Data Enhanced Products

Data Enhanced Products Mosaic Factor icon

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.

View solution

Data As a Service Products

DaaS Mosaic Factor icon

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.

View solution

Optimisation Models

Optimisation Models icon Mosaic Factor

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

View solution

Descriptive Models

Descriptive Models Mosaic Factor icon

Descriptive models aim to describe patterns, relationships, and structures within data. They don’t predict future outcomes but provide insights into existing phenomena.

View solution

Predictive Models

Predictive models Mosaic Factor icon

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.

View solution

LLMs

LLMs Mosaic Factor icon

At Mosaic Factor, we focus on the creation of domain specific LLMs (or light Large Language Models) for our client organisations.

View solution

Synthetic Data

Synthetic Data Mosaic Factor icon

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

View solution

Digital Twins

Digital Twins Mosaic Factor icon

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.

View solution

Predictive Maintenance

Predictive models Mosaic Factor icon

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.

View solution

Demand Cost Forecasting

Predictive models Mosaic Factor icon

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.

View solution

Quality Analytics

Predictive models Mosaic Factor icon

We identify patterns that correlate with defects or quality issues, allowing your business to take corrective actions early and maintain high-quality standards.

View solution

Inventory Management

Predictive models Mosaic Factor icon

We use predictive models to optimise inventory levels by considering factors such as lead time, demand variability, and storage costs.

View solution

Supply Chain Management

Predictive models Mosaic Factor icon

We can use historical and real-time data analytics to manage the supply chain, optimise transportation and ensure on-time product delivery.

View solution

Market Understanding

Descriptive Models Mosaic Factor icon

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

View solution

Pattern Exploration

Descriptive Models Mosaic Factor icon

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

View solution

Trustworthy AI

Trustworthy AI Mosaic Factor icon

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.

View solution

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.

View industry

Automotive

B:SM Tram ParquĂ­metre

Mosaic Factor’s apply AI solutions in various aspects of the automotive industry, usually by enhancing vehicles and its components during its development.

View industry

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.

View industry

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.

View industry

Manufacturing

Manufacturing

Mosaic Factor’s higher priority in Manufacturing is aid our clients decrease costs, increase sustainability while streamlining the production chain.

View industry

Healthcare

Healthcare header

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.

View industry

Webinar series: Explainable AI for decision makers

📅 Tuesday, 26th May, 2026

⏰ 11:00 h – 12:00 h

Artificial intelligence is rapidly transforming every industry, from automotive and healthcare to manufacturing and energy. AI models are increasingly embedded in critical decision‑making processes, influencing operational efficiency, product performance, and strategic direction.

As AI systems become more powerful and complex, one fundamental question moves to the forefront: How can we trust AI models enough to rely on them for real business and engineering decisions?

To launch our new Explainable AI (XAI) webinar series, we are pleased to introduce a cross‑industry online masterclass that lays the foundations for turning AI models into understandable, reliable, and trusted tools, both by technical teams and by decision makers.

Why Explainable AI matters across industries 

High‑performing AI models are no longer sufficient on their own. Across industries, organisations are facing similar challenges:

  • Complex models that deliver predictions but not understanding.
  • Difficulty explaining results to non‑technical stakeholders.
  • Limited visibility into model behavior, assumptions, and limitations.
  • Low trust in AI outputs when decisions carry operational or strategic risk.

Explainable AI provides the tools and frameworks to bridge these gaps. When applied correctly, XAI helps organisations:

  • Understand why a model behaves the way it does.
  • Improve model robustness during development and iteration.
  • Detect hidden issues, biases, or data dependencies early.
  • Communicate AI insights clearly to product owners, managers, and executives.
  • Build shared trust between data teams and decision makers.

Explainability is, therefore, not just a technical feature, it is a critical enabler of reliable AI adoption at scale.

The First Session in a Webinar Series

This session opens a broader webinar series on Explainable AI. While future editions will explore industry‑specific applications, this first masterclass takes a cross‑industry perspective, focusing on the decision makers and common principles that apply regardless of sector.

The objective is to establish a shared language and mindset around XAI that aligns technical development with business decision‑making.

What you will learn 

By the end of this session, participants will understand how to move beyond accuracy alone and design AI systems that are: 

  • Transparent: their behavior can be understood and inspected.
  • Explainable: predictions can be justified in human‑interpretable terms and traced in a way that models can be accountable.
  • Trustworthy: results can be confidently used in real‑world decisions.

During the session, we will cover:

  1. Why Explainable AI Matters to Decision Makers
  2. Explainable AI Foundations, without the Technical overhead
  3. Where Explainable AI creates Business Value
  4. Explainability across AI approaches
  5. Operationalizing XAI in the organisation
  6. Communicating AI Decisions with confidence
  7. Case‑driven walkthrough: Mosaic Factor XAI Workflow

The focus remains firmly on practical decision‑making and model reliability, not abstract theory.
At the end of the session, our Chief Data Scientist, Burcu Kolbay, will host a live Q&A.

Who should attend 

This masterclass is designed for professionals working (or willing to work) with AI across industries, including: 

  • Decision makers who rely on AI outputs (C-level)
  • Product Owners and Technical Managers
  • Innovation and Digital Transformation Leaders

No legal or compliance background is required. The session prioritises applied, development‑focused, and communication‑driven perspectives.

Event Details

📅 Tuesday, 26th May, 2026

⏰ 11:00 h – 12:00 h

  • Format: Online (live)
  • Duration: 60 minutes
  • Level: Basic to Intermediate
  • Language: English 

Registration

Join this first session of our Explainable AI webinar series to ensure your AI solutions are not only powerful but also understandable, reliable, and trusted by those who use them to make decisions.

📹 Click here to register now to secure your place.Â