Private AI for reliability engineers.

Find the failure pattern before deciding the maintenance interval

Distinguish a load change from a developing fault, compare similar operating regimes and estimate risk only where the failure history supports it.

Inputs: Asset-specific vibration spectra, bearing temperatures, motor current, operating load and confirmed maintenance outcomes.

From observation to completed task

Create an evidence-linked reliability case and compare inspection intervals against downtime and maintenance cost.

Draft a failure-review agenda from approved records

The supporting records include maintenance histories, authorised failure reports and engineering notes.

Engineers validate causes and reliability calculations.

The systems involved

Time-synchronised sensor histories, confirmed fault labels and a CMMS connector.

SOS AI configures the local models and tool permissions for this workflow. Actions in business software follow the authority you approve; uncertain cases and actions outside those limits go to the responsible person.

What a useful result must get right

Backtest warning lead time, missed faults and nuisance alerts by asset; remaining-life estimates need uncertainty ranges.