Data Enhanced Products

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Through different data sources (ie. physical tests) and ML models and usually in combination with our digital twin solutions, our data enhancement solution can learn, predict, and simulate outcomes to provide automatic product configurations that result in product and component improvement during the development process.

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Data As a Service Products

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Data as a Service (DaaS) is a cloud-based model that allows companies to access, manage, and analyse data on demand, without the need for extensive on-premise infrastructure.

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Optimisation Models

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Optimisation AI models allow our client to improve processes, reduce costs and increase competitiveness.

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Descriptive Models

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Descriptive models aim to describe patterns, relationships, and structures within data. They don’t predict future outcomes but provide insights into existing phenomena.

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Predictive Models

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Predictive modelling, also known as predictive analytics, is a discipline that uses statistical, mathematical and artificial intelligence techniques to predict future outcomes based on historical data.

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LLMs

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At Mosaic Factor, we focus on the creation of domain specific LLMs (or light Large Language Models) for our client organisations.

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Synthetic Data

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Synthetic data is artificial data generated from original data using a model trained to reproduce its characteristics and structure.

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Digital Twins

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To allow your business to monitor and optimise your assets in real-time Mosaic Factor uses Digital Twins. They can predict failures, detect inefficiencies, and improve decision-making through the use of data.

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Predictive Maintenance

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For Predictive maintenance models, we use historical and real-time data to anticipate equipment failures or maintenance needs. By analysing sensor data, maintenance logs, and other relevant information, we can schedule maintenance proactively, reduce downtime, and extend the lifespan of your machinery.

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Demand Cost Forecasting

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Our predictive models help businesses forecast demand for products or services. By analysing historical sales data, seasonality, economic factors, and external events we can optimise inventory levels, allocate resources efficiently, and minimise overstock or stockouts.

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Quality Analytics

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We identify patterns that correlate with defects or quality issues, allowing your business to take corrective actions early and maintain high-quality standards.

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Inventory Management

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We use predictive models to optimise inventory levels by considering factors such as lead time, demand variability, and storage costs.

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Supply Chain Management

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We can use historical and real-time data analytics to manage the supply chain, optimise transportation and ensure on-time product delivery.

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Market Understanding

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Our descriptive AI models provide valuable insights for decision-making and understanding complex systems of your organisation.

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Pattern Exploration

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Our descriptive AI models provide valuable insights for decision-making and understanding complex systems of your organisation.

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Trustworthy AI

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When using AI models in environments where compliance standards are important, Mosaic Factor can help your company be on top of data governance by applying trustworthy AI solutions.

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Logistics

Logistics

Mosaic Factor’s higher priority in Logistics is sharing key data across different Supply Chain players to optimise performance while managing sustainability by mitigating the impact of these operations.

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Automotive

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Mosaic Factor’s apply AI solutions in various aspects of the automotive industry, usually by enhancing vehicles and its components during its development.

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Mobility

Mobility

Mosaic Factor’s higher priority in Mobility is to optimise transport systems to people’s mobility while improving overall security and sustainability of transport solutions.

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Corporate Services

Corporate Services

Our machine learning and complex algorithms help organisations manage compliance and customer service to increase the service level of your organization while optimising resolution time for several processes.

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Manufacturing

Manufacturing

Mosaic Factor’s higher priority in Manufacturing is aid our clients decrease costs, increase sustainability while streamlining the production chain.

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Healthcare

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Mosaic Factor’s higher priority in Healthcare is making use of data to improve patient care and monitoring in a safe manner to optimise healthcare systems resources and assisting healthcare professionals.

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Twin-Loop project meeting in Catania

Last month, our team had the honor of hosting the General Assembly Meeting for the Twin-Loop project in the vibrant city of Catania, Sicily. Held at the iconic Museo Diocesano -a place where heritage meets innovation-, the event marked a pivotal moment for the Horizon Europe initiative focused on revolutionising energy efficiency in electric vehicles.

As proud coordinators of Twin-Loop, we are thrilled to lead this groundbreaking effort to develop an Open Framework for TwinOps and Digital Twin technologies tailored to Electric Vehicles. The project’s ultimate goal: to significantly reduce EV energy consumption and pave the way for smarter, more sustainable mobility.

Assembly Highlights

  • Collaborative Breakthroughs. Partners from across Europe came together in dynamic sessions centered on the Advisory Board and Use Cases, aligning on technical strategies and shared goals.
  • Project Progress Review. A thorough evaluation of milestones, deliverables, and future objectives ensured all stakeholders are moving forward with clarity and cohesion.
  • Networking & Synergy. Informal gatherings and structured networking activities fostered new relationships and deepened existing ones, reinforcing the collaborative spirit of Twin-Loop.
  • Hands-On Workshops. Interactive sessions sparked fresh ideas and actionable insights, equipping participants with renewed momentum and a clear roadmap for the next phase.

As Twin-Loop continues to evolve, the energy and engagement from this General Assembly will serve as a powerful catalyst for innovation, impact, and progress toward a more efficient EV future.

Advancing Digital Twin Intelligence for the Future of SDV in electric vehicles

Twin-Loop is pioneering a new generation of Digital Twin innovation by harnessing the expanding capabilities of cloud-native architectures and High-Performance Computing (HPC). As Electric Vehicles transition into fully Software-Defined Vehicles, the project taps into real-time data orchestration and edge-to-cloud integration to redefine how digital replicas interact with physical systems. Our ambition is to elevate the EV experience: enhancing personalisation, improving energy efficiency, and embedded cybersecurity.

Current Digital Twin solutions often overlook the granular complexity of EVs, where each vehicle operates with a distinct blend of hardware, firmware, and software updates. Twin-Loop embraces this individuality, using fleet-wide operational intelligence and lifecycle-wide data fusion to build adaptive models that evolve with each vehicle. This approach unlocks the potential to reduce energy consumption intelligently, without sacrificing performance, comfort, or safety.

Through the development of an Open TwinOps Framework and a modular suite of digital tools, Twin-Loop will enable continuous optimisation across the four critical stages of the EV lifecycle: design, production, operation, and end-of-life.

→ Check our Digital Twins solution