Mechanical Assembler 1

Mercedes-Benz puts product development in the AI fast lane. Published: Oct 22, 2026 Summarize with AI From digital drawing board to production line, Mercedes-Benz is rewiring global vehicle development around PLM.AI. The AI-ready backbone will connect the entire product lifecycle, cut development cycles by around 17 months, and unlock efficiency gains of up to 50 percent in individual engineering processes. Faster to the future, without cutting corners. * The new Product Lifecycle Management (PLM) system will use artificial intelligence (AI) to connect development, procurement, production, sales, and aftersales across the entire product lifecycle, helping innovations reach series maturity faster. * Mercedes-Benz expects to reduce the development time for new vehicles by approximately 17 months, whilst achieving efficiency gains of up to 50 percent in selected engineering processes. * Mercedes-Benz is working with two strategic technology partners on the transformation programme. CONTACT Software provides the platform's open-core system, whilst Siemens is expanding its long-standing collaboration with Mercedes-Benz in physical design and digital validation. Mercedes-Benz is preparing for a fundamental shift in how it conceives, engineers, and brings vehicles to market. With PLM.AI, the company is building an AI-native digital backbone that connects development, procurement, production, sales, and aftersales through a single, intelligent knowledge base. By creating an uninterrupted flow of data and insight across these functions, Mercedes-Benz aims to bring new products and technologies to series maturity faster, more efficiently, and more sustainably. Development time could be reduced by up to 30 percent, with efficiency gains up to 50 percent expected in selected engineering processes. The ambition is not to move faster by making compromises. It is to help teams make better decisions earlier, giving the right people access to the right information precisely when they need it. This connected approach is intended to reduce complexity, accelerate innovation, and bring new technologies to customers sooner. Throughout that transformation, the standards associated with every Mercedes-Benz vehicle will remain firmly in place. The company's commitment to quality and safety is unchanged. Vehicles developed with PLM.AI will remain unmistakably Mercedes-Benz, shaped by the same craftsmanship, engineering excellence, and desirability, but delivered to customers faster. From data silos to an ai-ready digital thread. AI is already woven into Mercedes-Benz's product development. AI agents support a growing range of tasks, from optimising material costs and navigating certification and homologation processes to identifying faults, resolving technical issues, and assisting with software development. Today, many of these agents operate within individual stages of the development process. PLM.AI is designed to take the next step by connecting applications across those stages through standardised data models and end-to-end integration of development information. The result will be a continuous digital thread built on consistent data and clearly defined standards. Information will be available in real time, coordination between teams will become simpler, and promising innovations will be able to move through development more quickly. Backed by an investment of about half a billion euros, Mercedes-Benz is laying the foundation for a new generation of product development that is more connected, data-driven, and intelligent than before. The system will allow teams to assess complex interdependencies more holistically and translate emerging technologies into tangible customer benefits at greater speed. PLM.AI will help employees examine cross-functional relationships, intelligently connect relevant information, and identify potential trade-offs much earlier in the development cycle. AI-powered simulations and digital development processes will also let teams use physical testing more selectively, potentially cutting it by half over time. Resources can therefore be directed more efficiently, whilst teams can validate development decisions earlier and with greater confidence. People remain at the centre of the process, however, and teams will continue to own product decisions. Weight optimisation illustrates the potential. Changing a single component can affect cost, crash performance, hardware and software functions, and the production process itself. Specialised AI agents can examine these interdependencies simultaneously, giving development teams a comprehensive picture sooner. With that broader view, teams can evaluate potential solutions against safety, quality, and commercial requirements rather than considering each factor in isolation. The same connected approach could also open new aftersales opportunities by bringing together product, service, and operating data. Three partners, one ai-ready backbone. Mercedes-Benz is working with CONTACT Software and Siemens to deliver the PLM.AI architecture. Together, the two strategic technology partners will play a central role in modernising product development and establishing the technical foundations for the new system. More specifically, CONTACT Software is supplying the open PLM core layer through CONTACT Elements. This will give Mercedes-Benz a modular, scalable foundation for integrating product data, processes, and applications throughout product development, whilst retaining the flexibility to incorporate future AI technologies. Meanwhile, Siemens is contributing solutions and expertise in AI-enabled automotive design, simulation and digital validation for the digital twin. Supported by PLM.AI's shared data and process foundation, these capabilities will help key engineering disciplines become more closely connected and move faster. For its part, Mercedes-Benz will lead the end-to-end transformation, defining the target vision, architecture, data models, and governance for PLM.AI. The company will also oversee the global rollout through an agile transformation programme and continue to develop the digital backbone over time. PLM.AI is set to become the cornerstone of the Mercedes-Benz engineering ecosystem, providing the foundation for fully integrated, data-driven, and AI-supported development across every vehicle programme. Throughout the transformation, Mercedes-Benz will retain full ownership and control of its data, as well as the AI models developed and operated using that data.

Siemens and NVIDIA advance AI-powered PCB inspection from Digital Twin to factory. 2 minutes read Siemens announced a new collaboration with NVIDIA to develop adaptive AI-powered manufacturing capabilities that combine industrial software, Siemens' Digital Twin technology and NVIDIA Cosmos to accelerate production planning, inspection preparation and optimization. The collaboration combines Siemens' Teamcenter software for product lifecycle management (PLM) and Process Preparation X with NVIDIA Cosmos to create a new AI-powered workflow for PCB inspection, helping electronics manufacturers prepare and optimize automated optical inspection (AOI) processes before physical production begins. The solution is being demonstrated at Siemens Electronics Factory Erlangen, Siemens' Customer Zero for adaptive AI-powered production - with the goal of creating a highly adaptive factory that can rapidly introduce near daily changes (3 to 5 times a week) without disruption. "By connecting the comprehensive Digital Twin to the shop floor and AI we are improving both line productivity and product quality. At the Erlangen factory, we're demonstrating how the deterministic nature of Siemens' Digital Twin is the foundation for leveraging physics AI for preparing production," said Mark Hindsbo, executive vice president operations software, Siemens. "By leveraging a 'virtual' golden board, electronics manufacturers can change production dramatically quicker without taking the line out of production. This enables electronics manufacturers to respond more quickly to change and helps reach production readiness at scale and speed - while helping to reduce manual engineering effort, accelerate AOI readiness and improve manufacturing quality." "Manufacturers shouldn't have to wait for the first physical circuit board to start preparing inspection," said Rev Lebaredian, vice president, Physical AI Simulation at NVIDIA. "By combining Siemens' industrial software and manufacturing expertise with NVIDIA Cosmos, we can generate synthetic images of good and defective boards to prepare inspection virtually helping manufacturers bring new products into production faster." Traditionally, introducing a new product or product variant requires a physical prototype before optical inspection processes can be prepared and trained for quality control. The new approach moves more of that work into the virtual world. Teamcenter provides PCB design, bill of materials and component information while Process Preparation X validates, normalizes and assembles this information into a Digital Twin of the printed circuit board assembly (PCBA). This provides the comprehensive product and manufacturing context needed to prepare inspection before the first physical board reaches the production line. This Digital Twin of the "perfect" reference board, often referred to as the "golden board," is used with NVIDIA Cosmos to create a fully photorealistic virtual golden board along with synthetic photorealistic samples that represent both good and false conditions created during production. Siemens uses NVIDIA Cosmos Data Factory to generate synthetic circuit-board defect images, enabling inspection-machine calibration before physical boards are produced. The workflow then extends into production through an AI-supported secondary inspection capability. When an AOI system identifies a potential defect, the secondary inspection capability can help analyze the detected condition and determine whether it represents a real defect or a false call. Together, these technologies are intended to create an end-to-end digital and AI-supported workflow spanning virtual AOI preparation, calibration and inspection support once production begins. Customer Zero at Siemens Electronics Factory, Erlangen The collaboration is being demonstrated at the Siemens Electronics Factory in Erlangen, Germany, which serves as Customer Zero for broader Siemens and NVIDIA work on adaptive, autonomous and AI-powered production. The factory contributes the manufacturing use case, production environment and subject-matter expertise in a highly adaptable, high-mix environment with ever-changing production processes. The project is targeting up to 85 percent less manual engineering effort in AOI program generation - with 40 percent fewer false calls and more than 5 percent first-pass yield improvement. For manufacturers, the intended value is earlier inspection preparation, reduced manual engineering effort, lower dependency on physical samples, better-calibrated inspection processes, reduced manual verification, faster production readiness and improved manufacturing quality. The collaboration also demonstrates how the Digital Twin and industrial AI can connect engineering intent with real-world inspection across the digital and real stages of production and how the comprehensive Digital Twin evolves from a virtual representation of the product into an active foundation for AI-powered manufacturing and quality processes.

Siemens adds self-verification for long-running EDA agents. Self-verifying agents can manage extended semiconductor design workflows, but deterministic EDA tools and human engineers still determine whether results are valid. Siemens recently expanded its strategic partnership with Nvidia to add new AI capabilities to the Fuse EDA AI Agent, enabling self-verifying agentic AI workflows for semiconductor and printed-circuit board (PCB) design. The goal is to move toward agents that can remain active throughout long-running semiconductor and PCB design workflows. Partner Content The new capability combines Siemens' electronic design automation (EDA) software with Nvidia's AI open models, agent software, secure runtime, and accelerated computing stack. It also spans Siemens' EDA technologies, including Catapult high-level synthesis, custom IC design and verification with Solido, Aprisa physical implementation, Calibre signoff verification, and Tessen design-for-test. The Fuse EDA AI Agent system, now integrated into Siemens' recently launched Intelligence Center X, supports agent creation and orchestration in its enterprise industrial AI environment. The self-verifying AI workflows enable autonomous and long-running EDA AI agents to continuously validate their decisions against proven engineering tools. It allows the agent to monitor a workflow, inspect the results, change its configuration, and run other operations while checking its progress against established EDA engines. This is what Siemens calls self-verification. The language model does not, by itself, determine whether a design is correct. Instead, the agent uses simulation, verification, and physical design tools to produce the engineering evidence for the next decision. For example, library characterization using Siemens' Solido Characterization Suite with agentic AI can reduce characterization turnaround by more than 10x and token cost by 5x to 10x, according to the company. These agentic workflows automatically generate and verify Liberty files via Solido Characterizer with Solido LibSPICE, Solido Generator, and Solido Analytics. The journey toward self-verifying agents. At DAC 2025, Siemens introduced the Fuse EDA AI System, which provided the data and model infrastructure to use generative AI with EDA data. The Fuse EDA AI Agent, announced at Nvidia GTC in March 2026, added an execution layer. The supervisory agent can break an engineering objective into tasks, delegate work to specialized agents, and connect to EDA applications through Model Context Protocol (MCP) interfaces. This new expansion, released at DAC 2026, adds long-running agents that can keep working after the first tool call. For example, an agent can launch a simulation, inspect the returned logs, identify a failure, modify the setup, and try again. The Fuse EDA AI Agent translates an engineer's objective into a plan and the specialized agents select tools and execute the steps. Extending the agentic AI workflows with Nvidia's Nemotron 3 Ultra model provides reasoning for planning and multi-step execution, while the Nvidia NeMo Gym open library is used to evaluate agent behavior to improve results and optimize workflows. It also leverages Nvidia OpenShell secure runtime to provide a secure environment for running the agents. But the engineering result comes from Siemens' EDA tools. For instance, a model may decide which SPICE analysis to run or if another process, voltage, and temperature (PVT) corner is needed, but the simulator produces the electrical result. "The goal is not to replace physics with AI," said Timothy Costa, general manager for industrial and computational engineering at Nvidia. The objective is to connect AI reasoning with accelerated simulation and physics-aware models and keep trusted engineering tools as the source of validation, he added. How a long-running agent handles library characterization. Library characterization shows why this type of agent can be useful even when the underlying simulations are already automated. In the case of a standard-cell, memory, or custom-IP library, it has to be characterized across different operating conditions before its timing and power models can be used. Engineers first configure the library and run preliminary simulations to confirm that the environment is working correctly. Amit Gupta, senior vice president and general manager of Solido Custom IC and chief AI strategy officer, at Siemens EDA, Siemens Digital Industries Software, described this process as "pipe cleaning." If simulations fail because of an incorrect setup or convergence issue, an engineer has to inspect the result, change the configuration, and rerun the task. Once the setup is stable, the full characterization run begins. Engineers continue to monitor failures, review outliers, and decide if additional PVT corners need to be simulated before the Liberty models can pass quality checks. "It comes out to about a four-to-six-week process for a library," Gupta said. The Solido Characterization agent automates much of this supervisory loop. Gupta said an engineer can initiate the process with a single prompt. The agent can launch hours-long runs, monitor them, react to failures, change configurations, rerun jobs, and perform final quality assurance steps. The workflow combines Solido Characterizer, Solido LibSPICE, Solido Generator, and Solido Analytics. Characterizer manages the characterization tasks, LibSPICE runs the circuit simulations, Generator produces the Liberty models, and Analytics examines the results and supports outlier analysis. The productivity gain comes from reducing the manual work between simulations. Nvidia CUDA-X libraries can also accelerate numerical operations used by the simulation and physical verification tools to reduce the time between an agent's action and the feedback it needs for the next decision. Nvidia's PhysicsNeMo open-source PyTorch framework for physics AI models provides another layer where physics-aware models can give the agent fast estimates to explore a design and decide where a higher-fidelity simulation is required. But Costa said those models are not a substitute for engineering validation. According to him, trusted solvers, test data, and domain-specific criteria are the final references. Cross-domain autonomy remains the larger engineering challenge. The Solido characterization example is a workflow with well-defined inputs, tools, and acceptance checks. Extending the same autonomy across several EDA domains is more difficult. Gupta said Siemens is currently focused on long-running agents within individual areas such as custom IC design, digital verification, design-for-test, and Calibre physical verification. "What we see as the next step is being able to stitch those product families together," he added. Siemens has demonstrated an early example, where Calibre detects a design rule violation, and the agent invokes Aprisa semiconductor software for a digital physical design repair or Solido for an analog repair. This moves the agent from identifying a problem to changing the design, and it also creates new verification requirements. Fixing a local design rule check violation can create a timing, congestion, or signal integrity problem somewhere else in the design. The challenge is more than exposing additional applications through MCP. Even a cross-domain agent has to understand how different representations of the same design correlate to one another. Gupta explained that Siemens is developing EDA ontologies that map relationships between different domains and improve the models' ability to understand the different data types. This context is necessary because a change in one stage may invalidate results elsewhere. Human judgment remains part of the design and verification workflow. When an agent repairs a design rule violation, it can create another physical problem, he added. Engineers therefore need to observe the agent's actions, pause or stop execution, and redirect the workflow. Near-term deployment of this capability will remain domain-scoped and supervised. But more full-scale autonomy comes when EDA companies can preserve engineering intent, while agents can move across multiple tools, data models, and verification steps. This shows that agentic EDA is not replacing the physics-based tools used to design and verify chips. The agent can keep the workflow moving and decide what should happen next, while the deterministic EDA engines and human engineers determine if the results can be trusted. The expanded EDA AI capabilities will be available in upcoming releases of Siemens' AI-native EDA portfolio. VIRTUAL events. Industry summits * | Electronica Forums 2026 * | Arrow New Space Technology Summit 2026 * | Autonomous Agriculture Technology Summit 2027 EXPERT-LED webinars. ON DEMAND or Live * | Edge-first Architectures for Building and Campus Safety - EE Times * | Lower Risk, Save Costs with Certified WiFi & BLE Modules - EE Times * | Power Distribution Systems Electrification Evolution - EE Times Featured WHITE PAPERS. Technical Resources * | Loop Compensation and Decoupling Design with "The Loop Compensator" - EE Times * | Selecting the Correct Level of DC-DC Converter for Your Application - EE Times * | The Future of Open Cable Systems: Conditional Access Migrates to DCAS - EE Times