OBD diagnostics, catalogues, commerce and motorsport: examine what your AI understands, claims and recommends in its intended context.
The evaluation concerns the responses and behaviour of AI within your product. It documents their consistency with professional references, their limitations and the role of human control.
Compare explanations of OBD codes and data with the supplied information. Observe whether AI distinguishes a clue from an established cause, identifies missing data and explains its limitations without presenting a hypothesis as a certain diagnosis.
Evaluate parts indexing, stated compatibility and catalogue entries’ compliance with the supplied rules and reference documents. Examine sales and showroom descriptions: versions, characteristics, exclusions and missing information.
Examine AI analysis of motorsport driving techniques: racing lines, braking and acceleration. Compare comments with available telemetry and the stated context, without presenting an estimate as a measurement.
A domain benchmark provides a reference for comparing your AI’s versions on agreed scenarios. The report brings together findings, evidence, limitations and recommendations. The method explains the framework; required resources, including those for a robot, are defined before testing.
The profile of founder Jean-David Jaïs presents his self-reported automotive experience. The reviewer and scope are confirmed for each assignment.
OBD interpretation, catalogues, sales or telemetry analysis: describe the AI feature and its context. The scenarios above illustrate possible uses and do not constitute test findings.
Light provides a remote evaluation; Full adds testing on site. Read about the Light and Full formats; conditions are confirmed in the quote.
Describe the use case without third parties’ confidential information or passwords. Read the privacy policy before submitting your enquiry.
Describe your product and the analysis you want reviewed. A sample PDF report is available on request.