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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H2020 inclusive Transport project

Client

Funded by H2020 European Commission

Partners

The problem

INCLUSION aims to understandassess, and evaluate the accessibility and inclusiveness of transport solutions in European prioritised areas to identify gaps and unmet needs.

We did a use case together with BusUp and the Canet Rock.

    • Objective: to facilitate accessibility to the concert by bus transport. BusUp was already serving the concert but wanted to know possible new transport lines to be set up for the concert as well as possible new intermediate stops.
    • Target: occasional group or travellers (particularly young people) moving as individuals or in small groups, travelling to common destinations (i.e. music festival)
    • When there is limited Public Transport, target user groups tend to either take their own car (if possible) or not attend the event due to difficulties in transport.

The solution

Mosaic Factor coordinated the Barcelona Pilot Lab by targeting groups of young travellers and people travelling to common destinations through Social Media data.

To anticipate potential demand for concert attendees, we combined information from regular transport lines (to know areas that are poorly served at night) with socioeconomic information (population, income and electoral results).

With the process, we proposed several new locations, some of which were transformed into new line proposals.

By identifying prioritised areas, user groups and needs the project delivered a new accessible and inclusive mobility solutions and business models using Big Data.

Data

    • Open Data
    • Demographic data
    • Public Transport data
    • Social Media profiles
    • Vulnerable target users:
      • Safety risk for the attendees
      • 64% attendees under 24 y.o.
      • 69% attendees are female
    • Vulnerable target areas:
      • Limited Public Transport accessibility.
      • Inflexible, infrequent during night-time.
      • Operated on a radial routes’ structures linking peripheries and Barcelona

Results

Reducing territorial accessibility barriers to attend cultural events located in peri-urban areas of the Barcelona Metropolitan Region, due to poor or inflexible transport offer.

Identification of geographical areas with potential demand to attend to the event and to propose the most suitable bus-stop locations for this uncovered demand.

Do you have any questions?

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