Gefond Perpetuo AI predictive maintenance 2026 is entering real production environments as die casting companies begin using artificial intelligence to reduce downtime and stabilise operations. At Euroguss, Gefond officially introduced Perpetuo, its AI-based predictive maintenance platform designed to extend equipment life and stabilise production.
We spoke with CEO Tiziana Tronci about how the system was developed, what results it is delivering, and how predictive intelligence is reshaping the foundry floor.
MTN: What is the name of your new AI software and what specific problem in the die casting industry does it solve?
Tiziana Tronci:
Our artificial intelligence-based software is called Perpetuo. We chose this name because the basic concept is that the software works to extend the life cycle of equipment and maintain the continuity of the production process.
It was created to address one of the most critical problems in die casting and the manufacturing sector: the difficulty of predicting failures, process anomalies, and performance drops before they result in machine downtime or production instability. Perpetuo transforms data from machines and equipment into predictive indicators, helping foundries move from reactive to predictive and data-driven maintenance.
MTN: How did the concept for this AI solution originate inside Gefond, and what were the first steps in turning the idea into a product?
Tiziana Tronci:
The idea came about almost by chance. Some time ago, I heard about predictive maintenance for the first time. Driven by the curiosity that has always guided my professional career, I decided to explore the topic further.
Through continuous research and discussions with Gefond technicians, a very clear picture emerged: most of our customers only intervened when a fault occurred, in emergency situations, often under considerable pressure due to machine downtime and production stoppages. Spare parts were not always available in stock and were subject to technical procurement times. I realised that operating in this way makes it extremely difficult to optimise work and be truly efficient.
This led to the question: are there tools that can help customers reduce breakdown interventions? Looking at other industrial sectors, I realised that ours also needed to evolve, moving from a reactive approach to a preventive and predictive one. From our direct experience in foundries, we realised the data was already present in the equipment, but it was not being used in a structured way.
The first step was to collect reliable real-time data from production machines, analyse it together with foundry technicians, and develop models capable of recognising abnormal patterns. Only after this field phase did the project become a structured software product.
MTN: Can you describe the core technologies powering the software and how they compare to existing solutions on the market?
Tiziana Tronci:
Perpetuo uses machine learning algorithms and advanced data analysis to identify anomalies in equipment behaviour. Unlike many traditional systems, it does not simply display historical data, but is designed to interpret it predictively, progressively adapting to the specific behaviour of each piece of equipment.
The system transforms data collected from sensors or machine PLCs into meaningful information for predictive maintenance of mechanical, electrical, hydraulic, and pneumatic parts subject to wear or failure.
Our predictive maintenance software represents a unique approach: it was developed based on in-depth knowledge of production processes rather than theoretical models alone. This bottom-up approach allows us to speak the language of operators and maintenance technicians, offering concrete and technically effective solutions.
MTN: At Euroguss you officially launched the software. What was the reaction from attendees and early adopters?
Tiziana Tronci:
At Euroguss, we saw a lot of interest, especially from foundries that are looking for concrete tools to improve reliability and production continuity.
At a time when profit margins are falling, being able to monitor energy, compressed air, and release agent consumption in real time can drastically reduce hidden costs. Many visitors appreciated the practical approach: a solution born from real process experience.
MTN: How does this AI software integrate with existing production systems used by die casting manufacturers?
Tiziana Tronci:
Perpetuo is designed to integrate with machines, PLCs, sensors, software, and ERP systems without requiring infrastructure replacement. It connects to existing production data and organises it in a structured way, making it usable for predictive purposes.
MTN: What measurable results have pilot customers reported so far?
Tiziana Tronci:
To date, we have connected over 150 pieces of equipment to Perpetuo, with concrete results in terms of reduced unscheduled downtime, greater production stability, and improved maintenance planning.
In pilot projects and early industrial installations, we have seen measurable benefits typical of predictive maintenance, including:
- 35% reduction in unscheduled downtime
- 15% extension of equipment life
- 16% increase in production
- 20% energy savings
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In one case study, predictive monitoring of hydraulic parameters reduced pipe replacement interventions by 83%, oil leak interventions by 63%, and filter or heat exchanger replacements by 86%. The improvements resulted in a total saving of 248 hours of production time.
MTN: What role does data play in the performance of the AI, and what support do customers need to get started?
Tiziana Tronci:
Data is the foundation of the system. The more complete and reliable it is, the more accurate the model becomes.
Implementation follows a structured four-phase process: analysis of the customer’s systems and objectives, hardware integration when sensors are required, software configuration and model training, and finally the predictive phase where the system generates operational recommendations.
Throughout the process, Gefond also supports customers with training activities, because technology only becomes effective when people know how to interpret the data and use it in their daily decisions.
MTN: What product enhancements or new modules are on your roadmap?
Tiziana Tronci:
We are working on new features that increasingly integrate Perpetuo with other Gefond systems. This has led to the creation of the Perpetuo digital ecosystem, designed to bring foundries into a new era where every machine communicates and every decision is guided by predictive intelligence.
In addition to Perpetuo, we have developed Die.Tective, which monitors die operating parameters, and Foundry.Focus, which supervises and optimises production processes. Together, these tools make the factory more efficient, sustainable, and connected.
MTN: How does this new software fit into Gefond’s broader strategy for growth and innovation?
Tiziana Tronci:
Gefond combines two aspects in this journey: a vision of the future of cutting-edge industry and almost thirty years of experience and expertise in foundry work, both from a commercial point of view in the sale of plants and in technical assistance, installation, and spare parts. The combination of this forward-looking entrepreneurial vision and our experience is leading to change, to the transformation of a business model that shifts the focus to customer and market needs. A range of product-related services is therefore added to the sale of machinery.
Our perception today is that customers need solutions in addition to the purchase of the physical asset. We have transformed this request into training, remote technical assistance with augmented reality, and Perpetuo software. The goal is to connect as many machines as possible. With a self-learning process based on artificial intelligence algorithms, the system is able to become increasingly reliable by connecting systems and recording and interconnecting countless data points.
Furthermore, Gefond’s neutral position in the market and careful management of data privacy has allowed us to build important technical and commercial partnerships with manufacturers of die-casting machines and peripherals that are competitors with each other. More and more manufacturers are showing interest in predictive maintenance because they understand that by joining the Perpetuo system, they can optimize customer service and gain a better understanding of how their systems work. With Perpetuo software, we are demonstrating that today, the difference in terms of competitiveness is achieved by leveraging the predictive power of data.
This approach, which originated in the world of die casting, is now spreading to manufacturing in a broader sense. We are applying the same logic of data collection and analysis, monitoring, and predictive maintenance to other industrial contexts, including those related to mechanical processing, a growth strategy that combines vertical specialization and openness to new sectors.
MTN: As CEO, what do you think is the biggest misconception about AI in manufacturing?
Tiziana Tronci:
The biggest misconception is to think that AI is a plug-and-play “magic box.” In manufacturing, value only comes when algorithms are linked to real factory conditions, clean data, and clear workflows.
Perpetuo addresses this by transforming AI into a practical tool. It structures data, provides interpretable reports, and gives priority recommendations that maintenance and production teams can act on immediately.
MTN Analysis: Predictive Intelligence Becomes a Competitive Lever
Gefond’s Perpetuo platform reflects a wider shift across the metal manufacturing sector. Rather than focusing on fully autonomous factories, many companies are adopting AI through targeted, high-impact applications such as predictive maintenance and energy optimisation. The Gefond Perpetuo AI predictive maintenance 2026 release highlights how predictive intelligence is becoming a practical competitive tool.
Three signals stand out:
- Predictive maintenance is becoming the primary entry point for AI in heavy industry.
- Integration with existing equipment is essential for adoption.
- Energy efficiency is now a central part of the AI value proposition.
Under Tiziana Tronci’s leadership, the company is combining digitalisation with sustainability. After joining the family business in 2016, she led new product development and later founded the HPDC startup to improve energy efficiency in die casting.
That combination of experience and strategy is shaping a new model for foundry technology, where predictive intelligence is positioned as a practical tool for improving uptime, reducing energy consumption, and stabilising production.
FAQ: Gefond Perpetuo Predictive Maintenance Platform
What is the Perpetuo platform from Gefond?
Perpetuo is an AI-based predictive maintenance platform developed by Gefond for die casting and foundry operations. It analyses machine data to detect anomalies, predict failures, and help manufacturers avoid unplanned downtime.
What problem does Perpetuo solve in die casting?
The platform addresses one of the main challenges in die casting: unexpected machine failures and production instability. By predicting issues before they occur, it allows foundries to move from reactive maintenance to a more stable, data-driven approach.
How does Perpetuo collect and analyse data?
Perpetuo connects to existing machines, sensors, PLCs, and ERP systems. It gathers real-time production data and uses machine learning algorithms to identify abnormal patterns that indicate wear, faults, or process inefficiencies.
Does the system require new equipment or major infrastructure changes?
No. Perpetuo is designed to integrate with existing production equipment and control systems. It uses the data already available on the shop floor, which allows foundries to adopt predictive maintenance without replacing machines.
What results have early users reported?
Pilot installations and early deployments have shown measurable improvements, including:
- 35% reduction in unscheduled downtime
- 15% extension of equipment life
- 16% increase in production
- 20% energy savings
In one case, predictive monitoring significantly reduced hydraulic and maintenance interventions, saving 248 hours of production time.
What is required to implement Perpetuo in a foundry?
Implementation typically follows four stages:
- Analysis of the customer’s systems and objectives
- Hardware integration if additional sensors are needed
- Software configuration and model training
- Predictive phase with operational recommendations
Gefond also provides training to help teams interpret and act on the data.
Is Perpetuo limited to die casting applications?
The platform was developed for die casting, but the same predictive maintenance approach can be applied to other manufacturing environments, including mechanical processing and metalworking.
What other digital tools are part of the Perpetuo ecosystem?
Gefond is building a broader digital ecosystem around the platform, including:
- Die.Tective for monitoring die parameters
- Foundry.Focus for supervising and optimising production processes
Together, these tools aim to improve efficiency, sustainability, and connectivity across the foundry.
