Valeo and Zuken have announced a strategic partnership to create an advanced, open, AI-assisted electronic design platform through their joint "Zuken Valeo InnoLab" program. The partnership will combine Zuken's AI architecture and cutting-edge AI roadmap with Valeo's "AI Agents" and industrial expertise, with the goal of integrating AI across the entire design flow. The collaboration aims to significantly reduce design times and ensure robustness in electronic design by establishing an ecosystem in which the tool and the engineer collaborate in real time.

Using Zuken System Planner, Valeo leverages its generative AI capabilities to automatically generate and assess optimal multi-criteria electronic architectures in real time. The system evaluates design alternatives against Valeo’s engineering standards, enabling faster and more efficient architecture development. Built on Zuken’s open platform, the solution ensures seamless integration with Valeo’s broader engineering ecosystem while maintaining full digital continuity throughout the development process. This approach supports complete traceability and compliance with the Automotive SPICE 4.0 (ASPICE 4.0) Hardware Engineering process requirements. Valeo’s AI tools can analyze engineering data, generate insights, and automatically feed resulting actions back into the development platform.

Valeo is also deploying AI-powered virtual copilots, or “AI Agents,” to assist engineers throughout the detailed design phase. These agents provide real-time support for solution exploration, hardware rule verification, and design constraint management. At the same time, Zuken is developing native AI capabilities that accelerate schematic creation by leveraging Valeo’s standardized engineering databases.

For physical hardware integration, the workflow uses Zuken’s Design Force platform, which incorporates advanced AI-based placement and routing (AI-PR) algorithms. To achieve a “first-time-right” design approach, Valeo employs Zuken’s software development kit (SDK) to interact directly with the design environment and train the AI models using the stringent requirements and constraints specific to the automotive industry. This enables more accurate component placement, routing optimization, and design execution from the earliest stages of development.

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