PROBLEM
Factory challenge
Rare defects, label cost, equipment variation and model ownership block production deployment.
A · VISION AI
AI & Machine Vision
Keep existing cameras and equipment while connecting inspection, deployment, data and reporting in one loop.
APPROACH
OPTO approach
Combine hybrid AI, low-data learning, edge deployment, dataset operations and quality reporting.
PERFORMANCE · target metrics
99.9%
Inspection accuracy
Low-data Hybrid AI · target
<50ms
Inference latency
Real-time edge GPU
90%
Less labeling time
Synthetic defects · auto-label
24/7
Continuous operation
Continual learning · drift
※ Figures are planning assumptions, agreed per sample and site conditions via PoC gates.
Vision Pipeline
From camera input to quality report — one inspection loop
Capture and preprocessing through deep-learning/VLM inference, decision and SPC reporting run as one traceable pipeline.
-
01 · CAPTURE
Capture · lighting
2D/3D, thermal and HSI cameras with lighting alignment for reproducible input.
-
02 · PREPROCESS
Preprocess · align
Denoise, register and ROI-extract to standardize model input.
-
03 · INFERENCE
AI inference (DL · VLM)
Deep learning, vision-language models and anomaly detection produce defects and evidence.
-
04 · DECISION
Decision · threshold
Tolerances, thresholds and rules separate over/under-detection for the final verdict.
-
05 · REPORT
Report · SPC
Verdict evidence, statistics and lot tracking recirculate via QualityOS.
SOLUTIONS
Representative solutions
Traceable inspection operations from camera input to quality reporting
InspectAI
Low-data hybrid AI visual inspection
Combines rule-based logic and Hybrid AI to validate defect decisions while preserving evidence, dataset lineage and production operating criteria.
Learn more
VisionOps
Edge deployment, model operations and continual learning
Manages device, recipe, model and deployment versions so approved releases, rollback and performance review can be operated consistently.
Learn more
Fusion3D
2D/3D, thermal and spectral sensor-fusion inspection
Combines complementary sensors to distinguish shape, material and thermal characteristics that a single sensing method cannot reliably separate.
Learn more
DataForge
Synthetic defects, assisted labeling and dataset management
Supports rare-defect development while preserving the source, label, usage rights and training lineage of manufacturing datasets.
Learn more
Quality Copilot
Evidence-linked quality analysis and reporting assistant
Uses inspection, metrology and process history to suggest root-cause candidates and the evidence that a quality engineer should review.
Learn moreWhy our Vision AI
Vision AI that holds up on low data
Even where manufacturing data is scarce, synthetic data and Hybrid AI reliably learn rare defects. AI verdicts must be explainable and keep improving in operation.
- Learn rare defects with synthetic data & auto-labeling
- Explainable verdicts via VLM evidence
- Edge continual learning & automatic drift control
- Human-in-the-loop verification
Vision & AI Stack
Vision & AI technology stack
Input · Sensors
Industrial 2D cameras3D line-scan · structured lightThermal imagingHyperspectral (HSI)
Models · AI
DL classification · detectionVision-language models (VLM)Anomaly detection3D shape registration
Data
Synthetic defect generationAuto-labelingActive learningDataset versioning
Deploy · Operate
Edge GPU inferenceContinual learningDrift managementModel versioning · rollback
PROOF & DELIVERABLES
Verification deliverables that remain
Acceptance is based on the documents, data and procedures needed for approval and operation.
- 01Sample evaluation report
- 02Inspection recipe and model version
- 03Edge deployment image
- 04False-reject and false-accept analysis
- 05Operating runbook
INTEGRATION
Integration scope
- Industrial cameras
- 2D/3D, thermal and spectral sensors
- PLC triggers and I/O
- MES and quality databases
- VisionOps
VISION AI
Design a verification path for your site
Specifications and performance are agreed through sample and site-specific PoC gates.