Trust and transparency
Our editorial methodology
This page explains how ScanRacer turns photos, data, and automotive research into useful answers while clearly stating the limits of AI.
1. Observe and collect
Visual examples come from photos actually published by members or illustrations whose origin is disclosed. A public photo alone is not enough to prove a trim, year, or engine.
2. Cross-check information
Indexable pages must include several substantial sections and at least two references. For specifications, we prioritize manufacturer sources, official documents, and established databases, then separate facts from visual observations.
3. Frame artificial intelligence
The scanner returns a likely prediction, not a mechanical assessment or administrative proof. Quality depends on framing, light, angle, visible details, and whether the model exists in the training data.
4. Review before indexing
Content may be prepared with automated assistance. Library pages are submitted for indexing only when published, sufficiently complete, consistent, and sourced. Drafts and weak pages stay out of the sitemap.
5. Correct and update
An error can be reported with the relevant URL, the passage to correct, and a supporting source. We then review the request, update the page when necessary, and display a modification date where the format supports it.