Orb — Analyze
From imagery to intelligence. At scale.
Purpose-built computer vision models process inspection imagery automatically — identifying faults, classifying severity, and surfacing structured findings. Analysts focus on exceptions and decisions, not volume review.
Hero: fault detection interface — annotated inspection image with classification panel open (fault type, severity, confidence score, asset linkage, geolocation).
AI App Analysis
Purpose-built inspection models detect and classify faults across your full imagery dataset — automatically, without manual triage. Models are trained on infrastructure-specific asset classes, not generic computer vision datasets repurposed for infrastructure.
- Automated fault detection across full inspection image set
- Asset-class-specific model selection per inspection type
- Fault type classification and severity rating
- High-volume batch processing with structured output
- Detection confidence scoring per finding
Analysis pipeline: Inspection imagery → AI App model processing → Structured findings output.
Insight Results
Every finding structured: fault type, severity classification, image reference, geolocation, and asset linkage — in a single record. Immediately reviewable, filterable, and actionable. No unstructured reports. No manual result compilation.
- Structured finding record per detected fault
- Severity classification and prioritization
- Image and geospatial reference per finding
- Asset linkage for downstream reporting and integration
- Filterable by severity, asset class, location, and cycle
Findings list: Fault Type, Asset, Location, Severity, Inspection Date, Status.
Fault & Defect Annotation
Analyst review and annotation integrated into the workflow — not handled separately. Confirm, reclassify, add notes, and escalate findings from within the Orb interface. Every review action logged and auditable.
- In-platform annotation and reclassification
- Exception flagging and escalation workflow
- Analyst notes and evidence attachment per finding
- Full audit trail of review actions and decisions
- Configurable review workflow by severity or asset class
Model Development
Build, train, and deploy custom inspection models within the Orb AI App development environment — against your specific asset classes, fault types, and environmental conditions. Models improve with your data and deploy directly to production workflows.
- Custom model development within Orb
- Training on your labeled asset imagery
- Performance validation and benchmarking
- One-click deployment to production inspection workflows
- Version control and rollback capability
- Shared AI App library for common asset classes
AI App development environment: model training panel, performance metrics (precision, recall, F1), deployment controls.
How it fits into the Orb workflow
Vision Analytics receives structured media from Autofly and produces structured findings that feed directly into Review, Reporting, and enterprise system integration.
Workflow position
- Plan
- Capture
- Analyze
- Review
- Act
Outcomes
- Shorter time from capture to insight — imagery processed automatically; analysts receive structured findings, not raw images
- Shorter decision cycle time — from anomaly detection to remediation, with evidence already attached
- Higher data completeness — every image reviewed, every finding recorded; no backlog, no missed assets
- Lower cost per inspection — analyst time focused on exceptions and decisions, not volume review
- Better defect detection rate — consistent model application outperforms manual review at scale
- Compliance-ready findings — every result traceable, structured, and exportable for regulatory documentation
