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Visual Inspection AI

Quickly train and deploy AI models to automatically detect production defects—no technical expertise required.

  • Start quickly with a guided user interface on Google Cloud or our API

  • Train high-precision, domain-specific AI models to detect the tiniest defects

  • Minimal labeling effort due to active learning; start with only a few defect images

  • Run prediction models at the production line through Docker containers


Run on-premises

Deploy high-performance inspection models at the network edge or on your factory floor.

Short time to value

Deliver significant ROI by reducing inspection costs, rework, and scrap and improving key quality metrics (e.g. escape rate, overkill rate, and yield).

Superior AI technology

Our top-ranked computer vision and machine learning technology lets you take on the most challenging inspection tasks.

Key features

Purpose-built, deep learning algorithm-based models for high-precision manufacturing inspection

Assembly inspection

Detects even the most subtle defects at various stages of the assembly process (wrong, misplaced, missing, rotated, or deformed components).

Assembly inspection

Cosmetic inspection

Locates even tiniest and most complex defects (dents, scratches, cracks, deformations, etc.) on any kind of surface.

Cosmetic inspection

Scale on-premises

Run models right on your shop floor with easily deployed Docker containers. Models are trained to meet your production quality requirements for escape and overkill rates.

Scale on-premises

Quick start capability

Start building models with only a few labeled images. Active learning will automatically suggest additional images for the operator to label and further improve the model’s performance.

View all features
Foxconn logo

"We cannot wait to roll the Assembly Inspection solution further across our extensive PCB manufacturing operations."

Sabcat Shih, Global Lead for Visual Inspection, FIH Mobile

Use cases

Explore common uses for Visual Inspection AI

Use case
Manufacturing inspection tasks

Visual Inspection AI has been purpose-built for the production environment and addresses a wide range of use cases across the automotive, electronics, semiconductor, and industrial sectors.

An animation showing how Visual Inspection AI works with cameras on the production line
Use case
Welding seam inspection

Automotive manufacturers use Visual Inspection AI to inspect robot-welded seams for anomalies at the most critical structural joints of the chassis.

Welding seam inspection
Use case
Mobile phone PCB inspection

Electronics manufacturers use Visual Inspection AI to simultaneously inspect dozens of individual components on high volume printed circuit boards (PCBs) to detect missing, misplaced, or damaged components, screws, springs, and soldering issues.

Mobile Phone Printed Circuit Board with many components
Use case
Silicon wafer defect analysis

Semiconductor manufacturers use Visual Inspection AI to detect and locate wafer defects, chip defects, or die cracks.

A silicon wafer

Compare features

Product comparison



Solve your generalized image use case.

Visual Inspection AI

Solve your visual inspection use case.

Typical use cases

  • General classification problems

  • Targeted for visual inspection tasks in manufacturing environments

On-premises deployment

  • Y

  • Y (through Docker container)

Manufacturing-specific models

  • N

  • Y (continuously improving due to domain-specific model optimization)

Imbalanced dataset handling

  • N (requires equal number of labeled defects and non-defects)

  • Y (can use a larger number of normal samples, plus a few labeled defects, which is typical in manufacturing) 

Active learning

  • N

  • Y (identifies suspect examples quickly for human review and labeling)

Segmentation and localization

  • N

  • Y (pinpoint the area where the defect was found)

Missing component detection

  • N

  • Y

High resolution image support

  • N

  • Y (up to 100M pixels)

Automatic image alignment

  • N

  • Y (auto-align images from camera streams)

Image anomaly detection

  • N

  • Y



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