Know What's Breaking.
Before It Breaks Production.
Predictive AI detects equipment failures early — before they stop the line. Voice AI guides technicians hands-free. Root cause in minutes, not a shift-long guessing game. Unplanned downtime caught before it happens.
The Real Problem
Reactive Maintenance Is The Most Expensive Kind
Equipment failures are discovered when the line stops, not before
Predictive AI monitors vibration, temperature, and runtime patterns to flag failures 2–3 weeks before they happen
Technicians spend significant time searching for parts and documentation instead of fixing equipment
Voice AI pulls up schematics, torque specs, and parts lists hands-free while the technician works on the machine
Work orders are filled out after the fact — if at all — and the data is useless
Voice AI captures maintenance actions, parts used, and findings in real time at the machine
PM schedules are based on calendar time, not actual machine condition
AI adjusts PM intervals based on actual runtime, load cycles, and condition data — so you maintain what needs it, not the calendar
What We Deploy
Four AI Systems Built For Maintenance Teams
Predictive Failure Detection
AI monitors machine signals — vibration, temperature, current draw, pressure — and learns the normal patterns for each asset. Anomalies trigger alerts 2–3 weeks before failure, not after.
Voice-Guided Maintenance
Technicians describe what they're seeing and AI responds with the right schematic, torque spec, or parts list. Hands stay on the machine. Knowledge stays in the system.
Condition-Based PM Scheduling
AI analyzes actual machine condition, runtime hours, and historical failure data to recommend PM intervals that match reality — not the OEM's conservative calendar schedule.
Automated Work Order Intelligence
When AI detects an anomaly, it creates a work order, assigns it to the right technician, pulls the relevant history, and pre-populates the parts list — before the technician arrives.