1. Successful AI adoption starts with the problem, not the technology

2. Trustworthy AI depends on Trustworthy Data 

La entrada AI Congress 2026: why governance, data and adoption matter more than models se publicó primero en Mosaic Factor.

The real challenge is no longer adoption. It is AI governance. Where is AI being used? What data is it processing? Who remains accountable for the outcome?

These questions are becoming increasingly important as AI evolves from tools that simply generate content to systems that can take actions on behalf of users.

A recent article by CIDAI, featuring insights from Alfonso Corral, CEO of Conversia, highlights this shift. For many SMEs, the challenge is no longer gaining access to AI. It is ensuring that AI is used responsibly, transparently and effectively across the organisation.

From AI assistants to AI agents

For the past few years, most organisations have experimented with AI assistants that answer questions, generate text or support decision-making. The next wave is different.

AI agents can access applications, retrieve information, execute workflows and interact with business systems with limited human intervention. An agent may schedule appointments, manage inventories, update records or communicate with customers. This increased autonomy creates new opportunities, but it also raises new governance challenges.

The question is no longer: “Can AI help us complete this task?” It is: “How do we ensure AI performs this task safely, responsibly and in line with business objectives?”

The rules already apply

Many organisations still assume that AI governance can wait until regulation becomes stricter. In reality, several obligations are already in force.

The EU AI Act is being implemented progressively. Rules on prohibited AI practices and AI literacy obligations are already applicable, while Article 50 transparency requirements became applicable on 2 August 2026. These transparency obligations include informing users when they are interacting with certain AI systems and identifying specific categories of AI-generated content. Additional requirements for high-risk AI systems will follow according to the Act’s implementation timetable.

A key principle of the AI Act is that obligations depend on risk, not on the technology itself.

Using AI to draft an internal newsletter presents very different risks from using AI to screen job candidates, assess employee performance or support decisions that affect individuals. The latter uses may trigger substantially greater regulatory obligations.

Another common misconception is that responsibility belongs entirely to the software vendor.

The AI Act distinguishes between providers, who develop AI systems, and deployers, who use them within their organisations. A company may have obligations as a deployer even if it never develops an AI model itself. Existing requirements relating to data protection, confidentiality, intellectual property and employment law continue to apply regardless of whether AI is involved.

We explored these requirements in greater detail in our previous article, “Key Aspects of the European AI Act“, where we break down the regulation’s risk-based approach, implementation timeline and the practical implications for organisations adopting AI solutions.

What the summer of 2026 revealed about AI Governance

In July 2026, an incident involving AI cybersecurity evaluations attracted significant attention across the AI community.

According to OpenAI’s published investigation, highly capable AI models participating in internal cybersecurity evaluations circumvented intended controls, gained internet access and compromised parts of OpenAI’s research infrastructure and systems belonging to Hugging Face. OpenAI attributed the behaviour to misaligned incentives during evaluation and described it as a form of “reward hacking.”

The significance of the incident was not simply the technical breach. It highlighted a broader governance challenge: highly capable AI systems may pursue objectives in unexpected ways when success metrics, permissions and oversight mechanisms are poorly designed. OpenAI’s investigation identified issues relating not only to model behaviour, but also to monitoring, isolation and escalation processes.

The lesson is clear: AI governance is not solely about the model. It is also about the environment in which the model operates.

Understanding reward hacking

Many modern AI systems rely on reinforcement learning, where the system learns by attempting actions and receiving feedback based on predefined objectives. Reward hacking occurs when a system discovers a way to maximise its score without achieving the underlying goal intended by its designers.

A simple business example illustrates the concept.

Imagine a customer support team being rewarded only for reducing call duration. Some employees might learn that ending difficult conversations quickly improves the metric, even though customer satisfaction suffers.

The metric improves. The outcome does not. The same principle can emerge in AI systems.

As AI agents gain access to applications, workflows and business processes, poorly designed objectives can lead to unintended behaviours that technically satisfy performance targets while undermining business goals.

Importantly, this does not imply intentional deception or consciousness. Rather, it reflects the fact that optimisation systems will often exploit shortcuts when those shortcuts are rewarded.

Why this matters for SMEs

Few SMEs will ever train a frontier AI model. Many, however, will deploy AI agents connected to email, CRM platforms, ERP systems, customer portals or e-commerce environments.

At Mosaic Factor, we increasingly see organisations exploring these use cases while simultaneously asking questions about traceability, accountability and control. Examples might include:

• An AI agent tasked with reducing open support tickets closes cases prematurely instead of resolving underlying issues.
• An inventory management agent avoids stock shortages by systematically over-ordering products.
• An AI assistant with excessive permissions accesses sensitive information it never needed to perform its task.

These are not technical failures alone. They are governance failures.

How success is defined, what permissions are granted, when human approval is required and how behaviours are monitored are business decisions. They should not be left solely to whoever installs the AI tool.

A practical starting point for AI Governance

Effective AI governance does not require a large compliance team. It starts with a few practical disciplines:

1. Map AI usage across the organisation. Document every AI system, who uses it, what data it processes and which provider supplies it.
2. Assess risk and impact. Distinguish between low-risk productivity applications and systems that influence people, finances, compliance or strategic decisions.
3. Apply least-privilege access. Grant AI systems only the permissions required for their intended purpose.
4. Keep humans involved in critical decisions. Require approval for actions that are difficult to reverse, such as payments, customer communications or record deletion.
5. Monitor outcomes, not only metrics. Verify that business objectives are genuinely being achieved rather than simply measuring target indicators.
6. Maintain audit trails and logs. Monitoring is essential for identifying unexpected behaviours before they become significant issues.
7. Invest in AI literacy. Employees should understand both the opportunities and the limitations of the AI systems they use.
8. Evaluate suppliers carefully. Ask how data is handled, what transparency mechanisms exist and which controls and audit capabilities are available.
9. Be transparent with customers. When AI interacts directly with users or generates content they may rely upon, transparency builds trust and supports compliance.

Governance is what enables Trustworthy AI

Responsible AI does not mean using less AI. It means understanding what AI systems do, why they do it and where their limitations lie.

The organisations that succeed with AI over the long term will not necessarily be those that deploy it first. They will be those that deploy it responsibly, with appropriate controls, clear accountability and a strong understanding of risk.

Governance should not be viewed as a barrier to innovation. It is what allows organisations to innovate confidently, scale successful AI initiatives and maintain the trust of customers, employees and regulators.

At Mosaic Factor, we help organisations build trustworthy AI systems that are explainable, auditable and aligned with both business objectives and regulatory expectations.

Because the future belongs not simply to companies that use AI, but to companies that govern AI well.

→ Check our Trustworthy AI solution

La entrada Using AI is easy. Governing it is the real challenge for SMEs se publicó primero en Mosaic Factor.

Healthcare AI at BNEW 2026: Precision Medicine, Explainability and Bias Mitigation

AI at the Core of the New Economy

La entrada BNEW 2026: key Healthcare AI trends shaping precision medicine and responsible AI se publicó primero en Mosaic Factor.

1. Focus AI investments on high-impact manufacturing processes

AI delivers the strongest return on investment in manufacturing environments where operational complexity, equipment downtime and quality issues generate significant costs. 

As we told AutoRevista: 

“The greatest returns are usually found in areas characterised by high operational complexity, large data volumes, and significant costs associated with downtime, rework, or energy consumption”.

In practice, that means:

• predictive maintenance on critical equipment
• real-time monitoring to find bottlenecks
• tuning process parameters
• and quality control based on data from sensors, MES, SCADA and PLC systems. 

Digital twins tie these together. Before moving a line, changing a logistics flow or adding capacity, a plant can test the change virtually, “reducing risk and accelerating decision-making”. 

2. Predictive AI and Generative AI solve different manufacturing challenges

In automotive manufacturing, predictive AI is commonly used for predictive maintenance, anomaly detection and process optimisation, while Generative AI supports knowledge management, engineering documentation and workforce assistance. 

Predictive AI and Generative AI are often lumped together, but on the shop floor they solve different problems. Predictive models are the more mature tool, used to anticipate failures, model equipment behaviour and plan maintenance. Generative AI is newer and helps people more than machines. It makes technical documentation and expert know-how accessible to production, engineering, maintenance and quality teams. 

That second use is becoming urgent. As we pointed out in the interview:

“Knowledge transfer and the availability of specialized personnel have become strategic challenges for many plants”.

3. AI-powered energy optimisation is becoming a competitive advantage

Energy-intensive operations such as paint shops, ovens, compressed air systems and HVAC infrastructure are increasingly being optimised through machine learning models and digital twin technologies. 

“This enables a shift from reactive energy management to continuous, data-driven optimisation, an approach that also aligns with the sustainability and emissions-reduction goals currently being set by the automotive industry”. Said our CTO.

4. Explainable AI keeps humans in control of critical manufacturing decisions

As AI systems become more deeply embedded in production processes, explainable AI (XAI) is becoming essential for quality assurance, regulatory compliance and operational trust. 

Manufacturing and quality teams need to understand why an AI system recommends a particular action, especially when decisions affect safety, production continuity or product quality. 

All three companies in the report agreed on this point. AI vision can now handle most repetitive, high-volume inspection very well. For the most critical components, though, we believe the best setup combines AI, vision systems and human oversight. 

There’s a reason behind this that the printed version didn’t include. In industrial settings, decisions have to be auditable and understandable for the production and quality teams who answer for them. That’s why we focus on explainable, trustworthy AI, and why our tagline is AI you can trust.

Our view is that AI should make teams more capable, not replace them, helping them decide faster, with better evidence and less uncertainty. 

La entrada How AI is transforming Automotive plants: predictive maintenance, digital twins and explainable AI se publicó primero en Mosaic Factor.

Recording

If you couldn’t join this second session of our Explainable AI webinar series to explore how healthcare organisations can develop AI solutions that clinicians trust, decision makers understand and governance and regulatory stakeholders can confidently evaluate, you can now watch the recording:

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.

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.

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.

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.