1. Unacceptable risk is prohibited. Therefore, the following types of models should not be used:
   1. a. Subliminal, manipulative, or deceptive AI
   2. b. Systems that exploit vulnerabilities related to age, disability, or socio-economic circumstances to distort behaviour, causing significant harm.
   3. c. Biometric categorisation systems inferring sensitive attributes (race, political opinions, trade union membership, religious or philosophical beliefs, sex life, or sexual orientation), except labelling or filtering of lawfully acquired biometric datasets or when law enforcement categorises biometric data.
   4. d. Social scoring systems.
   5. e. Systems assessing risk of an individual committing criminal offenses.
   6. f. Compiling facial recognition databases from the internet or CCTV footage.
   7. g. Inferring emotions in workplaces or educational institutions.
   8. h. ‘Real-time’ remote biometric identification (RBI) in publicly accessible spaces for law enforcement.
2. High-risk AI systems: they are regulated, and the AI Act focuses mostly on these.
3. Limited risk AI systems: they are subject to lighter transparency obligations. This means, developers and deployers should make sure that end-users are aware that they are interacting with AI (thus making it clear that there is an AI model behind chatbots and deepfakes).
4. Minimal risk AI models are unregulated: these include most AI applications that were available on the EU single market at the moment of starting AI Act in 2021. For instance, AI-enabled video games and spam filters.

Clearly, this scenario is changing with generative AI, which raises the level of risk of AI models, making them mostly high-risk. Even if now AI regulations are advancing, we think AI systems should be overseen by people, rather than by automation, to prevent harmful outcomes. This includes making sure we are able to generate trustworthy AI systems that are fair by design and are explainable and clear to decision makers. Legislation will clearly not fast-track adoption of responsible AI but organisations are the ones who need to share experiences and solutions to show what “good practice” looks like. Boards need to embrace Corporate Digital Responsibility to assess digital impacts of products/services on all stakeholders by examining societal, economic, technological & environmental impacts. Therefore, we are supporting companies in their role to ensure the technology is not deployed in “negative use cases” that could harm society and generating AI models that are transparent and accountable across industries.

→ Check our Trustworthy AI solution

La entrada Key aspects of the european AI act se publicó primero en Mosaic Factor.

⏰ 11:00 h – 12:00 h

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

📨 Click here to register and secure your place today

Artificial intelligence is increasingly supporting healthcare organisations in areas such as diagnostics, medical imaging, patient monitoring, risk prediction, treatment optimisation, and operational planning. As AI becomes more deeply embedded in clinical workflows, healthcare providers face a common challenge: ensuring that AI systems are not only accurate, but also understandable, reliable, and usable in real-world healthcare environments.

Bridging the Gap Between AI Models and Clinical Trust

In the second session of our Explainable AI (XAI) webinar series (you can rewatch the first session, XAI for decision makers here), we will explore how XAI can help bridge the gap between technical model performance and practical clinical adoption.

This session focuses on how to design and communicate AI explanations that support healthcare professionals in understanding, validating, and appropriately using AI-powered insights.

These capabilities are also increasingly relevant to regulatory readiness in Europe, as the EU AI Act places requirements relating to transparency, output interpretation, and effective human oversight on certain high-risk healthcare AI systems. While this is not a regulatory session, the webinar will briefly explore how practical, clinician-centred approaches to XAI can support these expectations.

How can clinicians trust an AI recommendation if they do not understand how it was reached? The reality is that technical accuracy alone rarely drives adoption in healthcare. Even highly performant models may remain unused if healthcare professionals cannot assess the reasoning behind predictions, understand the contributing factors, or determine when an output should be challenged.

Why Explainable AI matters in Healthcare 

Healthcare decisions directly affect patient outcomes. As a result, clinicians must be able to evaluate and understand the recommendations provided by AI systems before incorporating them into their workflows.

One of the most common barriers to AI adoption in healthcare is not model performance, but lack of trust.

Questions frequently raised by clinicians include:

• Why was this patient classified as high risk?
• Which factors contributed most to this prediction?
• Can this recommendation be clinically justified?
• Does the explanation align with established medical knowledge?
• When should the model’s output be challenged or reviewed?

Without meaningful explanations, AI systems can be perceived as black boxes that are difficult to validate in real-world clinical settings.

Explainable AI helps address these concerns by making model reasoning more transparent, allowing clinicians to better understand predictions and integrate AI insights into their decision-making process.

Healthcare organisations are increasingly looking beyond model accuracy when evaluating AI solutions. To create meaningful value in clinical settings, AI systems must also be:

• Transparent enough for practitioners to understand the factors behind predictions and recommendations
• Reliable and robust across diverse patient populations and clinical scenarios
• Aligned with clinical reasoning and established healthcare practices
• Capable of supporting collaboration between data scientists, healthcare professionals, and decision makers
• Trusted by end users who are ultimately responsible for patient care

While lack of trust is often cited as a barrier to AI adoption, it is rarely the only challenge. Effective AI deployment also requires clear communication, interpretability, and a shared understanding of model behaviour across both technical and clinical teams.

Explainable AI provides the tools and methodologies needed to address these challenges and transform AI from a technical output into a practical decision-support capability.

📨 Click here to register and secure your place today

Making Explainability understandable to clinicians

Producing explanations is only the first step.

Many XAI techniques generate outputs that are meaningful to data scientists but difficult for healthcare professionals to interpret within the context of patient care. For AI to become truly useful in healthcare, explanations must be translated into forms that align with clinical thinking and decision-making processes.

This session will explore how do we translate AI explanations into clinically meaningful insights?:

• What clinicians actually need from AI explanations
• How different healthcare stakeholders interpret model outputs
• Techniques for presenting model reasoning in clinically meaningful ways
• How XAI can strengthen collaboration between healthcare experts and AI development teams

The objective is not simply to explain the model, but to ensure that explanations support informed clinical judgement. Many organizations successfully implement sophisticated XAI techniques, yet the resulting visualizations, metrics, and technical outputs are often designed for data scientists rather than healthcare professionals.

Participants will learn how to move from technical explanations to explanations that support clinical judgment and decision-making.

What you will learn 

During this session, participants will gain practical insights into:

• Why trust remains one of the biggest barriers to healthcare AI adoption
• The role of Explainable AI in supporting clinical decision-making
• Translating technical explanations into clinician-friendly insights, the difference between explaining a model and communicating an explanation effectively
• Designing transparent explanations for different healthcare stakeholders that fit within clinical workflows
• How XAI can support transparency and human oversight for high-risk healthcare AI under the EU AI Act
• Best practices for human-centred approaches to AI adoption in healthcare

The focus is firmly on creating AI systems that healthcare professionals can understand, evaluate, and confidently use in practice.

By the end of this session, participants will understand how Explainable AI can help create healthcare AI systems that are:

• Transparent – clinicians can understand the factors driving predictions
• Interpretable – explanations are presented in clinically meaningful ways
• Trustworthy – healthcare professionals can confidently evaluate and use AI outputs
• Adoptable – AI solutions fit naturally into clinical workflows

The emphasis will be on making explainability useful, understandable, and actionable for healthcare professionals.

At the end of the session, our Chief Data Scientist, Burcu Kolbay, will host a live Q&A.

Special guest speaker: joining our Chief Data Scientist will be Dr. Josep Vidal, a member of the multidisciplinary and multi-territorial AI projects working group at the Institut Català de la Salut (ICS). Dr. Vidal will share practical perspectives on the adoption of AI in healthcare environments, the importance of explainability for clinical decision-making, and the challenges of translating AI insights into information that healthcare professionals can confidently use in their daily practice.

Who should attend 

This masterclass is designed for professionals involved in the development, deployment, evaluation, or adoption of AI in healthcare, including:

• Clinical Informatics Specialists
• Digital Health Leaders
• Medical Device and HealthTech Professionals
• Physicians and Clinical Researchers
• Hospital Innovation Teams
• Healthcare Decision Makers

No advanced technical background is required. The session is designed to foster dialogue between technical and clinical communities.

Event Details

📅 Wednesday, 7th October, 2026

⏰ 11:00 h – 12:00 h

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

La entrada Webinar series: Explainable AI in Healthcare se publicó primero en Mosaic Factor.

Today, AI is everywhere. It writes, creates, converses, and imagines. Terms like generative AI and large language models have entered everyday conversations, reshaping how we think about technology and its role in our lives.

As we celebrate our 10th anniversary, we are not just marking time. We are reflecting on how far AI has come and how we have grown alongside it.

The beginning

Mosaic Factor started with a simple but ambitious idea: to turn advanced AI into something practical, usable, and impactful.

Founded in 2016 by Stefano Persi (CEO) and José Fernandez (CTO), the company emerged from hands-on work in innovation projects. They saw an opportunity not just to build algorithms, but to help organisations truly use them.

In those early days, the focus was on “traditional AI“: predictive models, optimisation systems, and data‑driven decision tools. The challenge was not just accuracy. It was making these systems reliable, scalable, and understandable in real‑world environments. Which still is, to be fair.

From the very beginning, Mosaic Factor took a different approach: not just building AI, but building AI that is explainable and trustworthy.

A decade of change in AI

Over the past ten years, the field of AI has gone through a transformation few could have predicted:

1. We have moved from structured data and classic machine learning to systems that can generate text, images, analysis, and ideas.
2. From models that answered questions to models that hold conversations.
3. From tools once used by specialists to platforms used by millions.
4. The rise of generative AI and large language models has brought extraordinary possibilities, alongside new questions. How do we trust these systems? How do we use them responsibly?

Through all this, Mosaic Factor has remained present across both worlds. We have worked with traditional AI, where precision, interpretability, and robustness are essential. And we have embraced modern AI, where creativity, adaptability, and scale redefine what is possible.

One thing has never changed: our commitment to building AI that is explainable, trustworthy, and grounded in real impact. Because as AI becomes more powerful, trust matters more than ever.

The people behind the journey

At the heart of our 10‑year journey is something simple: people.

The team at Mosaic Factor makes everything possible, bringing creativity, expertise, and passion to every project. Here are leadership reflections for this milestone:

CEO, Stefano Persi:

“When we started Mosaic Factor, our mission was clear: make AI truly work for people. Ten years later, I am proud that our purpose remains the same and that our team continues to push the boundaries of what responsible innovation looks like”.

CTO, José Fernandez:

“Technology evolves fast, but trust must move even faster. Our commitment to transparent, explainable AI has guided us from day one, and it will continue to shape how we build the next generation of intelligent systems”.

Chief Data Scientist, Burcu Kolbay:

“Over the past decade, data has become central to how we understand problems and make decisions. It’s been inspiring to see curiosity, data‑driven thinking, and advances in AI shape how we work every day. As these technologies evolve, our role is to guide them so intelligence serves people in meaningful and reliable ways and I’m excited for what comes next”.

Innovation Project Manager, Montserrat Anglès:

“Innovation is not just about adopting new technologies. It is about using them responsibly to improve people’s lives, protect our environment, and contribute to a more sustainable future for all living beings. At Mosaic Factor, we have spent a decade transforming innovative ideas into practical solutions, working across borders and disciplines to create meaningful impact. That commitment to collaboration, sustainability, and positive change continues to drive us forward.”.

A special thank you goes to our team members and everyone who has contributed to our story. This milestone belongs to all of you. We are equally grateful to our clients, partners, and community who have supported us every step of the way. You have shaped who we are, and who we aspire to become.

Looking ahead

If the last decade has taught us anything, it is that change is constant, but purpose matters.
AI will continue to evolve, becoming faster, more capable, and more deeply embedded in everyday life. The challenge will not just be what we can build, but how we choose to build it.
At Mosaic Factor, our goal is clear:

1. To design AI systems people can trust, understand, and rely on.
2. To bridge innovation with responsibility.
3. To grow into one of Europe’s leading AI and Big Data companies while staying true to the impact we want to create.

The story of AI is still being written. And we are proud to be part of it.

La entrada Mosaic Factor turns 10 se publicó primero en Mosaic Factor.

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.

Recording

If you couldn’t 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, you can now watch the recording:

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 

La entrada Webinar series: Explainable AI for decision makers se publicó primero en Mosaic Factor.

This moment marks more than just a presentation: it reflects our next step as a company. We have joined the AI Factory incubation program with a clear objective: to expand our work into the healthcare sector.

Over the next six months, we will contribute within the AI Factory, where we will have access to office space, technical resources, and a strong network within the Tech Barcelona ecosystem. One of the key reasons for joining is the opportunity to engage with the healthcare cluster and better understand the real-world challenges in this space.

Our pitch at BSC AI Factory incubator

The event gave us an opportunity to share what we are building, exchange ideas with other teams, and engage with the local AI community. Our team was represented by Stefano Persi, our CEO, José Fernández (CTO), Andrea Santiago (Financial Controller) and Joan Sampablo, our Head of Sales.

In our pitch, we focused on a core idea that drives our work at Mosaic Factor: AI should not only perform well in controlled environments, it needs to work reliably in real-world conditions where constraints are unavoidable.

The problem we are solving

Across industries such as automotive and manufacturing, AI failures can lead to serious consequences:

• safety risks,
• financial losses,
• and privacy issues.

These everyday risks are just as relevant in healthcare. By focusing on reliability and practical solutions, our approach is designed to help organizations navigate these challenges and deliver trustworthy AI solutions.

From Cloud to Real Environments

A big part of our work focuses on closing the gap between AI in the cloud and AI in production environments. In many cases, AI models are developed in powerful cloud environments but need to run in places where resources are limited such as: vehicles, embedded systems, or microchips. In these environments, compute power and energy usage are constrained, but performance still needs to remain high.

We focus on optimising AI systems so they can operate efficiently under these conditions without losing reliability.

How We Work

At Mosaic Factor, we both create AI solutions to solve specific client problems and enhance existing AI systems to ensure they perform reliably. This dual approach lets us move quickly into new sectors like healthcare, where understanding the challenge and maintaining dependable performance are equally critical.

Looking ahead

Joining the AI Factory programme is an important step in our move into healthcare. Through this programme, we aim to connect with partners, explore real use cases, and better understand the specific challenges of the sector.

Our goal is to apply what we have learned in other industries to build AI solutions that a practical, reliable, and ready for real-world healthcare environments. Presenting at the event was our first step in that direction.

La entrada Mosaic Factor joins BSC AI Factory se publicó primero en Mosaic Factor.

The BYOD app transforms everyday courier operations into a fully connected, data-driven workflow. From parcel validation to proof of delivery and disruption reporting, every action is securely recorded and transmitted to the central platform, ensuring logistics providers remain fully informed. 

When a courier logs in, the application automatically adapts to the configuration of the specific Living Lab deployment. The available features and workflows depend on the operational model of each environment. The BYOD app is designed to support different city deployments with tailored configurations without requiring changes to the core application. 

In the Athens Living Lab, for example, couriers can operate through either Parcels or Stops within the main menu. This flexibility allows the same application to support multiple logistics scenarios without altering the core system. 

Parcel function

In the Parcel function, couriers add parcels by scanning QR codes or by manually entering parcel IDs. For greater efficiency, multiple parcels can be selected at once by scanning code sets or entering a set ID for batch processing. 

Once validated, parcels appear in the current working list, confirming that they are correctly linked to the courier. They remain visible until delivery completion or manual removal or once the delivery is confirmed in the system. A refresh option allows the courier to retrieve the most up-to-date parcel information at any time.  

Selecting a parcel provides access to essential delivery data, including its identification number, status, delivery address, expected delivery date, weight, service type, and associated round. During the delivery process, couriers can register events and update parcel quality directly within the app. 

For proof of delivery, a single parcel is selected, and the receiver signs directly on the device. The signature is securely recorded and immediately reported, ensuring reliable confirmation of delivery and traceability. 

Stop function

Through the main menu, couriers can switch to the Stop function, which provides a structured overview of planned stops and related parcel information grouped by delivery location. Stops can be visualised on an interactive map, offering clear route visibility and improved situational awareness through real-time geolocation.  

Selecting a stop reveals the parcels assigned to that location, allowing couriers to manage grouped deliveries efficiently. If a disruption occurs, the courier can report it directly within the app by selecting the disruption type, adding comments, and automatically sharing their position. This real-time communication supports immediate operational adjustments and proactive issue management.  

BYOD impact

The Green-Log BYOD tool ensures that every action, from parcel validation to signature capture and disruption reporting, is securely transmitted to logistics operators. This continuous flow of information enhances: 

• Transparency 
• Improves coordination 
• Supports data-driven decision-making 

By combining flexibility, live operational visibility, and secure reporting, BYOD strengthens last-mile delivery efficiency while contributing to more sustainable and optimised urban logistics operations across different city environments. 

→ Check our Digital Twins solution

La entrada Bring Your Own Device overview se publicó primero en Mosaic Factor.

To address this challenge, we are pleased to introduce an upcoming Explainable AI (XAI) masterclass, tailored specifically for automotive professionals who need to design AI models that are accountable, transparent, and trustworthy. This session explores the principles, techniques, and real-world practices required to move from opaque “black-box” models to AI systems that engineers, regulators, and customers can confidently trust. 

Why Explainable AI Matters in the Automotive Industry 

Automotive AI systems must meet exceptionally high standards for safety, compliance, and accountability. Explainable AI is essential to enable: 

• Regulatory compliance (including emerging AI regulations and automotive safety standards).
• Traceability of decisions in safety-critical systems.
• Debugging and validation of complex machine learning models.
• Bias detection and mitigation in both data and predictions.
• Trust and acceptance from regulators, partners, and end users.

Without explainability, even high-performing models may be difficult or impossible to certify, validate, or deploy responsibly. 

Online Masterclass

While the session is delivered online, its hands-on, practical, and in-depth approach goes beyond a traditional webinar. Participants will not only learn what Explainable AI is, but also how to apply it directly within real automotive use cases and development pipelines. 

For this reason, we define the event as an online masterclass, combining depth and interactivity with the accessibility and convenience of a webinar format. 

📨 Click here to register now. 

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.
• Accountable: decisions can be justified and traced.
• Trustworthy: safe to deploy in real automotive environments.

In this session, participants will gain practical, actionable insights into: 

1. The foundations of Explainable AI (XAI) and model interpretability.
2. The distinction between inherently transparent models and post-hoc explainability.
3. Key XAI techniques for automotive applications (for example: feature attribution and local versus global explanations).
4. How to design AI systems that are accountable by design
5. Integrating explainability into development, testing, and validation workflows
6. Supporting audits, documentation, and regulatory reviews using XAI
7. Real-world automotive examples and lessons learned 

The focus is firmly on practical decision-making, not abstract theory. Our Chief Data Scientist, Burcu Kolbay, will be answering questions at the end.

Who Should Attend 

This masterclass is designed for professionals working with AI across the automotive ecosystem, including: 

• AI and Machine Learning Engineers
• Data Scientists
• ADAS and Autonomous Driving Engineers
• Functional Safety and Compliance Managers
• R&D and Innovation Leaders
• Product Owners and Technical Managers 

No legal background is required. Applied, engineering-focused perspectives take center stage. 

Event Details

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

Registration

Join this masterclass to ensure your AI solutions are powerful, but also understandable, accountable, and trusted.

📨 Click here to register to secure your place and help shape the future of trustworthy automotive AI. 

La entrada Masterclass Explainable AI for automotive: from black boxes to trust  se publicó primero en Mosaic Factor.

Within the AutoMoTIF project, this challenge is addressed through automated shunting as a service, with Mosaic Factor leading the development of the simulation framework. 

From operational bottleneck to coordinated rail operations

Shunting plays a central role in intermodal terminals, linking maritime cargo flows with inland distribution. However, traditional shunting operations are often reactive, fragmented across systems, labour-intensive, and energy-inefficient. 

Shunting as a service reimagines these operations as a digitally orchestrated service platform where autonomous locomotives, yard resources, and scheduling systems operate as an integrated ecosystem. The aim is not simply automation, but service optimisation. 

Simulation driving the transformation

Mosaic Factor’s advanced simulation environment replicates the operational complexity of rail terminals, including train movements, wagon marshalling, yard capacity constraints, container handling cycles, resource allocation, and disruption scenarios. 

Autonomous shunting locomotives are modelled as intelligent agents that dynamically respond to congestion, schedule changes, and infrastructure constraints. 

Through scenario modelling, the simulations evaluate: 

• Reduced shunting time 
• Faster wagon turnaround 
• Lower idle and waiting times 
• Optimised energy consumption 
• Increased yard throughput 
• Improved safety and lower operational costs 

This data-driven approach ensures automation concepts are validated before real-world deployment. 

Shunting as a Service Platform

Shunting as a service introduces a shift in how rail yard operations are structured. Instead of a fixed internal activity, shunting is modelled as a service-oriented platform where capacity is dynamically allocated, operations are digitally coordinated, and performance is continuously monitored. 

This approach supports greater interoperability between terminal operators, rail infrastructure managers, logistics providers, and port authorities, while enabling integration with other automated processes within AutoMoTIF. 

Supporting Smarter Rail Terminals 

To ensure realistic outcomes, Mosaic Factor calibrates simulations using historical operational data, planning inputs, and stress-test scenarios that reflect peak demand and future growth. 

The resulting models provide decision-support tools for infrastructure investment, automation strategies, business models, and regulatory alignment, helping reduce risk and accelerate deployment. 

Strengthening Europe’s Rail Freight Network 

By improving rail efficiency, automated shunting supports broader logistics goals, including: 

• Modal shift from road to rail 
• Reduced terminal congestion 
• Lower emissions 
• Safer working conditions 
• More reliable logistics operations 

Through simulation-driven validation, Mosaic Factor demonstrates how automated shunting can increase throughput, reduce delays, optimise energy use, and enhance safety. 

Automated shunting as a service-oriented platform represents more than a technological upgrade. It introduces a new operational model that strengthens the role of rail in Europe’s transport system while supporting a more efficient and sustainable logistics network. 

→ Check our Digital Twins solution

La entrada Automated Shunting as a Service Platform se publicó primero en Mosaic Factor.

Representing Mosaic Factor at the event was Head of Sales Joan Sampablo, who attended on behalf of Mosaic Factor to explore the latest developments shaping the future of connectivity, artificial intelligence, and digital infrastructure.

Telecom as the backbone of AI 

MWC 2026 highlighted how the telecom industry is moving beyond connectivity to become a core enabler of the next generation of AI-powered services. Leadership from the GSMA outlined three key pillars expected to drive the next phase of digital growth:  

• The completion of 5G standalone network rollouts.
• Broader access to open and inclusive AI technologies.
• Stronger global digital safety and trust frameworks 

These priorities reflect a broader shift toward networks that are not only faster but also capable of supporting distributed intelligence and AI-driven services. 

The rise of on-device intelligence

A major theme throughout the event was the rise of AI everywhere, particularly the shift toward on-device and network-native intelligence. Chipmakers and device manufacturers showcased powerful new system-on-chip architectures designed for local AI inference, enabling capabilities such as:

• Real-time translation
• On-device summarization
• Advanced photo processing
• AI assistants capable of performing complex tasks without relying on the cloud.

For developers and enterprises, this shift signals a new design standard where applications are built with local AI processing and multimodal interaction in mind, combining camera, voice, and text interfaces to deliver richer experiences.

Future of autonomous systems

Another prominent topic was the emergence of agentic AI systems, where autonomous AI agents can coordinate tasks across networks, infrastructure, and physical environments. Telecom providers and startups demonstrated how such systems could be applied to network orchestration, automated operations, and enhanced cybersecurity.

Next generation of connected systems

One of the most talked-about concepts at this year’s congress was “kinetic compute” and “kinetic tokens.” These represent ultra-fast compute units designed to synchronise real-world actions across robotics, drones, autonomous vehicles, and other distributed systems. Telecom operators argue that their networks are uniquely positioned to deliver the millisecond-level coordination required to power these emerging technologies. 

Security, digital trust, and global inclusion

Security and global digital inclusion also featured prominently across the conference agenda. With cybercrime costs continuing to rise worldwide, industry leaders emphasised the importance of:

• Privacy-preserving AI
• Stronger digital identity frameworks
• AI models capable of supporting more global languages
• Cross-border infrastructure standards

These priorities reflect the growing recognition that technological innovation must be accompanied by strong governance and inclusive digital access.

Mosaic Factor at MWC 2026

According to Joan, attending MWC provides valuable insights into how the telecom and technology landscape is evolving at a rapid pace. The event highlighted the growing importance of AI-enabled infrastructure and edge computing, as well as the increasing role telecom networks will play in enabling intelligent applications and connected systems.

For Mosaic Factor, attending the congress offers an opportunity to stay closely aligned with emerging industry trends and innovations that will shape the next generation of digital solutions.

As the industry moves deeper into the so-called IQ-Era, the convergence of AI, advanced connectivity, and distributed computing is expected to redefine how devices, networks, and businesses interact, creating new opportunities for innovation across sectors worldwide.

La entrada Mobile World Congress 2026: Entering the IQ Era se publicó primero en Mosaic Factor.

Driving efficiency and innovation with digital twins

Persi highlighted the MyEV Digital Twin, a two-layer architecture with a full off-board version for simulations and a lighter in-vehicle version, fully connected and interoperable with AI applications to enable continuous updates and optimisation. 

This design enables continuous updates and interoperability with advanced AI applications, ensuring that software-defined EV functions can be optimised for energy efficiency, safety, and driver experience throughout the vehicle’s lifecycle. 

TwinOps Methodology: Continuous Optimisation

At the heart of Twin-Loop is the TwinOps concept, which merges: 

• Model-based engineering (MBE) principles 
• DevOps practices 
• Digital twin continuous integration 

This methodology allows digital twins to remain synchronised with evolving vehicle designs, supporting efficient design, validation, and operational decision-making. TwinOps also leverages vehicle and fleet data to enhance energy optimisation, safety, and system integrity without interfering with core EV functions. 

Energy, safety and sustainability

Energy efficiency was a key focus of the presentation. Persi outlined how optimised in-vehicle algorithms and compressed data transmission can reduce energy usage while maintaining high performance. He also highlighted how TwinOps integrates environmental modeling, supporting lifecycle emission reduction and the transition toward software-defined vehicles (SDVs). 

Safety and cybersecurity remain central to the framework: digital twins and apps are designed to operate behind core systems, with tools such as CyberCar, OFSCE, ModVV, and ZTBCar providing protection against individual vehicle attacks, fleet-level intrusions, and in-vehicle system violations. 

Fostering collaboration and European competitiveness

The Twin-Loop consortium brings together 12 partners from 8 countries, spanning academia, automotive manufacturers, technology providers, and consulting firms. Advisory board members include ZF Friedrichshafen AG, Volvo Group, Zunder, and Traton, and the project is supported by a €5 million budget. 

By developing an open framework for MyEV Digital Twins and a suite of connected applications, Twin-Loop aims to accelerate innovation, reduce development time, and strengthen European automotive competitiveness. The framework provides reusable modules, development guidelines, and shared services, simplifying complex digital system development. 

Looking ahead

The presentation highlighted how TwinOps and MyEV Digital Twins are driving a new era of smarter, safer, and more sustainable electric vehicles. By leveraging continuous improvement and fleet-level insights, these tools enhance energy efficiency, driver experience, and overall system resilience. Through Twin-Loop, the project is helping shape the future of software-defined vehicles and driving the European automotive industry toward the next generation of mobility.

La entrada TwinOps and MyEV Digital Twins: advancing SDV at RTR Conference se publicó primero en Mosaic Factor.