Prognostics Maintenance powered by Cortex AI

Estimates RUL using advance ML/DL models
Agentic intelligence recommends maintenance actions
Seamless Integration with legacy systems
Reduces AOG exposure
Mitigates SCM bottlenecks

Challenge

Unscheduled shop visits cost 2–3× more than planned, often $1.5M–$3M per event. These reactive interventions drive avoidable engine removals, higher labor intensity at premium rates.

Operational & Financial Losses

Unplanned downtime results in $50K–$150K in direct costs per disruption
Indirect losses in the form of aircraft parking, crew repositioning, lease expenses
Adversely affects passenger experience & airline public image

Parts & Supply Chain Constraints

Limited visibility into actual degradation drives rush procurement
Leads to premium pricing and extended AOG times
OEM bottlenecks, PMA constraints, and scarce USM inventory further delay maintenance and amplify disruption

Approach

Holistic RUL Modeling

Leverage advanced ML/DL models that incorporate a full spectrum of operational, historical, and contextual data.

Agentic Intelligence for Action

Translate RUL signals into recommended maintenance actions, ideal removal windows, and part provisioning requirements.

Seamless System Integration

Feed RUL + agentic outputs directly into Flight Plan Manager, Demand Management, and Inventory systems to align planning, supply, and operations.

Solution

Predictive Maintenance Driven by RUL Intelligence

Agentic & NLP-Enabled Maintenance Experience

Engineers can ask natural-language questions ("Why is this engine flagged?", "What parts will we need next week?", "Show upcoming high-risk segments").

End-to-End Operational Alignment

Results

50%

Reduction to Unscheduled Maintenance Events

25%

Improved Fleet Availability

30%

Lowered Maintenance Costs

Reduce Current Wait Time by 1/2

Ready to Implement Predictive Maintenance?

Let our AI experts help you transform your maintenance operations with predictive analytics. Get in touch today to discuss how we can reduce costs and improve fleet availability.