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Software Defined Automation
Dna Industry Solutions

Contact

Whether you have any questions about features, pricing, trials or anything else, Kevin is ready to answer any questions for you. Just schedule a quick call with him.

Our Use Cases

Use our platform to build AI vision-based factory applications without any coding to significantly improve your productivity.

How it works 

Camera System

Increase your asset productivity

by up to 20%

 

Reduce maintenance costs

by up to 10%

 

Increase defect detection rate 

by up to 90%

Increase your asset productivity

by up to 20%

 

Reduce maintenance costs

by up to 10%

 

Increase defect detection rate 

by up to 90%

Keen to see how AI can help your business?

Success stories

Discover selected projects from our installations across industries that show how our solution is used in practice.

Multi-Sensor Anomaly Detection

By converting sound, vibration, and temperature data into hyperspectorgrams, combined with the more advanced AI-Vision algorithms, very high accuracies can be achieved in the detection and identification of maintenance needs. With this predictive approach, downtimes can be decreased.

for predictive maintenance in textile industry

Work Safety Inspection

A potentially hazardous but unfenced, human-robot collaboration area is permanently supervised by a camera. An artificial neural network is trained to identify the presence of persons and the absence of personal safety equipment. Eventually, warnings are displayed on a dashboard screen and signal light.

of an assembly cell in electronics Industry

In-Line Quality Inspection

Using Convolutional Neural Networks (CNN) the in-line quality inspection solution detects different surface error categories (e.g., cracks, colors, scratches). By this, the inspection task will be fully automated.

of sealings in metals industry

News

Keen to find out how AI can help your business?

AI Use Case

Phase 1

Explore

Gather an overview of the most exciting use cases of AI and evaluate whether your use case is suitable for AI-powered Machine Vision

Software Concept

Phase 2

Define

Get an proposal for an application-specific hard- and software concept for your AI solution.

Machine learning

Phase 3

Build

Train top-notch machine learning models with no code on the world’s leading training provider

Digital Factory

Phase 4

Deploy

Embed and update your model in the ANTICIPATE HUB with one click

Digital Production Line

Outcome

ANTICIPATE INSPECT

Bring your company up to speed with customized workshops and self-learning opportunities

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Independent

No vendor lock-in effect for hardware and software

End-to-End

Support of the full implementation process

Tailored

Tailored Hardware setup for specific industry problems

No Code

AI specialists are limited and very expensive

Secure

Meets the highest data protection standards

Flexible

Flexible deployment options from on-premise to cloud

Scalable

Horizontal scaling, through microservice architecture

Our Core Features

Run your own models on our industry graded operating software, which is designed to overcome the most critical hurdles of AI vision implementations in manufacturing.

Scalable

Horizontal scaling, through microservice architecture

Secure

Meets the highest data protection standards

Secure

Meets the highest data protection standards

Secure

Meets the highest data protection standards

Secure

Meets the highest data protection standards

Scalable

Horizontal scaling, through microservice architecture

Solution

We developed a no-code platform which enables engineers to realize AI vision solutions within days instead of months.

AI Use Case

Phase 1

Explore

Gather an overview of the most exciting use cases of AI and evaluate whether your use case is suitable for AI-powered Machine Vision

Assembly Verification

With low-cost cameras, manual processes can be monitored with high precision using AI. Individual motions are detected and compared with the target process in order to validate the process sequence and measure cycle times.

of a Packaging Process in Consumer Electronics Industry

BUILD YOUR OWN INDUSTRIAL

AI VISION SOLUTIONS

Scalable

Horizontal scaling, through microservice architecture

Flexible

Flexible deployment options from on-premise to cloud

Secure

Meets the highest data protection standards

Automate expensive expert-related repetitive work by analyzing sensor and camera data automatically with AI.

Multi-Sensor

Anomaly Detection

Automate expensive expert-related repetitive work by analyzing sensor and camera data automatically with AI.

Situation

In hazardous, open-plan shopfloors, measures must be taken to prevent accidental misbehavior regar-ding PSE (personal safety equipment). A human-based supervision regarding PSE usage is very resource intensive.

Solution

A potentially hazardous but unfenced, human-robot collaboration area is permanently supervised by a camera. An artificial neural network is trained to identify the presence of persons and the absence of personal safety equipment. Eventually, warnings are displayed on a dashboard screen and signal light.

Result

10% reduction of time losses by interruptions caused by accidents due to missing protective equipment

15% less injuries and thus significantly higher safety on your shopfloor due to early safety risk detection.

Situation

Currently, inspection processes in manufacturing require a lot of manual intervention and experience to achieve good quality in reasonable time. On the market is no solution to continuously control the products quality presently.

Solution

Using Convolutional Neural Networks (CNN) the in-line quality inspection solution detects different surface error categories (e.g., cracks, colors, scratches). By this the inspection task will be fully automated.

Result

10% increased yield, as defective parts are detected with a higher accuracy.

20% reduced operating time for manual inspection in assembly process.

Situation

Errors made in manual processes, e.g., during assembly, are usually discovered late in the process or even by the customer, which leads to customer dissatisfaction, high costs for error correction and customer service.

Solution

With low-cost cameras, manual processes can be monitored with high precision using AI. Individual motions are detected and compared with the target process in order to validate the process sequence and measure cycle times.

Result

20% operating cost reduction through early fault detection, since error correction is more expensive in the later process.

40% reduction of customer return rate of finished products due to error prevention in the manual manufacturing processes.

Situation

Long downtimes due to unprepared changeover processes lead to high costs due to a reduced throughput rate and are a problem in the manufacturing industry.

Solution

By converting sound, vibration and temperature data into hyperspectorgrams, combined with the more advanced AI-Vision algorithms, very high accuracies can be achieved in the detection and identification of maintenance needs. With this predictive approach downtimes can be decreased.

Result

45% reduction in downtime. As maintenance needs are known and actions can be prepared in beforehand.

30% reduction in maintenance costs, due to faster identification of maintenance root causes and more efficient actions.

Automate expensive expert-related repetitive work by analyzing sensor and camera data automatically with AI.

Work Safety

Recognition

Detection of Personal Protective Equipment (PPE) and dangerous situations increases the work safety of your employees.

Situation

In hazardous, open-plan shopfloors, measures must be taken to prevent accidental misbehavior regar-ding PSE (personal safety equipment). A human-based supervision regarding PSE usage is very resource intensive.

Solution

A potentially hazardous but unfenced, human-robot collaboration area is permanently supervised by a camera. An artificial neural network is trained to identify the presence of persons and the absence of personal safety equipment. Eventually, warnings are displayed on a dashboard screen and signal light.

Result

10% reduction of time losses by interruptions caused by accidents due to missing protective equipment

15% less injuries and thus significantly higher safety on your shopfloor due to early safety risk detection.

Situation

Currently, inspection processes in manufacturing require a lot of manual intervention and experience to achieve good quality in reasonable time. On the market is no solution to continuously control the products quality presently.

Solution

Using Convolutional Neural Networks (CNN) the in-line quality inspection solution detects different surface error categories (e.g., cracks, colors, scratches). By this the inspection task will be fully automated.

Result

10% increased yield, as defective parts are detected with a higher accuracy.

20% reduced operating time for manual inspection in assembly process.

Situation

Errors made in manual processes, e.g., during assembly, are usually discovered late in the process or even by the customer, which leads to customer dissatisfaction, high costs for error correction and customer service.

Solution

With low-cost cameras, manual processes can be monitored with high precision using AI. Individual motions are detected and compared with the target process in order to validate the process sequence and measure cycle times.

Result

20% operating cost reduction through early fault detection, since error correction is more expensive in the later process.

40% reduction of customer return rate of finished products due to error prevention in the manual manufacturing processes.

Situation

In hazardous, open-plan shopfloors, measures must be taken to prevent accidental misbehavior regar-ding PSE (personal safety equipment). A human-based supervision regarding PSE usage is very resource intensive.

Solution

A potentially hazardous but unfenced, human-robot collaboration area is permanently supervised by a camera. An artificial neural network is trained to identify the presence of persons and the absence of personal safety equipment. Eventually, warnings are displayed on a dashboard screen and signal light.

Result

10% reduction of time losses by interruptions caused by accidents due to missing protective equipment.

15% fewer injuries and thus significantly higher safety across your shopflor due to early safety risk detection.  

Automate expensive expert-related repetitive work by analyzing sensor and camera data automatically with AI.

In-Line Quality

Inspection

Automated quality inspection and optimization increase throughput and decrease costs significantly.

Situation

In hazardous, open-plan shopfloors, measures must be taken to prevent accidental misbehavior regar-ding PSE (personal safety equipment). A human-based supervision regarding PSE usage is very resource intensive.

Solution

A potentially hazardous but unfenced, human-robot collaboration area is permanently supervised by a camera. An artificial neural network is trained to identify the presence of persons and the absence of personal safety equipment. Eventually, warnings are displayed on a dashboard screen and signal light.

Result

10% reduction of time losses by interruptions caused by accidents due to missing protective equipment

15% less injuries and thus significantly higher safety on your shopfloor due to early safety risk detection.

Situation

Currently, inspection processes in manufacturing require a lot of manual intervention and experience to achieve good quality in reasonable time. On the market is no solution to continuously control the products quality presently.

Solution

Using Convolutional Neural Networks (CNN) the in-line quality inspection solution detects different surface error categories (e.g., cracks, colors, scratches). By this the inspection task will be fully automated.

Result

10% increased yield, as defective parts are detected with a higher accuracy.

20% reduced operating time for manual inspection in assembly process.

Situation

Errors made in manual processes, e.g., during assembly, are usually discovered late in the process or even by the customer, which leads to customer dissatisfaction, high costs for error correction and customer service.

Solution

With low-cost cameras, manual processes can be monitored with high precision using AI. Individual motions are detected and compared with the target process in order to validate the process sequence and measure cycle times.

Result

20% operating cost reduction through early fault detection, since error correction is more expensive in the later process.

40% reduction of customer return rate of finished products due to error prevention in the manual manufacturing processes.

Situation

Currently, inspection processes in manufacturing require a lot of manual intervention and experience to achieve good quality in reasonable time. On the market is no solution to continuously control the products quality presently.

Solution

Using Convolutional Neural Networks (CNN) the in-line quality inspection solution detects different surface error categories (e.g., cracks, colors, scratches). By this the inspection task will be fully automated.

Result

10% increased yield, as defective parts are detected with a higher accuracy.

20% reduced operating time for manual inspection in assembly process.

Automate expensive expert-related repetitive work by analyzing sensor and camera data automatically with AI.

Assembly

Verification

Inspection of manual assembly tasks increases transparency about cycle times and detects errors early in the process.

Situation

In hazardous, open-plan shopfloors, measures must be taken to prevent accidental misbehavior regar-ding PSE (personal safety equipment). A human-based supervision regarding PSE usage is very resource intensive.

Solution

A potentially hazardous but unfenced, human-robot collaboration area is permanently supervised by a camera. An artificial neural network is trained to identify the presence of persons and the absence of personal safety equipment. Eventually, warnings are displayed on a dashboard screen and signal light.

Result

10% reduction of time losses by interruptions caused by accidents due to missing protective equipment

15% less injuries and thus significantly higher safety on your shopfloor due to early safety risk detection.

Situation

Currently, inspection processes in manufacturing require a lot of manual intervention and experience to achieve good quality in reasonable time. On the market is no solution to continuously control the products quality presently.

Solution

Using Convolutional Neural Networks (CNN) the in-line quality inspection solution detects different surface error categories (e.g., cracks, colors, scratches). By this the inspection task will be fully automated.

Result

10% increased yield, as defective parts are detected with a higher accuracy.

20% reduced operating time for manual inspection in assembly process.

Situation

Errors made in manual processes, e.g., during assembly, are usually discovered late in the process or even by the customer, which leads to customer dissatisfaction, high costs for error correction and customer service.

Solution

With low-cost cameras, manual processes can be monitored with high precision using AI. Individual motions are detected and compared with the target process in order to validate the process sequence and measure cycle times.

Result

20% operating cost reduction through early fault detection, since error correction is more expensive in the later process.

40% reduction of customer return rate of finished products due to error prevention in the manual manufacturing processes.

Situation

Errors made in manual processes, e.g., during assembly, are usually discovered late in the process or even by the customer, which leads to customer dissatisfaction, high costs for error correction and customer service.

Solution

With low-cost cameras, manual processes can be monitored with high precision using AI. Individual motions are detected and compared with the target process in order to validate the process sequence and measure cycle times.

Result

20% operating cost reduction through early fault detection, since error correction is more expensive in the later process.

40% reduction of customer return rate of finished products due to error prevention in the manual manufacturing processes.

Automate expensive expert-related repetitive work by analyzing sensor and camera data automatically with AI.

Multi-Sensor

Anomaly Detection

Work Safety

Recognition

In-Line Quality

Inspection

Assembly

Verification

AI-Powered Automation Speeds Up Time to Production

With our platform, you can explore the right use cases, define a tailored hardware concept, build your own AI models and deploy the model in your factory. 

​Your AI vision solutions are made easy to operationalize at the proper scale for load and number of models all within one, no-code, easy to manage platform.

End-to-End

Support of the full implementation process

Tailored

Tailored hardware configuration for specific industry problems

End-to-End

Support of the full implementation process

End-to-End

Support of the full implementation process

Secure

Meet the highest data protection standards

Flexible

Flexible deployment options from on-premise to cloud

End-to-End

Support of the full implementation process