Reliable context for vehicle research

Auto Reliability Intelligence helps shoppers and owners understand patterns in publicly reported automotive problems. Our goal is to turn large public safety datasets into clear, model-year-specific information without overstating what the data can prove.

Our data methodology

We ingest complaint records from NHTSA’s public complaints API, validate the source response, and aggregate records by make, model, and model year. We classify reported components, count crash and fire indicators, and retain failed records in a review queue rather than silently discarding them.

How the Reliability Score works

The score is a cohort-relative 1–10 signal. We annualize reported complaint, crash, and fire counts for a vehicle’s age, apply extra weight to crash and fire indicators, and rank the resulting signal against the vehicles currently indexed. A lower score means a higher relative level of documented reports in this dataset; it is not a mechanical diagnosis, a failure rate, or an estimate of the probability that an individual vehicle will fail.

Updates and corrections

Our scheduled ingestion process refreshes selected vehicle records at least every six hours. We welcome evidence-backed corrections to editorial presentation or data interpretation through our Contact page.