CorteX Central

Build better detection models faster with the CorteX Central AI management system - no AI experience needed

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Manage AI models in one central platform

Manage and optimize models without disrupting production

Deploy AI models across production lines globally with CorteX Central's scalable architecture

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Rapidly train with sample-efficient AI

Use intuitive, drag-and-drop interface to develop AI models

As few as 5 images needed to train AI models

Optimize quality criteria for yield

Instantly tune quality criteria across 6 adjustable attributes

Visualize impact on yield before deploying AI models to production

Use LIMIT-- a separate inference decision from OK or NG– to set aside parts based on certain thresholds to further validate

Not just defect detection. All-in-one inspection that provides:

Detection
Classification
Count
Thresholds
Location
Measurement
Depth
Barcode

The UnitX Advantage

Rule-Based Vision

Other AI / Deep Learning

UnitX CorteX

Effort
Requires engineers to develop complex, bespoke rules for each defect and part type
Requires 100s of images to train
Intuitive training interface requires as few as 5 images to train; no AI experience needed. Quick to adapt to new product and defect types
Accuracy
Only accurate for well-defined defects and parts with consistent environment, leading to escapes and overkill
Can fail on part position and orientation variability, sometimes requiring additional positioning tools
Accurate for defects that are complex and variable in shape, size, location, and presentation, automatically normalizing for position variability
Yield Optimization
Manual effort required to tune quality criteria. Unclear how criteria impact yield, leading to overkill
Manual effort required to tune quality criteria. Unclear how criteria impact yield, leading to overkill
Easily tune quality criteria specific to each defect across a number of attributes. Visualize impact on yield before pushing changes to production
See how CorteX works as part of the greater UnitX solution
1
Effort
Traditional Lighting

Limited lighting configuration self-service makes it difficult to adapt.

Requires engineers to develop complex, bespoke rules for each defect and part type
Other AI / Deep Learning

Limited lighting configuration self-service makes it difficult to adapt.

Requires 100s of images to train
UnitX CorteX

Limited lighting configuration self-service makes it difficult to adapt.

Intuitive training interface requires as few as 5 images to train; no AI experience needed. Quick to adapt to new product and defect types
2
Accuracy
Traditional Lighting

Limited lighting configuration self-service makes it difficult to adapt.

Only accurate for well-defined defects and parts with consistent environment, leading to escapes and overkill
Other AI / Deep Learning

Limited lighting configuration self-service makes it difficult to adapt.

Can fail on part position and orientation variability, sometimes requiring additional positioning tools
UnitX CorteX

Limited lighting configuration self-service makes it difficult to adapt.

Accurate for defects that are complex and variable in shape, size, location, and presentation, automatically normalizing for position variability
3
Yield Optimization
Traditional Lighting

Limited lighting configuration self-service makes it difficult to adapt.

Manual effort required to tune quality criteria. Unclear how criteria impact yield, leading to overkill
Other AI / Deep Learning

Limited lighting configuration self-service makes it difficult to adapt.

Manual effort required to tune quality criteria. Unclear how criteria impact yield, leading to overkill
UnitX CorteX

Limited lighting configuration self-service makes it difficult to adapt.

Easily tune quality criteria specific to each defect across a number of attributes. Visualize impact on yield before pushing changes to production

What people say about us

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Jessica Clark
Operations Director

CorteX Datasheet

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