TraceRoot ingests test logs, fault codes, and maintenance records, then uses AI to surface failure patterns and root causes that would take engineers days to find manually.
Your production line throws a yield drop. An engineer opens five systems, cross-references test logs against maintenance records, manually clusters failure modes, and maybe finds the root cause by Friday. Meanwhile, scrap keeps piling up.
TraceRoot does that investigation in minutes. Upload your data. Get answers. Ship fixes before the next shift.
Drag and drop test logs, fault codes, maintenance records, or measurement data. CSV, JSON, or raw text. No integrations required to start.
TraceRoot parses your data, clusters failure modes, correlates across stations and time windows, and identifies statistically significant patterns.
Get prioritized root causes with evidence trails. See which lots, stations, or components are driving failures. Know exactly where to intervene.
Ingest ICT, flying probe, and functional test results. Automatically cluster failures by component, station, lot, and time to isolate yield killers.
Surface hidden correlations across thousands of fault codes. Detect intermittent failures, fixture degradation, and process drift before they become systemic.
Analyze maintenance logs, diagnostic codes, and repair records to predict component failures and optimize service schedules across vehicle fleets.
Trace field returns and customer complaints back through production data to pinpoint exactly where quality escaped and why.
The platform that turns messy operational data into the engineering insight your team has been extracting by hand.