ZF

Solution

ZF uses in-house AI across its engineering lifecycle rather than in a single standalone app. A pilot run by the AI Tech Center ingests customer requests and requirement specifications, analyzes them for recurring or similar requirements, and surfaces related code, test specifications and tools so engineers in different divisions can reuse prior work and estimate new projects faster. In later development stages, AI methods are applied to simulation, testing and validation: AI agents orchestrate validation workflows, while machine-learning models act as non-linear approximators when enough data is available. ZF also combines hardware tests with AI evaluation to create Virtual Sensors that infer temperatures, speeds and positions that cannot be measured directly, and the cubiX control software uses machine learning for predictive trajectory control in chassis dynamics. The source does not name an external platform or dedicated data store; prior specs, code and measurement data are the working information base served back to developers and control functions.

Data flow

ZF feeds customer requests and requirement specifications into an internal AI pilot that detects recurring requirements and retrieves related code, test specifications and tools for engineers across divisions. In validation use cases, hardware-test measurements and simulation inputs are processed by ML models and AI agents to estimate hard-to-measure states and orchestrate validation; outputs are then consumed by developers and by control software such as cubiX.