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

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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 meeting marked an important early milestone for ECAVA’s working structure. The day began with a road mapping of current European initiatives in autonomous driving, highlighting both the depth of activity already underway and the need for better alignment. With global competition intensifying and “China speed” referenced repeatedly throughout the discussions, participants pointed to the rapid pace at which China is advancing in the tech and autonomous driving space. The shared understanding was that Europe must strengthen coordination and accelerate execution to build its own competitive, high-performance European AI capable of matching that momentum. 

From strategy to software foundations

A key part of the dialogue centred on advancing a European Autonomous Driving software stack. Rather than duplicating efforts, participants explored how shared foundations and clearer governance could support scalable innovation while reinforcing Europe’s technological sovereignty. 

Two priority areas shaped the exchanges: 

• Technical governance and coordination across initiatives 
• Structured collaboration on a European AD software stack 

Parallel discussions on Autonomous Driving Deployment and AI and data pooling added practical depth to the conversation. Topics ranged from deployment pathways and cross-border testing to how data sharing and computing capabilities can underpin competitive, trustworthy AI systems. 

Momentum across the ecosystem

The strong turnout signalled growing alignment across the automotive value chain. Industry leaders shared perspectives on deployment models and real-world implementation, sparking thoughtful debate and reinforcing the need to move beyond discussion toward tangible progress. 

To maintain continuity and avoid fragmentation, a dedicated core team will be formed under the leadership of soon-to-be-elected co-chairs. Further structural details are expected at the next Steering Committee meeting in March, reflecting ECAVA’s ambition to remain lean, focused, and outcome-oriented. 

Mosaic Factor’s CEO, Stefano, attended the sessions as part of the broader industry dialogue, contributing to the collective effort shaping Europe’s autonomous driving roadmap. 

The takeaway from Brussels was simple and practical: Europe has the expertise and industrial capacity. The next step is disciplined, coordinated execution. 

Read more about our trustworthy AI solutions.

La entrada Driving trustworthy AI in autonomous mobility: insights from ECAVA 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.

As proud coordinators of Twin-Loop, we are thrilled to lead this groundbreaking effort to develop an Open Framework for TwinOps and Digital Twin technologies tailored to Electric Vehicles. The project’s ultimate goal: to significantly reduce EV energy consumption and pave the way for smarter, more sustainable mobility.

Assembly Highlights

• Collaborative Breakthroughs. Partners from across Europe came together in dynamic sessions centered on the Advisory Board and Use Cases, aligning on technical strategies and shared goals.
• Project Progress Review. A thorough evaluation of milestones, deliverables, and future objectives ensured all stakeholders are moving forward with clarity and cohesion.
• Networking & Synergy. Informal gatherings and structured networking activities fostered new relationships and deepened existing ones, reinforcing the collaborative spirit of Twin-Loop.
• Hands-On Workshops. Interactive sessions sparked fresh ideas and actionable insights, equipping participants with renewed momentum and a clear roadmap for the next phase.

As Twin-Loop continues to evolve, the energy and engagement from this General Assembly will serve as a powerful catalyst for innovation, impact, and progress toward a more efficient EV future.

Advancing Digital Twin Intelligence for the Future of SDV in electric vehicles

Twin-Loop is pioneering a new generation of Digital Twin innovation by harnessing the expanding capabilities of cloud-native architectures and High-Performance Computing (HPC). As Electric Vehicles transition into fully Software-Defined Vehicles, the project taps into real-time data orchestration and edge-to-cloud integration to redefine how digital replicas interact with physical systems. Our ambition is to elevate the EV experience: enhancing personalisation, improving energy efficiency, and embedded cybersecurity.

Current Digital Twin solutions often overlook the granular complexity of EVs, where each vehicle operates with a distinct blend of hardware, firmware, and software updates. Twin-Loop embraces this individuality, using fleet-wide operational intelligence and lifecycle-wide data fusion to build adaptive models that evolve with each vehicle. This approach unlocks the potential to reduce energy consumption intelligently, without sacrificing performance, comfort, or safety.

Through the development of an Open TwinOps Framework and a modular suite of digital tools, Twin-Loop will enable continuous optimisation across the four critical stages of the EV lifecycle: design, production, operation, and end-of-life.

→ Check our Digital Twins solution

La entrada Twin-Loop project meeting in Catania 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.