Automating quality control with AI

Starting point: What is Layer7 AI all about?

Starting hypothesis: In order to build meaningful AI solutions you (at least) need 3 different skill sets

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Deep expertise in AI

Productization experience

Business Know-How

That’s why we build our team around these 3 core competences right from the start

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This team builds customized AI products for our clients

Use-Case: Automating quality control with AI

Problem: Quality control is still oftentimes performed manually creating several pain points

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High labour costs

Potentially long inspection times

Inconsistent inspection results

Non-transparent / non-digital

Work is exhaustive

High re-training effort due to churn

Solution: Modern Computer Vision algorithms can automate the quality control process addressing these pain points

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Low labour costs

Inspections need <1 second

Consistent (=learned) quality standard

Transparent & digital documentation

The model doesn’t tire

Model learns through feedback

We have built a product that aims to address these pain points through various features…

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On prem Cloud

Ruggedized TX2

Industrycamera

Industrymonitor

Inspect parts on the edge

Label new data

Review model

Generate automatic reports

…while leveraging human expertise to continuously learn and improve the inspection quality

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L7 quality control solution

Client

Layer7 AI

Product Deployment Image Review

Model Refinement FeedbackModel improvement

Error classification

Clients can test our solution free of charge – costs are only incurred if we manage to “solve” the specific use-case

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Hardware Installation

Data collection & Model training

Model Evaluation

Use-Case Identification

Model Integration & Improvement

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Free of chargePerformance

based purchase

But hasn’t automatic visual quality control been an industry standard for years?

Visual quality control market

The current visual quality control market can be broadly divided into three sub-segments…

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Traditional visual quality control

• Mostly based on classic rule-based systems

• Often used for simple relatively static use-cases – e.g. measuring & counting

100% manual visual quality control

Partial manual visual quality control

• Based on the human visual senses

• Often used for complex use-cases with a high variability –e.g. surface/texture inspection

• Based on the human visual senses

• Often used for complex use-cases with a particularly high throughput rate

Visual quality control market

…while most existing players operate with a clear focus on traditional visual quality control

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Traditional visual quality control

100% manual visual quality control

Partial manual visual quality control

New emerging market

No clear market leader has emerged in this new market as “one-size fits all” solutions do not exist in an AI world

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That‘s the reason why we decided to start our company in the Cyber Valley – Germany‘s home of modern machine learning

A well functioning AI model for one use-case…

…will most likely not translate to another inspection case

Most functioning AI-based quality control models still need to be customized and tailored by experts, which are hard to come by

According to Element AI, there are only 10,000 real AI experts worldwide

Thank you for yourattention

Peter DroegeCEOE-Mail: peter.droege@layer7.aiMobile: +49 159 01479983

Website: https://www.layer7.aiLinkedIn: https://www.linkedin.com/company/layer7-ai/Twitter: https://twitter.com/layer7aiMedium: https://medium.com/layer7-ai

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