Name: Spiral Roboscope
Brand: Spiral
Availability: InStock

AR-powered spatial inspection platform for large industrial parts. Built for wind turbine blade QC; expanding to construction and shipping.

Product overview

AR-anchored defect records.
Directly on the part.

Roboscope replaces paper logs and manual measurement with spatial AR markers anchored directly on physical surfaces. Built for wind turbine blade QC; expanding to construction and shipping.

Read the full technical overview →

AR defect markers anchored on physical surfaces (~5 cm accuracy)
Capture type, location, and dimensions in a single session
Session auto-registration via work task router scan
Direct ERP / QC system integration — no manual re-entry
Built-in edge case escalation — replaces ad-hoc email chains
ML vision models for defect types, structure, and paintwork

Industries

Wind turbine blades
Large aerospace structures
Marine & shipbuilding
Construction
NDT on large surfaces

AR spatial marker system

Place defect markers directly on physical surfaces

Using iPhone AR, inspectors anchor digital defect markers at the exact physical location on the part. No manual measurement, no coordinate entry — position captured spatially in a single session.

~5 cm

AR placement accuracy on surfaces
Single session

Full defect registration without re-measurement
iOS native

Native iPhone application, optimised for handheld use

Captured per defect

Defect type
Spatial location (AR anchor)
Dimensions
Photographic evidence
Inspector ID & timestamp

Layup Registration Table

Full layer breakdown at every defect location.

The Layup Registration Table maps every AR-anchored defect to the exact composite layers affected at that point on the part — giving engineers a precise cross-section view without disassembly.

1

Locate

AR anchor position is resolved against the part's layup geometry to identify the zone.
2

Layer lookup

LRT returns the ordered stack of plies at that coordinate — material, orientation, and thickness per layer.
3

Depth assessment

Defect depth is cross-referenced with the layer stack to determine which plies are affected.
4

Severity grading

Each affected layer is graded individually; structural plies trigger automatic escalation.
5

Export

Full layer-annotated defect record exported to ERP / repair workorder with ply-level detail.

LRT data per defect

Zone ID

Named layup zone resolved from AR anchor coordinates
Ply stack

Ordered list of plies with material, angle, and nominal thickness
Affected layers

Subset of plies intersecting the defect depth envelope
Repair class

Repair category derived from affected structural ply count

Layup Registration Table layer breakdown

ML intelligence

Purpose-trained models for complex defects

ML vision models cover the full defect spectrum for large industrial surfaces and wind turbine blades. Models are trained on domain-specific data and updated continuously from session evidence.