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.

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.

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.

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.

Tackling urban blind spots

In busy city environments, parked cars, buildings, and vegetation often obstruct vehicle sensors, creating blind spots that pose serious risks, especially for vulnerable road users (VRUs) like children, cyclists, and road workers. Current detection systems struggle in these scenarios, with recognition rates dropping below 65% when individuals are fully occluded.

HIDDEN aims to overcome this challenge by enhancing Collective Awareness through Vehicle-to-Everything (V2X) communication and Artificial Intelligence. By sharing sensor data between vehicles, infrastructure, and road users, the project enables a more complete and dynamic understanding of the urban environment.

Hybrid Intelligence: a human-machine fusion

What sets HIDDEN apart is its use of Hybrid Intelligence (HI): a fusion of human and machine intelligence. This approach allows automated systems to make decisions that are not only technically sound but also ethically and legally grounded, reflecting human judgment and behavior.

“HIDDEN goes beyond conventional AI,” said Dr. Angelos Amditis, HIDDEN Coordinator and R&D Director at ICCS. “We’re bringing human judgement into the loop -so automated systems can act not just accurately, but wisely.”

Real-world testing

The project will test its approach in four high-risk urban scenarios:

• A child running from behind a parked car
• A cyclist navigating mixed-traffic zones
• A road worker obscured by vegetation
• A vehicle hidden at an unsignalised intersection

These cases reflect complex, real-world challenges where improved perception and ethically grounded decision-making could be life-saving.

A pan-European collaboration toward safer, smarter cities

Funded by Horizon Europe’s Cluster 5 with a grant of approximately €5 million, HIDDEN is supported by the Connected, Cooperative and Automated Mobility (CCAM) Partnership. The consortium includes 14 partners and 2 affiliated entities across 7 EU countries, bringing together expertise from research institutes, universities, SMEs, automotive leaders, regulatory bodies, and social science researchers.

HIDDEN isn’t just about smarter vehicles—it’s about building trust, aligning technology with human values, and paving the way for safer streets across Europe. Through field tests and virtual simulations, the project will validate its technologies and work closely with EU type approval bodies and UNECE working groups to shape future standards and policies.

Mosaic Factor’s contribution

Mosaic Factor leads the development of Explainable AI (XAI) and Human-Feedback Reinforcement Learning (RLHF) methods within the project. Their work focuses on creating a transparency-first explanatory toolkit that fosters trust, user acceptance, and ethical integration of AI in connected and automated vehicles.

For more details, you can read and download the full press release here:

HIDDEN Press Release _EN_final

→ Review our Data Enhanced Product solutions

La entrada HIDDEN project launch 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.

There were so many things going on at the same time. If you want to have a global grasp of everything that took place there, you can check McKinsey’s global key take-aways from MWC2025, but here is a highlight of some of the most relevant moments of our participation.

Conferences

We were at various conferences on GenAI at 4YFN with magnificent talks such as “Prompting Business Models to Life” moderated by Oisin Lunny with speakers Miguel A. de Kfund, Pilar Manchón from Google, and Shahid Azim on how Generative AI rewrites business models.

Related to the topic above, we also participated in “Do AI Agents speak business?” and “The convergence of AI, GenAI and open RAN“.

Finally, we attended a conference on how collaboration facilitates the culture of innovation, as well as talks on how to enact equity in the digital age.

New Tech

Discover the latest tech trends that captivated us at the event. From groundbreaking advancements to game-changing innovations, here’s what you need to know.

1. AI Agents

At Mobile World Congress 2025, one of the most prominent trends was that of artificial intelligence (AI) agents. Not only do these agents respond to queries, but they can also adapt and personalize their responses based on the user’s history, preferences, and emotional tone.

This ability to personalise and adapt is known as Personality Engineering.

In addition, the integration of these agents in mobile devices, such as the Samsung Galaxy S25, which can process voice commands and perform actions between different applications without human intervention, was highlighted

This trend promises to reduce the need for direct interaction with devices, automating tasks based on user preferences.

2. AI-driven semiconductors

AI–powered semiconductors were also a central theme.

Intel introduced its new Xeon 61, a system–on–chip (SoC) with AI integration that significantly improves radio access network (RAN) capacity and energy efficiency. This chip offers up to 2.4x more RAN capacity and 70% better performance per watt compared to previous generations. Built–in AI acceleration enables up to 3.2x higher AI RAN performance.

AMD highlighted advances in AI–powered high–performance semiconductor technologies, especially in edge computing. These technologies allow data to be processed closer to where it is generated, improving the speed and efficiency of applications in real-time.

An AI-powered platform was introduced to redefine telecom operations, optimising network performance in real-time. This platform seeks to integrate AI directly into network infrastructure, such as radios, routers, and edge computing nodes, to improve efficiency and reduce costs. These innovations are designed to improve network connectivity and performance, making AI more viable at scale.

3. Smart Factory and the smart industrial revolution

A special focus was placed on smart factory systems, integrating signals, connectivity, and artificial intelligence (AI) to optimise processes and increase efficiency. This development underscores the importance of technological innovation in the transformation of the industry.

Meaningful connections

1. Digital Twins and sustainability with Fujitsu

We discovered Fujitsu’s innovative use of digital twins, based on data collected by marine drones. This project makes it possible to map and analyse the seafloor along the coast of Japan, providing information on carbon emissions, biodiversity, and environmental predictions. In addition, Fujitsu presented us with a Chat AI Agent designed for the automotive sector, along with a use case focused on the range and capacity of electric vehicle batteries.

2. Design, mobility and robotics: Pininfarina and Ducati

We had the pleasure of collaborating with the Pininfarina and Ducati teams, benchmarks in design and mobility, to explore technological and creative synergies, as they are now entering new technologies such as robotics.

3. EV charging solutions with Zunder

We explored the initiatives of Zunder, a leading leader in EV charging, which continues to lead the way towards more sustainable mobility.

Side events during MWC

In parallel, we also participated in Barcelona Woman Acceleration Week and Barcelona Open Data initiatives.

Barcelona Woman Acceleration Week
In BWOW 2025, we would highlight the speech of María González Veracruz, Secretary of State for Digitalization of Spain and AI, emphasizing the persistent gender equality gap in all industries, particularly in Tech: “We must work to create a new era of inclusive entrepreneurship”. Many women are not only qualified, but they are often overqualified, however, this does not always lead to opportunities in leadership roles.

Open Data day
We were also honored to participate in Open Data Day 2025, a global celebration of OpenData, with a session entitled “Women Leading Open Data for Equality.”

Organised by the Barcelona Open Data Initiative, this session highlighted the critical role that open data plays in addressing equality challenges. High-profile speakers -women at the forefront of data technologies and open data- shared their knowledge, experiences, and best practices in publishing and reusing open data. This event is part of Open Data Day, an annual global celebration that has been encouraging the adoption of open data in all countries and cultures for the past 12 years. Powered by the Open Knowledge Foundation and its network, the initiative underscores the importance of leveraging open data to drive innovation and promote equality globally.

La entrada Our take-aways from Mobile World Congress 2025 week se publicó primero en Mosaic Factor.

The EV Charging Location Planning Tool includes socio-demographic data, mobility flows, and charging session data from existing charging stations to predict future needs for charging points, both slow and fast, according to scenarios that include the anticipated adoption of electric vehicles. The tool was presented to target group users, mainly public authorities interested in the effective and efficient placement of charging points. Their feedback has been positive, especially as they seek to determine which sites to prioritise first and where to deploy additional chargers.

On another hand, users have seen different benefits from the tool’s demos.

1. It facilitates informed decision-making by allowing users to make data-backed decisions when planning charging infrastructure, which is a clear improvement over the traditional, intuition-based methods.
2. The tool also ensures efficiency in resource allocation by focusing on the most promising locations for new charging points and estimating usage rates and profitability.
3. It also enhances EV driver satisfaction by increasing the availability of charging points in the most needed areas.
4. Finally, the tool supports long-term planning by simulating scenarios for three to five years, providing confidence for future developments.

Nevertheless, conclusions during the final project meeting highlighted recommendations for policymakers and investors to guide future charging efforts and investments. Project partner experiences and a European survey of public authorities and operators pointed out:

• the need for tailored design guidance,
• improved grid connections,
• streamlined planning processes
• the importance of interoperability,
• user-friendly interfaces,
• and political support to maximise the impact and accessibility of innovative EV solutions.

Moving forward, following the end of the project we expect to reuse and possibly scale the product concept. Our scalability and exploitation efforts for this tool will focus on:

1. Business and Market analysis to assess fit to other locations and organisations in Europe in the EV ecosystem.
2. Contact and implementation plans to reuse the Location Planning Tool concept at R+D level in the identified locations and organisations, in collaboration with partners of the model development.
3. Result gathering to assess validation of the European rollout plan in the pre-identified locations and organisations and planning any future scaling to other locations outside the EU.
4. Assessment of different use cases where the product could be scaled in markets outside the EV environment where the applicability is strong (for instance, hydrogen vehicles).

The simulation platform

During the project, we first performed an analysis of EV drivers’ needs related to vehicle charging which resulted in the solution we designed and integrated: the Location Planning Tool.

This tool has been used and validated in three types of areas while running the project:

1. A small village in a rural environment without EVs (Val Trompia, in northern Italy). Public authorities validated the tool.
2. A city, Barcelona. A company has validated the tool: B:SM (Barcelona de Serveis Municipals).
3. One country, Luxembourg where CPOs have validated it.

This three-fold validation of the tool has proven valuable to illustrate the potential and capabilities of our approach of combining Big data analytics using real-time usage to enable public administrations and private companies to plan future charging infrastructure deployment in the right locations.

→ Check our Predictive Models solution

La entrada Charging Point Location Planning Tool se publicó primero en Mosaic Factor.