Engineering · Trace + Timeline

AI-powered trace decoding.

Drop a trace file. Get decoded signals, timeline plots, and anomaly flags — automatically. The same engine that powers Wayne's incident analysis.

15-second window · DPF anomaly auto-flagged

trace_2026-05-04_14-22.dbc.applied 15.0s · 4 signals · 1 anomaly
ENGINE_RPM
COOLANT_T
DPF_PRESS
EGT
! DPF_PRESS · ML-flagged anomaly at t = 11.4s · σ = 4.7

From raw bytes to flagged anomaly — automatically.

Auto-decode

Apply DBCs in one click. Drop a CAN trace, blf, or ASC file; get every frame resolved into named signals, units, and engineering values. The decode is cached and addressable via the API.

Multi-signal overlay

Plot any combination of signals on the same timeline. Compare RPM, coolant temp, DPF pressure, and exhaust gas temp on one view — see correlations the raw frames hide.

Statistical + ML anomaly flags

Standard deviation, rolling-window outliers, and an ML model trained on fleet-wide trace history. Anomalies are flagged with confidence and a context window — not raised as bare alerts.

Wayne integration

Every flagged anomaly becomes a context fragment Wayne can reason over. "What was happening at t = 11.4s on truck FT4-309?" gets a real answer, grounded in the actual decoded signals.

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