Every changeover resets your inspection.

High-mix, low-volume manufacturing pushes traditional quality systems to their limits. Each product run is short, each design is unique, and every changeover resets the inspection process. Machine-vision systems built for high-volume lines require fixtures and algorithm tuning that can take weeks — often longer than the production window itself.

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Key pain points

Setup overhead

Configuring hardware and algorithms consumes valuable time before any inspection can begin.
Inconsistent outcomes

Results vary by operator and shift, introducing variability at every changeover.
Documentation burden

ISO 9001 and AS9100 require detailed traceability records that manual processes cannot reliably produce.

Industries

Aerospace composites
Wind turbine blades
Precision machined components
Electronics assembly
NDT / surface inspection

Use case 1

Assembly correctness checking

The operator moves to the assembly, identifies it by scanning a QR code, and captures images zone by zone. The system checks each zone for missing components, misplacement, orientation errors, and foreign objects — at the part, not at a station.

Detection modes

Missing component

Flags absent parts against the authorized assembly configuration.
Extra / unwanted component

Detects parts present where none should be.
Misplaced component

Identifies correct parts in wrong positions.
Misaligned component

Catches incorrect orientation or rotation.
FOD / surface defect

Finds foreign objects, scratches, burrs, cracks, and pinholes.
Hardware & wiring

Verifies presence and placement of fasteners, brackets, connectors, tubing, wiring harnesses, panels, and seals.

Performance

±10 mm

Positional accuracy
±5°

Rotational accuracy
20″ – 13′

Assembly size range
~20″ × 36″

Capture zone per tile
100

Maximum inspection zones per session

Use case 2

Marking & labeling inspection

Parts are presented to the camera one at a time. The system reads and validates each marking against the production documentation, and the operator generates a report once the batch is complete.

Inspection scope

Presence / absence

Confirms expected markings exist and no unauthorized marks are present.
Text verification

Validates serial numbers, stamps, and labels against reference values. Supports multi-field validation per part type.
Legibility check

Flags marks that are present but unreadable.
Color coding

Verifies color-coded labels match the expected configuration for the part type.
Marking types

Batch numbers, part numbers, acceptance stamps, dates, packing labels, and custom labels.
Marking methods

Engraving and ink jet on metal and plastic, printed text on paper, handwriting.

Performance

3 mm

Minimum character height for reliable text recognition
< 5 s

Pass / fail result per marking zone
2″ – 8′

Part size range
4″ – 36″

Working distance from part surface
800 lux

Recommended ambient lighting

IT architecture

Three components.

Designed for offline-first operation, on-premise deployment, and zero reliance on external cloud infrastructure.

1

iOS application

Self-contained app with all detection and validation logic on-device. Stores sessions locally and syncs to the backend on reconnect. Distributed via Apple Business Manager.
2

Archive API & storage

Ingestion and storage service on a Linux VM within your infrastructure or Spiral-managed cloud. Persists sessions indexed by batch, timestamp, and part.
3

Configuration & audit portal

Browser-based read-only interface for reviewing archived sessions. Filter by batch, part type, status, and date. Export for documentation and audits.

Integration

Connects with ERP, QMS, PLM, MES, and BI platforms via REST API
Role-based authentication, HTTPS, multi-site sync
Exports: JSON, CSV, image packages

Technology

Layered vision model cascade

All inference runs on-device via CoreML — offline-capable, low latency. Models are trained on customer-specific data collected during setup and delivered as silent app updates through Apple Business Manager.

1

Part identification

Detect and classify all visible components in the camera frame.
2

Assembly configuration validation

Confirm all expected parts are present and correctly positioned relative to the reference design.
3

Surface region segmentation

Identify and isolate surface regions relevant to anomaly detection.
4

Surface anomaly detection

Flag features consistent with damage, foreign objects, or surface defects.
5

Marking & text recognition

Validate serial numbers, stamps, and printed labels using OCR and pattern matching.
6

Final validation

Combine all checks into a single pass / fail / inconclusive result with full traceability.