A large language model reads requirement documents in depth to extract test points, flag duplicates automatically and generate test cases in one click, connecting requirements to testing and addressing scattered requirements, disordered management and slow alignment.
Time spent organising, aligning and completing requirements is cut by more than 70%, removing manual cross-checking.
Detects ambiguous requirements and flags duplicates automatically, avoiding invalid tests and wasted effort.
From requirement parsing to case generation in a single step, shortening preparation time at the start of a project.
Deployed at a joint-venture OEM: requirement analysis and detailed test cases in just over 10 seconds, with a mind map of the requirement flow generated at the same time.
A large language model serves as the reasoning core, tuned on automotive R&D and testing corpora to support requirement parsing, root cause reasoning and code generation.
Brings together ZD simulation knowledge, historical fault cases and diagnostic experience with retrieval-augmented generation, so output matches real engineering contexts.
Supports local and private-cloud deployment so R&D data never leaves the company, meeting OEM data security and compliance requirements.
Agent output connects directly to test execution, simulation scheduling and ticketing systems, so results flow automatically and are tracked to closure.
Requirement analysis and detailed test cases in just over 10 seconds, with an automatic requirement mind map for faster reading. The LLM and RAG knowledge base run on-premises so data stays secure.
Case generation accuracy of 95%-98% after tuning, in production use for up to 21 months and savings of up to 16 person-months per customer.
ZD Technology · AI agents for vehicle R&D and testing