Solutions

AI Quality Control Toolkit

Reduces the manual labor required to detect quality problems in manufacturing.

Wasting too much effort on visual quality inspection?

The average accuracy of manual inspection is under 85%, and results in high level of employee burnout. Additionally, creating ML models for Artificial Vision with current technologies is a very complex problem that requires a high level of expertise.

Facing challenges to scale up your operations?

Facing challenges to scale up your operations?

Manual or traditional machine vision inspection approaches make it hard for enterprises to scale up their operations: it is very resource intensive, or very costly due to the need for highly specialized labor.

disadvantage

Knowledge transfer takes time.

disadvantage

Low process consistency.

Are you thinking of further automation beyond Visual AI?

Are you thinking of further automation beyond Visual AI?

Not being able to link the results from your Visual AI stage to other metrics in the production line prevents you from achieving additional benefits in terms of automated root cause analysis of manufacturing problems and preventive maintenance.

disadvantage

Unexpected downtime of your manufacturing lines.

disadvantage

Lack of consistent failure root cause analysis.

disadvantage

Poor quality control model.

AI Quality Control Toolkit overcomes the challenges of industrial digital transformation

video smart factory play video

What benefits can you expect with AI Quality Control Toolkit?

90%

Less manual labour

95%

Detection Accuracy

100%

Consistency

< 6"

Prediction latency

Improve quality control in your manufacturing lines

Watch our 3-part video series to see the AI Quality Control Toolkit PoC implemented with Premo, a leading automotive parts manufacturer.

Grupo Premo
video Training module: creation and training of AI models for non-AI experts play video

Training module: creation and training of AI models for non-AI experts

advantage

UI-based; easy to create AI models for different parts.

advantage

Detection & classification capabilities leveraging state-of-the art Convolutional Neural Networks (CNNs).

advantage

Creation of whole-part or specific component models, to enable further investigation.

advantage

Active Learning capabilities to facilitate labeling work.

video Real-Time Monitoring: AI predictions and traceability play video

Real-Time Monitoring: AI predictions and traceability

advantage

Centralized solution, works with any type of camera.

advantage

Real-time prediction of parts (under 6s).

advantage

Keeps track of each part prediction for analysis and retraining.

advantage

Increases inspection accuracy and prevents stopping of production lines.

video Centralized Dashboard: quick overview of company's performance play video

Centralized Dashboard: quick overview of company's performance

advantage

Individual and aggregated statistics at different levels (production line, factory, country, company).

advantage

Drill-down capabilities on each manufacturing facility and production line.

advantage

Possibility to add other metrics to monitor performance and perform Root Cause Analysis of problems.

advantage

It lays the foundations for implementation/integration with a real-time MES (Manufacturing Execution System).

Get a demo of AI Quality Control Toolkit today

Get started and request a demo to learn how AI Quality Control Toolkit can help you.

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Operations made simple with AI

Associations we belong to:

Technology Park of Andalusia Tupl Telecom infra project Tupl 5G Open Innovation Lab Ametic la voz de la industria digital Our EU values has been the seed to transform an idea and a dream proposed 20 years ago into a real bottom-up movement lead by the Global StartupCities initiative Atlas Tecnológico es la plataforma donde conectar con aquellos agentes que generan innovación en los sectores de la industria 4.0.