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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HIDDEN project launch

A groundbreaking EU-funded initiative, HIDDEN (Hybrid Intelligence for Advanced Collective Perception and Decision Making in Complex Urban Environments), officially launched on 8th July in Athens, with a bold mission: to make European cities safer by enabling automated vehicles to detect what they currently cannot: pedestrians, cyclists, and other road users hidden behind obstacles.

Tackling urban blind spots

In busy city environments, parked cars, buildings, and vegetation often obstruct vehicle sensors, creating blind spots that pose serious risks, especially for vulnerable road users (VRUs) like children, cyclists, and road workers. Current detection systems struggle in these scenarios, with recognition rates dropping below 65% when individuals are fully occluded.

HIDDEN aims to overcome this challenge by enhancing Collective Awareness through Vehicle-to-Everything (V2X) communication and Artificial Intelligence. By sharing sensor data between vehicles, infrastructure, and road users, the project enables a more complete and dynamic understanding of the urban environment.

Hybrid Intelligence: a human-machine fusion

What sets HIDDEN apart is its use of Hybrid Intelligence (HI): a fusion of human and machine intelligence. This approach allows automated systems to make decisions that are not only technically sound but also ethically and legally grounded, reflecting human judgment and behavior.

“HIDDEN goes beyond conventional AI,” said Dr. Angelos Amditis, HIDDEN Coordinator and R&D Director at ICCS. “We’re bringing human judgement into the loop -so automated systems can act not just accurately, but wisely.”

Real-world testing

The project will test its approach in four high-risk urban scenarios:

  • A child running from behind a parked car
  • A cyclist navigating mixed-traffic zones
  • A road worker obscured by vegetation
  • A vehicle hidden at an unsignalised intersection

These cases reflect complex, real-world challenges where improved perception and ethically grounded decision-making could be life-saving.

A pan-European collaboration toward safer, smarter cities

Funded by Horizon Europe’s Cluster 5 with a grant of approximately €5 million, HIDDEN is supported by the Connected, Cooperative and Automated Mobility (CCAM) Partnership. The consortium includes 14 partners and 2 affiliated entities across 7 EU countries, bringing together expertise from research institutes, universities, SMEs, automotive leaders, regulatory bodies, and social science researchers.

HIDDEN isn’t just about smarter vehicles—it’s about building trust, aligning technology with human values, and paving the way for safer streets across Europe. Through field tests and virtual simulations, the project will validate its technologies and work closely with EU type approval bodies and UNECE working groups to shape future standards and policies.

Mosaic Factor’s contribution

Mosaic Factor leads the development of Explainable AI (XAI) and Human-Feedback Reinforcement Learning (RLHF) methods within the project. Their work focuses on creating a transparency-first explanatory toolkit that fosters trust, user acceptance, and ethical integration of AI in connected and automated vehicles.

For more details, you can read and download the full press release here:

HIDDEN Press Release _EN_final