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Suakit on monitor screen over yellow bokeh

SuaKIT Vision Software

Automated deep learning visual inspection solution

Deep learning vision software automates visual inspection applications

SuaKIT vision software performs visual inspection and classification of complex materials and key components for the mobile, semiconductor, electronics, and automotive industries. It also automates manual inspections in the food and beverage, packaging, and raw materials industries.

SuaKIT deep learning vision software on computer monitor
Defect detected on computer chip

Improves quality and yield

SuaKIT’s finetuned deep learning models deliver highly accurate inspection results. The deep learning algorithm’s internal analysis process enhances quality upstream to reduce overkill and underkill rates to optimize quality and yield.

Reduces cost

Implementing an automated system decreases dependence on unreliable, manual inspections. Being able to run inspection operations around the clock optimizes throughput and improves takt time to satisfy customer demand. SuaKIT’s high detection rates also reduces the need to addition and costly inspection hardware.

Defect detected on a orange soda bottle
Defect detected on a part

Ensures reliable, validated results

SuaKIT’s highly consistent inspections guarantee the same results from line to line, shift to shift, and factory to factory. The software archives images and document results that can be reviewed and validated offline. This valuable data helps quality engineers to optimize applications and understand anomalous results.

Key functions


Example of SuaKIT detection features of found blue and yellow cars

Detects objects within different classes in a single image


Example of SuaKIT classification features

Groups images by multiple predetermined classes


blue car with sunroof marked as red

Accurately finds position/area/shape or defects in the images

Deep learning architectures

Single Image Analysis

Learns each image and identified defects

Image Comparison

Learns and detects defects by concentrating on the differences between a set of two images

Multi Image Analysis

Analyzes relationships between images to train defect detection model

One Class Learning

Identifies defects based on deviations from trained OK images

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