Trustworthy AI

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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Synthetic Data

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

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Descriptive Models

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

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

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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Digital Twins

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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Data Enhanced Products

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

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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Predictive Maintenance

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

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

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

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

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

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

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Pattern Exploration

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

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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

B:SM Tram Parquímetre

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

Healthcare

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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Projects

COREALIS

Multimodal inland planner

Client

Funded by H2020 European Commission

Partner

The problem

Corealis is a strategic framework supported by disruptive technologies, including IoT, data analytics, next generation traffic management and emerging 5G networks for cargo ports.

Predicting cargo operations contributes to the planning and control in port terminals and increases reliability and resiliency of port operations in an ecosystem with high uncertainties.

The solution

We developed a solution for the Port of Antwerp Burges to optimise port connections and container routes saving time, costs and CO2 emissions.

We developed an ad hoc algorithm to predict container flows en route to a specific place and time, as well as type of transport they are using.

Specifically with the port of Antwerp, we developed:

  • A tool to generate and calculate multimodal routes (train, barge and truck) for the transport of containers from PoA to any point in Europe. To do so, the information of connections between PoA and inland terminals was used based on the data of the actual offer of the port’s transport operators. The tool aims to give Freight forwarders visibility of the possibilities of transport beyond the “easy” but not efficient option of the truck. The tool gave estimated cost of time, money and CO2 consumption.
  • A comprehensive study of how to optimise container transport based on historical information on container arrivals. Here the difficulty was to combine this information with that of the destinations of the containers since the destination was not known. For this reason, we combined real data with statistical information from Eurostat, as well as other sources such as river transport data, which was a little more complete.

Data

  • Overview of the most efficient connections from the port to its hinterland by rail, barge or truck.
  • Optimal door-to-door container routes between two points, determining the optimal in terms of estimated DURATION, PRICE and CO2 EMISSIONS
  • Data sources:

Results

The innovations were implemented and tested in real operating conditions in 5 Living Labs: Piraeus port (GR), Valencia port (SP), Antwerp port (HOL), Livorno port (ITA) and Haminakotka port (FIN).

Do you have any questions?

We are always ready to help you and answer your questions. 





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