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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Optimisation Models

Optimisation AI models allow our client to improve processes, reduce costs and increase competitiveness.

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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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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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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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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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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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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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TwinLoop HE project kicks off in Barcelona

Mosaic Factor team hosted the Kick-off Meeting for TwinLoop project on January 21st and 22nd in Barcelona. We are thrilled to present this Horizon Europe project as coordinators. TwinLoop is an innovation project that will develop an Open Framework for TwinOps and Digital Twin for Electrical Vehicles. Its ultimate objective is to contribute to reduce EV´s Energy Consumption. During this 2-day event at Mosaic Factor, together with the key partners, we have focused on the roadmap, the challenges, and the objectives for the upcoming months.

Co-creation interactive workshop

During the meetings, we organised a World Café: an interactive co-creation workshop to better define core technical tasks by defining, mapping, and discussing:

  1. Recent initiatives in the topic.
  2. What are the main challenges (difficulties by importance).
  3. Which of the project’s key results is more relevant for exploitation purposes.

Innovation in Digital Twins for SDV

TwinLoop builds on a new opportunity in cloud-computation capacity due to the implementation of High-Performance Computing combined with digitisation of EVs under the SDV architecture. Our main goals are to enhance EV driver experience, safety as well as cybersecurity. Current Digital Twin state of the art is far from the complexity of EVs core performance. Any vehicle is unique and has its own hardware and software version. By considering the unicity of a single vehicle and learning from the operational data of a vehicle fleet and the use of data and digital models across all EV’s lifecycle, it would be possible to unlock the necessary extra step to reduce energy consumption without compromising comfort and safety. The TwinLoop will develop an Open Framework for TwinOps for EVs along with a suite of digital tools to constantly improve the following elements across the four stages of the vehicle lifecycle:

  • Energy Consumption
  • Hardware Costs
  • Driver Experience
  • Vehicle Resiliency

The project will implement the Open Framework, integrate with EV-specific tools, and assess their effectiveness in realistic conditions across three different use cases, identified for their relevance to the topic. We will also focus on fostering synergies between various sectors and stakeholders, aligning with European priorities and strategic partnerships like 2ZERO and Chips JU. This ensures the transferability of expected outcomes and underscores TwinLoop’s commitment to innovation management, research ambition, and market acceptance within the automotive industry.

Planning the HE project

Located in the beautiful “Barcelona Room” from Barcelona City Council Mobility buildings, the project consortium addressed several topics to kick off the TwinLoop project:

  • Software-defined EV trends and project requirements
  • Efficient and effective EV development
  • Open Framework for TwinOps and Digital Tools for EVs
  • Driver profiling, Privacy and Cyber Security
  • Use Cases and evaluation
  • Project Overview & Planning
  • Presentation by European Commission
  • Policy Expectations
  • Project Management & Administrative Topics

We will keep you posted on new developments from this exciting innovation project! → Check our Digital Twins solution