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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Charging Point Location Planning Tool

We have final developments from the innovation project eCharge4Drivers: we received feedback during the final project meeting in Barcelona and it looks like our electric vehicle charging location planning tool has been useful and generated positive outcomes through its testing sites in Barcelona (with partner B:SM), Luxembourg and rural northern Italy -part of Trans-European Transport Network (TEN-T) corridor-.

The EV Charging Location Planning Tool includes socio-demographic data, mobility flows, and charging session data from existing charging stations to predict future needs for charging points, both slow and fast, according to scenarios that include the anticipated adoption of electric vehicles. The tool was presented to target group users, mainly public authorities interested in the effective and efficient placement of charging points. Their feedback has been positive, especially as they seek to determine which sites to prioritise first and where to deploy additional chargers.

On another hand, users have seen different benefits from the tool’s demos.

  1. It facilitates informed decision-making by allowing users to make data-backed decisions when planning charging infrastructure, which is a clear improvement over the traditional, intuition-based methods.
  2. The tool also ensures efficiency in resource allocation by focusing on the most promising locations for new charging points and estimating usage rates and profitability.
  3. It also enhances EV driver satisfaction by increasing the availability of charging points in the most needed areas.
  4. Finally, the tool supports long-term planning by simulating scenarios for three to five years, providing confidence for future developments.

Nevertheless, conclusions during the final project meeting highlighted recommendations for policymakers and investors to guide future charging efforts and investments. Project partner experiences and a European survey of public authorities and operators pointed out:

  • the need for tailored design guidance,
  • improved grid connections,
  • streamlined planning processes
  • the importance of interoperability,
  • user-friendly interfaces,
  • and political support to maximise the impact and accessibility of innovative EV solutions.

Moving forward, following the end of the project we expect to reuse and possibly scale the product concept. Our scalability and exploitation efforts for this tool will focus on:

  1. Business and Market analysis to assess fit to other locations and organisations in Europe in the EV ecosystem.
  2. Contact and implementation plans to reuse the Location Planning Tool concept at R+D level in the identified locations and organisations, in collaboration with partners of the model development (VUB and UNIMORE).
  3. Result gathering to assess validation of the European rollout plan in the pre-identified locations and organisations and planning any future scaling to other locations outside the EU.
  4. Assessment of different use cases where the product could be scaled in markets outside the EV environment where the applicability is strong (for instance, hydrogen vehicles).

The simulation platform

During the project, we first performed an analysis of EV drivers’ needs related to vehicle charging which resulted in the solution we designed and integrated: the Location Planning Tool.

This tool has been used and validated in three types of areas while running the project:

  1. A small village in a rural environment without EVs (Val Trompia, in northern Italy).
  2. A city, Barcelona. A company has validated the tool: B:SM (Barcelona, Municipal Services company).
  3. One country, Luxembourg.

This three-fold validation of the tool has proven valuable to illustrate the potential and capabilities of our approach of combining Big data analytics using real-time usage to enable public administrations and private companies to plan future charging infrastructure deployment in the right locations.

→ Check our Predictive Models solution