated high-performance teams.
Madrid 2019 – 2023
Managing Director Dairy – Madrid
Managing Director of Industrial Business, new Businesses and Internationalization – Madrid
Madrid & Düsseldorf & Paris 2007 – 2019
Head of Cluster Europe South – Madrid & Düsseldorf
Head of Market France & Iberia / GAM Lactalis
Madrid & Paris & Bilbao & Pamplona, From 1990 to 2007
General Manager Smurfit Nervión – Bilbao
Industrial Operations Director Smurfit Paper Sack European Division – Paris & Madrid
General Manager Smurfit Ibersac – Bilbao
General Manager Smurfit Kappa Navarra – Pamplona
Begoña Calvo es Catedrática de Mecánica de los Medios Continuos en el Departamento de Ingeniería Mecánica de la Universidad de Zaragoza (España) desde 2010. Es miembro del I3A (Instituto de Investigación en Ingeniería de Aragón) y del CIBER_BBN (National Networking Center on Bioengineering, Biomaterials and Nanomedicine).
Su investigación se desarrolla en el campo de Mecánica de Medios Continuos y la Biomecánica Computacional. En los últimos años centrada en el diseño de Gemelos Digitales aplicados a distintos campos de la Ingeniería, combinando simulación numérica mediante elementos finitos e inteligencia artificial.
