Aerospace
Predict problems sooner, prescribe solutions faster, and keep aircraft flying.
Component wear is predicted per tail, so maintenance slots, spares, and crews are planned against real remaining life instead of flat intervals. Every decision is traceable back to the sensor history, maintenance record, and airworthiness rule behind it, and the same graph that plans the work tracks the parts that will be needed to do it.
In the news
Avathon in action
Predictive maintenance
We diagnose and prescribe detailed information in highly complex industrial environments through processing real-time sensor data. Degradation is caught before failure, traced to its root cause, and returned as a repair recommendation weighted by technician skill, schedule, parts availability, and economics. Planning covers spares, consumables, people, and logistics, so downtime shrinks across the whole repair network.
Autonomous supply & quality
Demand is forecast, supplier capacity assessed, and anomalies in part quality or delivery flagged as they appear. Provenance, supplier performance, and tariff exposure are monitored continuously, and alternate parts are qualified through intelligent clustering.
Workforce augmentation
AI-guided diagnostics and a contextual digital twin put the documentation at the point of work. Retiring experts’ knowledge is captured and codified, so new technicians reach productivity faster.
AOG network repair
Agents coordinate parts, people, and repair actions to recover from aircraft-on-ground events — weighing cannibalization, local inventory, and supplier expedites — and return the aircraft to service with minimal fleet disruption.
Fleet sustainment & obsolescence
Obsolescence is managed proactively, tracked down to the tail-number bill of materials. Scheduled work is optimized and unplanned maintenance answered with parts availability already in view.