The agent parses vehicle logs and test data of all kinds, identifies abnormal signals, matches historical fault cases and pinpoints root causes, shifting teams from reactive troubleshooting to proactive warning.
Problem analysis drops from 3 hours to 30 minutes, locating root causes in minutes.
Handles CAN/CAN FD, Ethernet and LIN data as well as DTC fault code decoding.
Links historical faulty versions with their solutions and builds a company-specific diagnostic knowledge base.
Together with ZD Datalogger logging hardware, signal bug localisation falls from hours to minutes.
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.
Covers 87% of common cockpit faults. Handling time drops from 4-8 hours to under 30 minutes, engineer productivity rises about 4x and knowledge reuse increases by 60%.
BLF, ASCL and MAT logging files feed the AI engine automatically for timing anomaly detection and value-range compliance checks: 90% detection for common signal bugs and 90% for suspicious signals, with localisation down from hours to minutes.
ZD Technology · AI agents for vehicle R&D and testing