The discussion formed part of the broader work around the CIDAI white paper, created through its think tank with contributions from public and private stakeholders. As participants in this effort, we were pleased to contribute to the roundtable, where Stefano Persi shared practical examples and case studies highlighting the role of AI and trusted data sharing in real logistics environments.

Industry insight

Rail freight volumes remain below the European average and a €5M fee exacerbates the challenge. Transporting goods by rail continues to face operation and structural challenges, even as demand is expected to grow significantly.

In line with the Climate Neutrality 2050 objectives, rail freight volumes are projected to double compared to recent historical averages. Achieving this growth will require not only infrastructure investment, but also better coordination, optimisation, and collaboration across the logistics ecosystem.

From CIMALSA’s perspective, multimodality is a central lever for improving logistics efficiency and sustainability. The optimal model combines rail for medium- and long-distance transport and road transport primarily for first and last-mile operations. AI enables this shift by supporting the reallocation of transport flows from truck to rail, optimising routes, schedules, and capacity usage. This approach can significantly reduce emissions compared to road-only transport, while maintaining operational flexibility.

Data sharing and the role of Data Spaces

A recurring challenge identified is the reluctance of operators and logistics agents to share information. While concerns around privacy are legitimate, they often limit system-wide optimisation. Data spaces were highlighted as a key enabler, providing:

• Secure data exchange and controlled access
• Clear rules on how data is shared and used
• Technical foundation for AI tools to suggest routes, estimate costs, and simulate operational scenarios.

By ensuring data trust, data spaces allow AI to support better decision-making without compromising sensitive business information.

Challenges facing the sector

Five major challenges shape the future of rail freight and logistics:

• Pressure for sustainability and decarbonisation
• Resilience in the face of global and geopolitical crises
• Urban congestion and last-mile regulation
• Digitalisation interoperability, and cybersecurity vulnerabilities
• Organisational readiness and technological transformation

These challenges are closely interconnected and require coordinated responses.

Where AI can deliver tangible value 

AI is already demonstrating measurable benefits in transportation and distribution operations, where it can deliver improvements in efficiency, cost, and sustainability. For Mosaic Factor, this includes automation projects for container loading and unloading at ports.

Despite its potential, barriers hinder AI adoption. These challenges include:

• Lack of an AI strategy within organisations,
• governance issues and data fragmentation,
• lack of quality historical data, difficulty in evaluating the ROI of AI initiatives,
• and a complex and inconsistent regulatory environment. Addressing these barriers requires clearer strategic alignment between technology, operations, and regulation.

Participants aligned on a three-level model for the sector:

1. Digitalisation: basic digitisation and automation, where progress is already visible.
2. Data sharing: secure exchange of data, enabling network visibility.
3. Visibility and success stories: more networks than logistics and gaining visibility of success stories.

Advancing through these levels is essential to unlocking the full potential of AI in logistics.

Key takeaways

Logistics efficiency depends on the effective combination of innovation, sustainability, and regulation. The key question for operators is what value is created by sharing data. AI, when combined with multimodality and trusted data-sharing frameworks, can significantly enhance the efficiency, sustainability, and resilience of rail freight and logistics systems.

During the roundtable, Stefano highlighted three projects Mosaic Factor contributed to:

1. Pioneers: Container Transport Forecast (EU Green Ports Initiative)
2. Port of Antwerp-Bruges: Cargo Flow Predictor
3. Disruptive: Detection & Classification of Logistics Network Disruptions

Together, these projects show the practical impact of AI and data collaboration in real logistics operations, grounding the CIDAI roundtable discussion in tangible solutions.

Click here to read the full white paper from CIDAI.

La entrada The role of AI in Multimodal Logistics and Sustainable Rail Freight 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.

Key sessions featuring AI

Agentic AI in the Car: From Orchestration to Agentic Personalisation

• Speaker: Dogukan Sonmez, Project Lead Machine Learning Platform, BMW AG
• Focus: generative AI applications beyond text, handling 3D models, sensor streams, and domain-specific data.
• Highlight: explored multi-agent system architectures and orchestration techniques, showing how agentic AI can improve efficiency, adaptability, and decision-making in SDVs.

This session revealed groundbreaking approaches to embedding AI into the very core of vehicle intelligence, making it one of the most forward-looking talks of the event.

Scalable Function Deployment for SDV

• Speaker: Michael Niklas-Höret, AUMOVIO

• Focus: the ongoing (R)evolution of E/E architectures in vehicles, from decentralised domain-centric to Server Zone and Central Compute/Zero Edge, and the resulting challenge in deploying functions across these diverse architectures.
• Highlight: introduction of a new function development pattern designed to enable the re-use of functions across the three main architectures (and their hybrids), aiming to ease OEM migration to new E/E platforms and increase re-use across vehicle lines.

From Code to Car – Accelerating SDV Integration with Shift-Left and CI/CT

• Speaker: Felix Pretscheck, Bosch
• Focus: how the introduction of next-generation Compute ECUs, combined with a shift-left approach, virtualisation, and a modular CI/CT (Continuous Integration/Continuous Testing) framework, is transforming and accelerating the software and system integration process for Software-Defined Vehicles (SDV).
• Highlight: showcasing key methodologies and architectural enablers that streamline integration and validation, leading to improved software quality, faster integration cycles, and more agile, scalable, and production-ready complex automotive systems.

Coding for Cars: AI in the Driver’s Seat

• Speaker: Mikhail Vink, VP of Business Development, JetBrains
• Focus: the role of AI-driven development in regulated industries, ensuring the quality and security of AI-generated code, and integrating AI into DevOps.
• Highlight: demonstrated how AI can streamline coding processes while maintaining compliance and safety standards.

Between Research and Current Development in ADAS

• Speaker: Khaled Alomari, Manager Connected Vehicles, MHP (A Porsche Company)
• Focus: bridging cutting-edge research with real-world Advanced Driver Assistance Systems (ADAS) development.
• Highlight: emphasized collaboration between academia, industry, and regulators to accelerate AI-powered ADAS adoption.

Khaled’s insights underscored the critical role of AI in enhancing safety and efficiency, making this session a must-attend for anyone interested in the future of connected vehicles.

Mastering Complexity with Digital Twins

• Speaker: Ignacio Izaguirre, VP of Product, Concentrio AG
• Focus: leveraging digital twins at the signal level for software management.
• Highlight: showed how applying AI to dependency nets can identify anomalies and vulnerabilities, improving OTA updates and system reliability.

AI-Driven Autonomy in SDVs

• Speaker: Shashank Pathak, Product Management, ZF Friedrichshafen AG
• Focus: how AI enables perception, planning, and control in autonomous SDVs.
• Highlight: discussed challenges of deploying AI on automotive hardware and strategies for safe, scalable autonomy.

Workshops and world café sessions

The workshop sessions “Challenge Your Peers” brought attendees together in small, moderated roundtables where we debated pressing industry questions using collective intelligence and mind maps. Topics ranged from how software testing evolves under SDV architectures, the role of AI in automotive development, and the future of ASPICE compliance, to redefining in-car experiences through advanced HMI and exploring AI-driven autonomy.

Each workshop encouraged participants to share experiences, challenge assumptions, and co-create solution concepts. Similarly, the World Café format provided a dynamic environment for peer learning, cross-industry networking, and the generation of actionable insights that complemented the more formal presentations and case studies.

Our takeouts

The conference highlighted that AI is no longer a peripheral tool but a central pillar in the evolution of software-defined vehicles. From agentic personalisation at BMW to AI-powered ADAS at MHP, the sessions demonstrated how intelligence is being woven into every layer of automotive innovation.

These talks not only showcased current applications but also pointed toward a future where vehicles are adaptive, intelligent, and deeply connected.

La entrada Software-Defined Vehicles Conference Berlin 2025 se publicó primero en Mosaic Factor.

Here are the key trends and developments that stood out.

Digital Twins take center stage

Digital Twin (DT) technology was one of the most prominent themes across exhibitor stands. Cities are increasingly adopting DTs to simulate and manage complex urban systems. The most common applications showcased included:

• Emergency and disaster management
• Human behaviour modelling
• Traffic and parking optimisation
• Energy demand forecasting
• Urban planning, such as identifying areas where new childcare facilities are needed

Several companies also presented the evolution of the Citiverse concept, part of a European initiative that integrates Digital Twins with cybersecurity, IoT, and other advanced technologies (European Commission Citiverse Project). Another highlight was the introduction of 4D Digital Twins, which incorporate the time dimension to enable predictive urban simulations (Nfold ROI).

Smart Cities and visual Language Models

AI innovation was another major focus. NVIDIA unveiled its Visual Language Model (VLM) platform for cities, designed to transform sensor-captured image data into an intelligent “city brain” capable of interpreting current and potential urban scenarios.

Practical applications were demonstrated in Leipzig, where AI-driven DTs are being used to optimise parking spaces and bicycle infrastructure. Meanwhile, the University of Hamburg showcased collaborative, open-source AI projects, emphasizing their interest in joining European-funded initiatives (DCS Intro 2024).

Cybersecurity and Global Engagement

Cybersecurity was a recurring theme throughout the expo, underscoring its critical role in safeguarding smart city infrastructures. Notably, the World Bank was actively involved, reflecting the global importance of secure digital ecosystems.

Our Contribution: Open Innovation Challenges

As part of our participation, we engaged in open innovation challenges, presenting proposals that leverage Large Language Models (LLMs) and Digital Twins for corporate applications. These initiatives demonstrate our commitment to pushing the boundaries of AI and urban technology, ensuring that cities of the future are not only smarter but also more resilient and inclusive.

Our presence at Smart City Expo Barcelona reaffirmed our role as a forward-looking technology provider. By contributing to discussions on Digital Twins, AI, cybersecurity, and open innovation, we continue to shape the future of urban living, driving solutions that make cities more adaptive, efficient, and human-centered.

La entrada Smart City Expo Barcelona 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.

Key Challenges for Automotive AI teams

The main challenges when developing AI models for SDVs are:

• High-Risk Classification: AI systems in SDVs often fall under the “high-risk” category, triggering mandatory conformity assessments, documentation, and audit readiness.
• Complex Compliance Landscape: with over 450 pages of legal text, translating regulation into engineering tasks is a daunting challenge. Manual reviews and fragmented documentation slow innovation and increase costs.
• Audit Pressure: teams must prepare for both scheduled and surprise inspections. Non-compliance can result in fines up to €7.5 million or 3% of global turnover.

AI Trust & Compliance frameworks

To address these hurdles, it is key to develop modular, automated compliance frameworks tailored for SDVs to enable:

• End-to-End System Assessments: rapid, audit-ready evaluations aligned with the EU AI Act.
• Configurable Reports: targeted assessments for specific AI components, such as dataset balance or model transparency.
• Audit Phase Interface: tools for third-party assessors, streamlining feedback and reducing evaluation time.

Who should worry about this?

• OEMs: responsible for system-level compliance, they must ensure every AI component meets regulatory standards before vehicle launch.
• Tier 1 Suppliers: developers of critical AI modules must demonstrate component-level compliance to OEMs, enhancing collaboration and market competitiveness.

Industry-wide implications

The EU AI Act is not just a legal hurdle, but a strategic opportunity. By embedding compliance into the development lifecycle, automakers can build more resilient, transparent, and future-proof AI systems.

The Act encourages:

• Cross-functional collaboration: AI, cybersecurity, safety, and regulatory teams must work in tandem.
• Lifecycle accountability: from design to post-market monitoring, traceability becomes a core requirement.
• Innovation through structure: automated tools and frameworks transform compliance from a bottleneck into a catalyst for better engineering practices.

As SDVs become the norm, the EU AI Act will shape how automotive AI is built, validated, and deployed. Forward-thinking developers and suppliers who embrace structured compliance will not only avoid penalties but will lead the next wave of intelligent mobility.

GPAI & GenAI Under the EU AI Act: what developers must know

The GPAI Code of Practice, finalised in May 2025, provides critical guidance for providers of General-Purpose AI (GPAI) models, including Generative AI (GenAI) systems. The EU AI Act distinguishes between complex GenAI models -those with systemic risk– and simpler GenAI models, each facing different compliance burdens:

• Complex GenAI (systemic risk models): These models exceed thresholds like 10²⁵ FLOPs in training compute or demonstrate high-impact capabilities across domains. Providers must conduct adversarial testing, risk assessments, and incident reporting, and ensure cybersecurity protections. They must also notify the European Commission for public database inclusion and maintain detailed documentation of model architecture and evaluation strategies.
• Simple GenAI Models: These are not classified as systemic risk and face lighter obligations. Providers must publish a summary of training data, ensure copyright compliance, and maintain technical documentation for downstream users. Transparency is key: outputs must be labeled, and users informed when interacting with AI systems.

The Code of Practice serves as a blueprint for demonstrating compliance, helping developers navigate the AI Act’s layered requirements while fostering innovation and trust in GenAI technologies.

La entrada Navigating the EU AI Act: what it means for SDV developers and Automotive innovators 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.

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.