Project Aurora is autonomous physical intelligence for industrial infrastructure. Computer vision and AI monitor machines, detect early warning signs, predict failure, and protect the people working alongside them.
Aurora's vision stack runs continuous spatial inference on every asset in view — detecting micro-deflections, surface fatigue, vibration anomalies and thermal drift that human inspections miss until it's too late.
The output isn't an alarm. It's a probability surface — where on the machine the next failure is most likely, with how much confidence, on what horizon.
Aurora fuses vision, acoustic, thermal, vibration, current, and operator context into a single asset-level health verdict — updated continuously, explained when challenged.
Continuous object-level segmentation of every asset. Identifies wear, missing fasteners, leaks, and PPE violations.
Microphone arrays trained on each asset's healthy signature. Aurora hears bearings degrade weeks before they fail.
Long-wave thermal imaging across every asset. Tracks micro-trends invisible to any single inspection.
FFT decomposition with per-asset frequency baselines. Anomalous bands lighten months before mechanical failure.
Sub-cycle current waveform analysis. Detects rotor faults, misalignment, and load anomalies in real time.
Shift logs, work orders, parts changes. The human context that turns a signal into a verdict.
A motor on Line A failed at 03:42 on a Tuesday. Here's what Aurora saw, in order. And what the alternative looked like.
Subtle 0.4% deviation in the 3.2 kHz band. Below human alert threshold. Logged.
Bearing housing trending 0.6 °C above 30-day baseline. Maintenance ticket auto-drafted.
Sub-millimeter glint detected near drive end. Confidence 0.91.
All four channels confirm. Aurora schedules replacement in the next planned window.
14-minute swap during a planned changeover. Asset back to baseline within an hour.
Unplanned 9-hour line stoppage. Cascading damage to downstream gearbox. Expedited parts. Lost shift output. Three injury near-misses.
Bearing cost. Planned-window labor. Zero stoppage. Zero injuries. The signal arrived in time.
Verified against retrospective maintenance records across pilot deployments.
Time from first signal to scheduled intervention window.
Measured against the previous twelve months of the same facility.
End-to-end response on the on-premise inference appliance.
Aurora deploys as an on-premise vision and sensor stack with cloud-side analytics. We work with operations and reliability teams to map assets, train baselines, and integrate with the CMMS and SCADA you already run.