NEWS

Milan, July 30, 2025

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Equipment-as-a-Service (EaaS) contracts represent a fundamental transformation in the contemporary industrial landscape, evolving from the traditional "produce-sell" model to a Service-based paradigm. While in the traditional model, machinery manufacturers simply sell equipment by completely transferring ownership and all operational risks to the customer, EaaS introduces a radically different approach: the supplier maintains ownership of the equipment and assumes responsibility for its performance, offering the Customer the use of machinery through Service Contracts that guarantee availability, efficiency, and operational results.

 

The EaaS market offers companies the opportunity to access advanced machinery and technologies without significant initial investments, transforming fixed costs into variable ones and ensuring greater operational flexibility and cost predictability in a period characterized by high economic volatility and uncertainty in industrial markets. This contractual model transfers operational risk from the Customer to the Service Provider, requiring accurate predictive capability of future operational costs and events that may influence machinery performance to ensure the economic success of the operation

 

The Critical Issues of Traditional Pricing

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Conventional pricing and risk management models for EaaS contracts are necessarily based on limited historical data, generating approximate estimates and conservative assumptions that result in several critical problems.

 

Contract overpricing emerges as a direct consequence of uncertainty: Original Equipment Manufacturers (OEMs) apply high safety margins that compromise the competitiveness of market offers. Simultaneously, risk underestimation resulting from insufficient understanding of operational scenarios can lead to economically unsustainable contracts for the service provider.

 

The traditional approach also imposes reactive management of critical events, as the inability to anticipate problematic situations forces emergency interventions characterized by high costs and operational ineffectiveness. Finally, financial institutions experience difficulties in credit access for these contracts, struggling to accurately assess the risk associated with unreliable forecasts.

 

The Importance of Accurate Forecasting

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Developing effective pricing models for EaaS contracts requires precise forecasting across multiple operational dimensions. Operational events include the frequency and type of failures, component wear, and programmed maintenance needs. Intervention costs encompass labor, spare parts, machine downtime, and logistical aspects.

 

Performance variations over time concern energy efficiency, productivity, and output quality, while stress scenarios analyze machinery behavior under extreme or non-standard operating conditions.

 

The Competitive Advantage of Synthetic Datasets

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A distinctive element of contemporary technological innovation concerns the quality and consistency of statistical samples obtainable through synthetic data, which overcome the structural limitations of data from physical machines.

 

Real operational data present privacy constraints that limit sharing and utilization, limited volumes that require prolonged time and high costs for collection, sample incompleteness due to underrepresentation of critical operational scenarios that occur rarely, and temporal bias that might not reflect future operating conditions.

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The generation of synthetic datasets revolutionizes this paradigm by enabling the processing of large volumes of data completely free from privacy constraints and generatable according to specific needs. This approach ensures complete representativeness through exhaustive coverage of all possible operational scenarios, including rare or extreme events.

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Statistical balancing eliminates bias in predictive models through optimal equilibrium between different types of events, while unlimited scalability allows the generation of datasets of any size necessary for algorithm training. Quality control ensures that every synthetic datum is generated according to controlled parameters, eliminating errors and inconsistencies typical of real data.

 

The Zerovis Architecture: An Advanced Technological Solution

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To address these challenges, the Zerovis architecture (acronym for Absolute Zero Virtual Industrial System) has been developed, which together with the trademark are subject to European patent and trademark filing and integrate the already patented methodology of PARADIGMIX™ Srl for industrial servitization. The system is designed specifically to provide credible forecasts through three integrated components. The system is based on the principles of the Absolute Zero Reasoner, an advanced reasoning approach described in the scientific research "Absolute Zero-Shot Reasoning for Structural Relational Reasoning via Compositional Patterns" conducted by Tsinghua University, Beijing Institute for General Artificial Intelligence, and Pennsylvania State University (authors: Chenyang Si, Zhengchu Shen, Zhiyuan Liu, Sheng Wang, available at https://arxiv.org/pdf/2505.03335).

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1. Completely Virtual Digital Twin

The first componentis the Completely Virtual Digital Twin, which constitutes a simulation system that replicates machinery behavior without requiring complex physical infrastructures, allowing the generation of diversified operational scenarios at contained costs. The system applies the principles of the Absolute Zero Reasoner to process compositional patterns that enable reasoning about scenarios never observed before, using synthetic data generated from models calibrated with real IoT data.

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2. Severity Modeler and Classifier

The second component is the Severity Modeler and Classifier, which represents an advanced algorithm that, following the Absolute Zero Reasoner approach, analyzes and classifies operational events according to severity levels through structural relational reasoning. This component quantifies the potential impact on costs and performance for each type of event, even in the absence of specific historical precedents.

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3. Cost Forecasting Engine

The third component is the Cost Forecasting Engine, which constitutes a predictive system that, based on large-scale synthetic datasets and event classification processed through compositional patterns, produces accurate estimates of future operational costs with different time horizons, applying "zero-shot" reasoning capabilities for unprecedented scenarios.

 

Protection of Technological Innovation

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The integration of Zerovis into the patented methodology of Paradigmix™ represents a significant qualitative leap, making the overall approach more robust and performant. The synergy between existing Paradigmix™ technologies and the Zerovis AI-based architecture has demonstrated preliminary innovative value so relevant that a specific European patent application nr. 25425034.3 was filed on July 29, 2025.

 

This intellectual protection formally recognizes the uniqueness of the integrated approach that combines consolidated methodologies for industrial servitization, the Zerovis architecture for synthetic data generation through the Absolute Zero Reasoner stack, and pricing algorithms with risk management systems using the patented methodology of Paradigmix™.

 

The patent filing highlights the technological maturity achieved and the strategic relevance of the solution in the Industry X.0 landscape, consolidating Paradigmix™'s competitive position in the advanced industrial services market.

 

Impact on Pricing Effectiveness

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The integrated and patented architecture, supported by the theoretical principles of the Absolute Zero Reasoner and the statistical superiority of synthetic datasets, enables substantial improvement in pricing process effectiveness.

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Uncertainty reduction is achieved through forecasts based on large-scale synthetic datasets and compositional reasoning that mimics the human cognitive learning process, significantly reducing the margin of error. Dynamic pricing allows prices to be adapted based on specific operational conditions and personalized risk profiles, even for scenarios that have never occurred previously.

 

Margin optimization eliminates surcharges due to uncertainty, maintaining adequate margins to cover actual risks calculated on statistically robust samples. Transparency toward the financial market is guaranteed by predictive models based on complete and scientifically founded synthetic datasets that facilitate the evaluation and financing of EaaS contracts.

 

Competitive protection through patent protection ensures sustainable technological advantage over time, consolidating the company's technological leadership position.

 

Conclusions

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The Zerovis architecture transforms pricing from a process based on conservative estimates and limited data to a scientific methodology supported by simulated evidence and advanced reasoning. This evolution enables the sustainable development of the Equipment-as-a-Service market through the statistical superiority of synthetic data and the robustness of the patented integration, representing an innovative paradigm for the manufacturing industry of the future.