Traditional radiographic interpretation faces fundamental limitations.
Radiographic evaluation depends on individual inspector experience, leading to inconsistent results across assessments.
Traditional methods lack measurement uncertainty, making risk-based decisions unreliable.
Manual record-keeping creates gaps in audit trails and makes historical trending impractical.
Four integrated modules deliver quantitative, uncertainty-aware inspection results.
Physics-informed neural networks reduce noise and optimise contrast while preserving measurement-critical features.
Deep learning models localise and classify defect indications across standard defect categories.
Physics-informed 3D reconstruction from 2D radiographic projections for volumetric analysis.
Compute depth, wall loss, and affected area with traceable uncertainty estimates.
Seven-stage AI pipeline from upload to validated report.
Every measurement includes uncertainty bounds for risk-based decision-making.
Consistent results with full audit trail and versioned AI models.
Built-in review workflow ensures AI results are confirmed by qualified inspectors.
All inspection data stored in structured, searchable format with complete traceability.
RESTful API for integration with asset management and integrity systems.
Simulated for demonstration purposes
| Defect | Class | Severity | Confidence | Depth (mm) | Wall Loss (%) |
|---|---|---|---|---|---|
| D-001 | CORROSION | HIGH | 94% | 3.2 ± 0.4 | 25.2 ± 2.8 |
| D-002 | POROSITY | MEDIUM | 87% | 1.8 ± 0.2 | 14.2 ± 2.1 |
| D-003 | CRACK | CRITICAL | 91% | 5.6 ± 0.7 | 44.1 ± 3.2 |
All data encrypted at rest and in transit
Role-based permissions with full audit trail
Multi-tenant architecture with strict isolation
Every action logged with timestamp and user
Start with a 30-day free trial. No credit card required.
For inspection teams
30-day free trial
Custom deployment