Flag defects from images, videos, and line cameras.
AI Vision Inspection Platform
Smarter visual inspection for defect-free production.
InspectionAI helps quality and operations teams detect defects, segment problem areas, classify severity, and generate evidence-rich inspection reports from real production images.
InspectionAI reviewDefect map ready
SegmentationRegion isolated
Reviewer queueUncertain regions prioritized
Outline affected regions for measurable visual evidence.
Route scratches, contamination, deformation, and custom issues.
Adapt models to your products, lighting, and tolerances.
Keep inspection images, confidence scores, and reviewer QA.
Platform
One connected pipeline for visual quality control.
InspectionAI supports the complete inspection lifecycle: data ingestion, annotation, segmentation, detection, classification, deployment, and ongoing production monitoring.
Annotation
Turn raw inspection images into governed training datasets with structured reviewer QA.
Segmentation
Isolate parts, surfaces, and defect regions before decisions are made.
Detection
Detect, categorize, and score defects against your quality specs.
Deployment
Push validated models into inspection stations, edge systems, or cloud workflows.
Inspection workspace
A cleaner inspection UI for upload, analysis, and review.
The earlier screenshots have been reinterpreted into a more professional UI system with clearer input modes, a focused inspection canvas, confidence scoring, defect taxonomy, and traceable reporting.
IMG_7814.JPGmulti-class defect review
Detection: 3 regions
Mask area: 12.4 mm²
TuneFocusZoom
Workspace Review
Upload sample images and see what InspectionAI would flag.
Share a small set of representative parts, surfaces, labels, or assemblies. INSAIT can walk through detected regions, segmentation masks, defect classes, and the review report your quality team would use.
Review Sample ImagesHow it works
From raw image to verified defect report.
The workflow keeps reviewers in control while giving quality teams a repeatable path from pilot data to production inspection.
Image captureReviewer QAModel release
01Data intake
Capture
Connect existing cameras, scanners, or sensors without replacing proven inspection hardware.
02Ground truth
Annotate
Human reviewers build governed ground-truth datasets with clear QA controls.
03Model build
Train
Segmentation and detection models learn from your parts, defects, tolerances, and edge cases.
04Quality gate
Validate
Benchmark models against held-out defect libraries before they touch production.
05Deployment
Deploy
Run inspection at the edge, in-line, on-premise, or in the cloud.
06Feedback loop
Monitor
Track accuracy, drift, throughput, and defect trends as production conditions change.
Pilot on your production data
Validate InspectionAI across parts, packages, panels, and devices.
Bring sample line images and define the defect classes, segmented regions, and reporting outputs your quality team needs to trust.
Request a Pilot ReviewDetect finds candidate defects or objects.Segment outlines the exact affected region.Classify assigns the issue type and priority.
Industries
Built for inspection programs across physical products.
Use cases can start with defect detection and expand into segmentation, categorization, measurement, drift monitoring, and automated reporting.
Electronics & PCB Assembly
Solder defects, missing components, alignment errors
Semiconductor Equipment & Wafers
Surface contamination, particle detection, dimensional tolerance
Automotive Components
Surface scratches, weld quality, dimensional conformance
Solar / PV Panels
Micro-cracks, cell discoloration, delamination
Packaging & Consumer Goods
Label placement, seal integrity, print defects
Medical Device Manufacturing
Seal integrity, burrs, particulate contamination, tolerance checks
About INSAIT Solutions
Build inspection models around your parts and defects.
INSAIT Solutions helps manufacturers turn real production images into custom machine learning models. We start with the parts, defect taxonomy, tolerances, lighting, and capture setup, then build a governed dataset and train models around the actual quality problems each industry needs to solve.
INSAIT model-building pathCustom datasets, trained models, and deployment support
01DatasetsProduction images from each line02Annotation QABoxes, masks, classes, edge cases03Model TrainingDetection, segmentation, classification04DeploymentEdge, cloud, dashboards, drift checks
Dataset and defect strategy
We define part families, image sources, defect classes, severity levels, and pass/fail rules before model work begins.
Annotation with QA controls
Reviewers label boxes, masks, classes, and edge cases with version history so training data stays consistent and auditable.
Models trained from scratch
We train and tune detection, segmentation, and classification models for your product surfaces, lighting, tolerances, and failure modes.
Industry-specific deployment
We help teams in electronics, semiconductors, automotive, solar, packaging, and medical devices validate performance and deploy with confidence.
Solution Design
Need a custom inspection model for your production line?
INSAIT Solutions can help scope the dataset, annotation plan, model architecture, validation targets, and deployment path.
Resources
Practical guidance for inspection teams moving from pilot to production.
Resources will give quality, operations, and engineering teams a clearer path for planning datasets, defining defect classes, validating models, and connecting InspectionAI into real inspection workflows.
Blog
Inspection ML Notes
Short technical articles for teams planning inspection AI programs: defect taxonomy, annotation QA, validation metrics, model drift, lighting control, and dataset readiness.
- Defect-library planning
- Annotation and reviewer QA
- Model validation guidance
Case studies
Production Quality Stories
Anonymized manufacturing examples that show how inspection use cases move from sample images to trained models, pilot validation, and line-side quality review.
- Pilot-to-production examples
- Industry-specific defect classes
- Quality team decision flow
Documentation
Integration Guides
Implementation references for connecting InspectionAI to image capture systems, edge devices, cloud workflows, reporting dashboards, and plant quality systems.
- Camera and folder intake
- Edge or cloud deployment
- MES, QA, and report handoff
Request a Demo
See it work on your images.
Bring a handful of sample inspection images and the INSAIT Solutions team can show how InspectionAI detects, segments, categorizes, and reports quality issues.
General inquiriescontacts@insaitsolutions.com