Automotive AI evaluation
Find where your automotive AI fails before you expand it.
Scope a focused evaluation around a dealership task. Define success, test representative cases, and use qualified review to understand failures that matter.
One task. A clear test. A decision you can make.
Start with a defined workflow and baseline system version. Agree a test set and scoring rubric, run the system, and have qualified reviewers assess outputs. The final report separates task completion, critical errors, and limits of the sample.
A useful first evaluation
A service-booking and handoff evaluation can test whether an agent confirms only what the operating rules and available tools support, asks for missing information, and escalates requests it cannot complete. Broader tasks are scoped separately.
Measures need a decision context.
Possible measures include task completion, rule adherence, appropriate escalation, record accuracy, and critical errors. A benchmark describes defined test performance. It does not establish safety in every situation or guarantee production results.
Training on evaluation examples changes what later scores can mean. Holdouts and reuse rules should be agreed before testing.