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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Driving trustworthy AI in autonomous mobility: insights from ECAVA

On 5th and 6th February in Brussels, the European Connected and Autonomous Vehicle Alliance (ECAVA) brought together close to 100 representatives from European OEMs, suppliers, technology companies, and public institutions (including Mosaic Factor!). The objective was clear: strengthen cooperation and accelerate Europe’s position in connected and autonomous mobility. 

The meeting marked an important early milestone for ECAVA’s working structure. The day began with a road mapping of current European initiatives in autonomous driving, highlighting both the depth of activity already underway and the need for better alignment. With global competition intensifying and “China speed” referenced repeatedly throughout the discussions, participants pointed to the rapid pace at which China is advancing in the tech and autonomous driving space. The shared understanding was that Europe must strengthen coordination and accelerate execution to build its own competitive, high-performance European AI capable of matching that momentum. 

From strategy to software foundations

A key part of the dialogue centred on advancing a European Autonomous Driving software stack. Rather than duplicating efforts, participants explored how shared foundations and clearer governance could support scalable innovation while reinforcing Europe’s technological sovereignty. 

Two priority areas shaped the exchanges: 

  • Technical governance and coordination across initiatives 
  • Structured collaboration on a European AD software stack 

Parallel discussions on Autonomous Driving Deployment and AI and data pooling added practical depth to the conversation. Topics ranged from deployment pathways and cross-border testing to how data sharing and computing capabilities can underpin competitive, trustworthy AI systems. 

Momentum across the ecosystem

The strong turnout signalled growing alignment across the automotive value chain. Industry leaders shared perspectives on deployment models and real-world implementation, sparking thoughtful debate and reinforcing the need to move beyond discussion toward tangible progress. 

To maintain continuity and avoid fragmentation, a dedicated core team will be formed under the leadership of soon-to-be-elected co-chairs. Further structural details are expected at the next Steering Committee meeting in March, reflecting ECAVA’s ambition to remain lean, focused, and outcome-oriented. 

Mosaic Factor’s CEO, Stefano, attended the sessions as part of the broader industry dialogue, contributing to the collective effort shaping Europe’s autonomous driving roadmap. 

The takeaway from Brussels was simple and practical: Europe has the expertise and industrial capacity. The next step is disciplined, coordinated execution. 

Read more about our trustworthy AI solutions.