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.

Moving beyond Traditional RAG

Traditional RAG pipelines embed a query, retrieve context, and generate a response.

Agentic RAG introduces intelligence into the process. By classifying intent before deciding whether to retrieve, call tools, or answer directly, companies report cost reductions of up to 40% and latency improvements of 35%.

Core patterns driving adoption

Industry experts point to three architectural patterns that define Agentic RAG:

• Intent-Based Query Routing: determines whether retrieval is necessary or if a direct answer suffices.
• Tool Orchestration with Error Handling: coordinates APIs, calculators, and databases while managing failures gracefully.
• Continuous Cost & Latency Evaluation: tracks token usage and performance metrics in real time.

These patterns allow systems to decide, adapt, and optimise, a critical requirement for enterprise-scale AI.

Architecture in practice

Agentic RAG systems are typically built on three layers:

• Orchestration Layer: the “decision brain” that routes queries intelligently.
• Execution Layer: handles retrieval, tool calls, and LLM inference.
• Infrastructure Layer: provides vector databases, deployment management, and observability.

Unlike traditional RAG, which always retrieves, Agentic RAG evaluates whether retrieval is even necessary, orchestrating the optimal combination of retrieval, tools, and generation.

Provider flexibility through gateway layers

Another key trend is the rise of gateway abstractions that allow developers to switch seamlessly between providers such as OpenAI, Anthropic, Google, and Bedrock. This approach enables:

• Failover routing when providers experience downtime.
• A/B testing without code changes.
• Cost optimization by directing queries to the most efficient model.
• Freedom from vendor lock-in.

Companies are increasingly adopting unified gateways to balance speed, cost, and reliability across providers.

Conclusion

Agentic RAG is no longer a niche experiment but the blueprint for production AI systems. By combining retrieval with decision-making, orchestration, and observability, the technique is setting new standards for efficiency and adaptability in enterprise AI.

“Production AI isn’t about retrieval alone. It’s about intelligence: knowing when to retrieve, when to call tools, and when to answer directly. Agentic RAG delivers that intelligence”.

La entrada Agentic RAG for AI se publicó primero en Mosaic Factor.

Let’s break down the key types of AI and how we help you apply them.

AGI: the long-term horizon

General AI, or General Artificial Intelligence (AGI), refers to machines that can perform any intellectual task a human can. It’s flexible, autonomous, and capable of reasoning across domains.

Current Status: AGI is still theoretical. No existing system has achieved true general intelligence.

We think this is a highly compelling topic, and we are monitoring developments with great interest and anticipation.

Generative AI: real-world creativity at scale

Generative AI (or GenAI) is already transforming industries. These models create new content -text, images, code, audio, amongst others- based on learned data patterns.

Use Cases we can deliver for GenAI:

• Automated content generation for specific industries (like healthcare).
• Document summarisation and contract analysis for legal or compliance teams.
• Intelligent chatbots for internal company queries or customer support.
• Code generation and debugging tools for industry-specific developers (like automotive).

Our Solutions: we are capable of building and fine-tuning generative AI models using your proprietary data, ensuring outputs are accurate, brand-aligned, and compliant. Whether you need a custom GPT-style assistant or an image generator for product design, we can make it happen.

Specific AI techniques

Our core focus is enabling businesses to solve precise challenges through advanced AI techniques. It is what we have always done -what we call ‘traditional AI’ – and it has been central to our journey since our founding.

Our Approach: We design, train, and deploy these models with full lifecycle support: from data strategy and infrastructure to governance and performance monitoring.

Why partner with Mosaic Factor?

AI is powerful, but only when applied with precision. We don’t just deliver tools, we deliver transformation.

• Strategic AI consulting and roadmap development
• Custom model design and integration
• Scalable cloud and edge deployment
• Ongoing support, compliance, and optimisation

Whether you are exploring generative AI for creative automation or machine learning for operational efficiency, we help you turn potential into performance.

Ready to explore what AI can do for your business? Contact us to build something extraordinary, together.

La entrada General vs. Generative AI: what they mean for your business se publicó primero en Mosaic Factor.

So we are talking about having open, compliant models based on diversity and ethics, at a European level.

Industry-specific LLMs

Working towards the development of industry-specific language models based on OpenEuroLLM models offers a unique opportunity for companies. These models not only democratise access to high-quality AI technologies but also allow precise customisation to meet the specific needs of each sector.

Key Benefits:

1. Adaptability and Precision: The models can be fine-tuned for specific applications, improving the accuracy and relevance of AI solutions in industrial contexts.
2. Regulatory Compliance: Developed within the European regulatory framework, these models ensure that AI solutions comply with current regulations, reducing legal and ethical risks.
3. Linguistic and Cultural Diversity: The multilingual capability of these models preserves linguistic and cultural diversity, enabling companies to operate effectively in multiple European markets.
4. Transparency and Community: The open nature of the project fosters collaboration and knowledge sharing, creating an active community of developers and users who can contribute to the continuous improvement of the models.

Based on Trustworthy AI

For companies, investing in the development of industry-specific language models based on OpenEuroLLM is not only an innovative strategy but also a way to ensure they are at the forefront of AI technology, complying with European standards and fully leveraging AI capabilities to enhance their competitiveness in the global market.

→ Check our LLMs solution

La entrada Open LLMs for AI transparency se publicó primero en Mosaic Factor.

To provide an idea, the top-5 algorithms we use more often for predictive models are:

1. Statistical Models: sophisticated statistical models and approaches such as generalised modeling, regularisation, Bayesian Inference, and time series analysis and forecasting which are used to capture intricate dependencies, model uncertainty, and make robust predictions with generalised models based on complex data distributions and latent structures.
2. Machine Learning Algorithms: powerful models to capture complex data relationships with tree-based, kernel-based, ensemble techniques (bagging, boosting, stacking and blending, and voting ensembles). The advanced supervised ML approaches are enhanced with techniques to improve generalisation and interpretability. Reinforcement learning through environment interactions by applying optimisation of policies, value-based learning, and actor-critic methods are designed for (sequential) decision-making.Advanced and tailored unsupervised learning techniques to focus on discovering hidden patterns, creation of segments and groups are developed and used. These techniques include:
   1. clustering,
   2. dimensionality reduction,
   3. and representation learning.
3. Deep Learning techniques: Deep Learning is based on deep neural networks to learn hierarchical representations of data, being key in applications such as natural language processing and image recognition.
4. Neural Networks are advanced models and approaches from deep learning. Representation learning, and attention-based architectures that enable state-of-the-art and also beyond-state-of-the-art with innovation in areas like computer vision, natural language processing, and sequential modelling. The motivation stands for:
   1. improving generalisation,
   2. scalability,
   3. and interpretability through advanced techniques by pushing the boundaries of what a machine can learn.
5. Explainable Artificial Intelligence (XAI techniques): methods aiming to uncover how models with complex dataset and structure make the predictions, providing transparency in decision-making pipelines and processes. Techniques include both model-agnostic and model-specific approaches; they are crucial to understand the rationale behind a model output and a decision.

→ Check our Predictive Model solutions as well as our Trustworthy AI solutions.

La entrada Our top algorithms for predictive modeling se publicó primero en Mosaic Factor.

What are LLMs?

LLMs (Large Language Models), are advanced AI systems capable of understanding and generating various forms of content, including text, code, images, video, and audio. These models are trained on at least one billion parameters (data points), which allow them to grasp language patterns and respond appropriately.

LLMs find applications in natural language processing tasks such as text generation, translation, sentiment analysis, data analysis, question answering, and text summarisation.

Evolution of LLMs

Key milestones include:

• 1966 ELIZA: The first chatbot simulating a psychotherapist.
• 2013 word2vec: Efficient methods for learning word embeddings from raw text.
• 2018 GPT and BERT: Groundbreaking models.
• 2020 GPT-3: A significant leap.
• Late 2021 and 2022: GPT-4 and other advancements.
• Statistical models: Developed to learn patterns from text data.

LLMs vs. NLP

While NLP (Natural Language Processing) models interpret or transform existing text, LLMs excel at generating new, coherent text from scratch.

They can create essays, stories, and even computer code that mimics human writing styles.

Light LLMs

Nowadays, though, there is an increasing importance of smaller models (light LLMs) for specific domain applications.

While the largest models would all be “general purpose”, light LLMs are developed with a specific sector use in mind.

That is:

• Large models use a huge number of parameters, without tuning to a specific use, use a lot of energy, sometimes with questionable reliability, and that provide answers even when they don’t know them.
• Smaller models consider the use that is going to be given to it, refining its responses (fine-tuning) the specific model for a specific use.

Light LLMs benefits

1. Efficiency: Light LLMs require fewer computational resources, making them faster and more cost-effective.
2. Scalability: Companies can deploy light LLMs across various applications without straining infrastructure.
3. Customisation: Light models allow fine-tuning for specific tasks, tailoring them to company needs.
4. Privacy: Smaller models reduce the risk of inadvertently leaking sensitive information.
5. Easier Maintenance: Light LLMs are simpler to manage and update.

To conclude, while both open-source and closed LLMs have their merits, light LLMs offer practical advantages for companies seeking efficient, adaptable solutions. Therefore, you should consider your specific requirements when choosing the right LLM for your organisation.

→ Check our LLMs solution

La entrada What are light LLMs? se publicó primero en Mosaic Factor.

Our team visited Kyutech in Japan to collaborate in the creation of AI-enhanced healthcare products

During the conference, we were able to connect with top HealthTech researchers, developers, and institutions. Following the conference, our team met with Professor Sozo Inoue, Director of “Care XDX Center Kyutech”, Kyushu Institute of Technology, focused on the application of ML and IoT on activity recognition aimed at Healthcare & Nursing Tech. These technologies can significantly improve patient care and monitoring.

It was also relevant the discussion with Doctor Colley from Hokkaido University (Noriyo Colley, Ed.D., MNS, BE, BN, RN in Japan and Australia) whose team developed an interactive simulator to train nurses and improve nursing care quality. Training nurses effectively is essential for maintaining high-quality healthcare services.

Innovation in Big Data and AI

These meetings will facilitate the collaboration between the institutions both in the field of innovation in big data and AI projects, and in the promotion of new data-based products in the international market to improve health care systems (HealthTech).

Clearly, collaborations like these foster innovation: integrating big data and AI can lead to breakthroughs in healthcare, from predictive analytics to personalised treatments.

It’s exciting to see how these partnerships will promote new data-based products globally, enhancing health systems.

Seminar on Trustworthy AI

During her visit, our CMO, as per her role as Associate Professor at UAB as well as her professional career as digital business and strategy expert, held the seminar entitled “Trustworthy AI: Solving problems with data while making a positive impact in society” at the Kyushu Institute of Technology.

The talk was about the importance of working not only on the use of data to solve real problems of companies and society but also the relevance of thinking and establishing how to work with data at a strategic level: paying attention both to market aspects and to what is referred as Trustworthy AI. This includes elements such as accessibility, security, equity, accountability, transparency, fairness, reliability, and robustness of the artificial intelligence algorithms that are integrated as well as the ability to explain how they reach to their conclusions.

Ensuring ethical and transparent AI development is crucial. The above factors are essential for building AI systems that benefit society. This means working with AI algorithms with a white-box perspective, including explainable-by-design and fairness-by-design approaches; so, making AI trustworthy by also having the capacity to explain the reasoning behind the algorithm and making this explanation accessible to different stakeholders so that they can take strategic and business decisions based on this information instead of working with black-box AI tools.

Finally, the talk included different use cases that we are working at Mosaic Factor on the application of AI algorithms in different sectors, including Trustworthy AI and explainability of AI algorithms.

→ Check our Trustworthy AI solution

La entrada AI-enhanced products to improve healthcare se publicó primero en Mosaic Factor.

The explainability of an AI model can be put in practice in different ways for various industries. Let’s see some examples.

1.Healthcare

When working in healthcare, we are talking about highly regulated environments that need to be certified, trusted and accountable. For instance, when performing patient disease diagnosis, explainable AI can explain the elements and data that was used to diagnose that patient. This way, we help create greater trust between patients and their doctors while mitigating any potential ethical issues when a machine is aiding the detection of a disease.

Typical use cases for this are validation of AI predictions that work with medical imaging data when diagnosing cancer.

2.Manufacturing

Explainable AI can also by applied in a production line to detect, map and explain the causes for unproper machine behaviour or defective product outputs, causing what it is called “nonconformities” on product quality in the production process or highlight the need for maintenance.

This way there is a higher understanding of machine-machine and machine-operator communication and business management policies can be made to decrease costs and gain productivity while keeping trusted and save production standards that need to be complaint and certified.

3.Mobility

Explainable AI is becoming increasingly important in the transport and automotive industry due to the expansion of IoT and smart mobility solutions as well as the potential expansion in use of autonomous vehicles -first in business environments such as logistics self-driven vehicles or trains, later in end-users.

This has placed an emphasis on explainability techniques for AI algorithms, especially when it comes to using cases that involve safety-critical decisions. Explainable AI can be used for autonomous vehicles where it provides increased situational awareness in accidents or unexpected situations, which could lead to more responsible technology operation (i.e., preventing crashes).

4.Recruitment

Resume screening: explainable artificial intelligence could be used to explain why a resume was selected or not. This provides an increased level of understanding between humans and machines, which helps create greater trust in AI systems while mitigating issues related to bias and unfairness.

5.Finance

Fraud detection: Explainable AI is important for fraud detection in financial services. This can be used to explain why a transaction was flagged as suspicious or legitimate, which helps mitigate potential ethical challenges associated with unfair bias and discrimination issues when it comes to identifying fraudulent transactions.

Loan approvals: explainable artificial intelligence can be used to explain why a loan was approved or denied. This is important because it helps mitigate any potential ethical challenges by providing an increased level of understanding between humans and machines, which will help create greater trust in AI systems.

→ Check our Mosaic XAI dashboards

La entrada Trustworthy AI for different industries 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.