- Inspection time
- ↓ 30–50%
- Defect detection
- ↑ 95%+
- Hazardous misses
- ↓ Zero
Outcomes
Key Features
Multi-Sensor Defect Detection
Ingest video, thermal, and LiDAR data from inspection cars. AI models detect rail cracks, gauge anomalies, fastener defects, tie degradation, and catenary wear. Runs in real-time on edge devices during route traversal—no connectivity required.
Geo-Tagged Findings with GIS Overlay
Every defect tagged with precise GPS coordinates and milepost reference. Findings overlay on ArcGIS, QGIS, or custom GIS platforms. Filter by defect type, severity, and date range. Route-level visualization for maintenance planning.
Automated Work Order Export to EAM
Defects auto-generate prioritized work orders in your EAM system—Maximo, SAP PM, Infor EAM. Priority scoring based on defect severity, traffic density, and maintenance history. Two-way sync for work order status updates.
Model Accuracy & QA Dashboards
Track detection accuracy by defect type and route. QA sampling workflows for ground truth validation. Confusion matrices, false positive rates, and model drift monitoring. Continuous improvement feedback loop.
RailAurex vs. Traditional Inspection
| Capability | RailAurex | Visual Inspection | Periodic Geometry Car |
|---|---|---|---|
| Inspection Speed | Track speed | 5-15 mph | Scheduled runs only |
| Coverage | 100% of route | Subjective sampling | Geometry only |
| Defect Types | Visual + thermal + geometry | Visual only | Geometry only |
| Detection Consistency | AI-consistent | Inspector-dependent | Limited scope |
| Real-Time Alerting | Yes, edge processing | Manual reporting | Post-processing |
| GIS Integration | Native | Manual entry | Batch export |
| EAM Work Orders | Automated | Manual creation | Separate workflow |
Technical Specifications
What it does
- Video/LiDAR ingestion; defect detectors
- Geo-tagged findings; GIS overlay
- Work order export to EAM
- Model accuracy dashboards
- Route-based inspection planning
How it works
Capture
Inspection cars collect video, thermal, and LiDAR data
Detect
Edge AI identifies defects in real-time during traversal
Locate
Findings geo-tagged with GPS coordinates and mileposts
Prioritize
Defects scored by severity for work order ranking
Dispatch
Work orders auto-export to EAM for maintenance crews
Capture
Inspection cars collect video, thermal, and LiDAR data
Detect
Edge AI identifies defects in real-time during traversal
Locate
Findings geo-tagged with GPS coordinates and mileposts
Prioritize
Defects scored by severity for work order ranking
Dispatch
Work orders auto-export to EAM for maintenance crews
Built for Rail Infrastructure of All Sizes
Class I Freight Railroads
Challenge
Thousands of miles, high traffic density, FRA compliance
Solution
High-speed inspection at track speed, priority routing for heavy-haul corridors, FRA reporting integration
Short Line & Regional Railroads
Challenge
Limited inspection resources, aging infrastructure, budget constraints
Solution
Cost-effective edge deployment, prioritized maintenance spending, do-more-with-less efficiency
Passenger & Commuter Rail
Challenge
Safety-critical, tight maintenance windows, public accountability
Solution
Night-run inspections, rapid turnaround reporting, public safety documentation
Transit Authorities
Challenge
Urban environment, electrified third rail/catenary, frequent service
Solution
Catenary and OCS inspection, third rail monitoring, integration with transit asset management
What you get
- Model set + threshold book
- GIS layer & route exporter
- EAM adapters (WO, status)
- QA sampling playbook
- Weekly inspection reports
Deployments & integrations
- Edge devices in inspection cars
- Batch processing on cloud/on-prem
- GIS integration (ArcGIS, etc.)
Security & governance
- TLS 1.3 for all data in transit
- RBAC with inspection/maintenance/manager roles
- Audit logs for all findings and work orders
- Evidence export for compliance
Related
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Frequently Asked Questions
Stop Guessing. Start Knowing.
RailAurex finds defects before they become derailments
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