- AI-based automation solution
AI & Digital Twin – More efficient development and smarter operations
Automating processes with AI assistants
Training AI using digital twins
Continuous optimisation through AI agents
Development and operation
of IoT systems
of IoT systems
Automating processes with AI assistants
Training AI using digital twins
Continuous optimisation through AI agents
Development and operation
of IoT systems
- Intelligent automation
The relevance of AI and digital twins for modern automation systems
Companies face complex engineering challenges:
- Extensive testing on real hardware
- Significant manual effort required for validation and troubleshooting
- A lack of transparency in day-to-day operations
- Challenges with scaling in production, laboratories and smart farming
Answer:
Digital engineering using AI and digital twins
Showcase – Plant Care Robot System
Smart crop monitoring of the future. A robot-controlled smart farming facility can sow, tend, monitor and deliver crops.
- Digital Twin enables early testing of the robot’s movements
- AI agents monitor plant health and optimise care processes
- The system can be virtually expanded or adapted without any risk
- Automatic detection of growth stages and problems using computer vision
- This provides a scalable, flexible solution for agriculture, research and greenhouses
- Demonstrator
How AI is accelerating engineering – the robotics demonstrator in action
The aim of the robot cell was to develop a demonstrator that compactly combines all „state-of-the-art“ approaches in mechatronics engineering within a realistic example. This technological breakthrough demonstrates how, through the skilful combination of AI in engineering and in the system solution, a functioning system can be achieved quickly and efficiently.
The demonstrator shows how AI supports the entire value chain – from design through to commissioning and operation.
Taking the example of the smart plant monitoring system of the future – the Plant Care Robot System.
Edge AI
- Decisions without delay
- Decentralised intelligence through AI agents
Voice Control & NLP
- Natural Language Processing Interface
- Voice commands such as „Give me my plant“
Digital Twin Lifecycle
- From design to operation
- Predictive Maintenance
Voice Control
Intuitive operation via voice control
AI Computer Vision
Monitoring plant health, growth stages and problems through image analysis
Digital Twin
Virtual replica of the robot cell for risk-free testing and optimisation
IoT connectivity
Seamless communication and remote monitoring via networked systems
Secure Communication
Protection of data and system integrity through modern security protocols
Validation
Automated tests and
safety checks for smooth processes
Continuous Optimisation
Optimisation of the digital twin through the continuous use of real-world field data
- Efficiency increase
- Continuous progress
Benefits for users and developers
-
Efficient
for everyone involved
faster
Commissioning
Commissioning
0
%
shorter
Development times
Development times
0
%
Cost savings
for prototypes
for prototypes
0
%
Digital twins speed up development through short feedback loops.
AI, control systems and mechatronics are continuously interconnected.
AI, control systems and mechatronics are continuously interconnected.
Development times reduced by up to 30–50 % by using virtual testing instead of physical prototypes.
Up to 70 % fewer iteration loops thanks to continuous simulation.
50–80 %: faster commissioning thanks to early virtual commissioning.
30–60 % Cost savings on prototypes.
Smart Farming
This smart farming example application demonstrates how real-world robots can be controlled using AI agents and voice control.
The entire robot cell was first modelled as a digital twin, enabling the integration of the AI components with the robot’s control programme to be tested at a very early stage.
Both the developer and the end user can choose to operate the system either purely digitally or in the real world. It is even possible to combine real and digital components to make testing and maintenance easier.
- Expertise in action
- Technology that connects
Partners & Technological Background
An interdisciplinary team of experts in robotics, AI and software development is working hand in hand on the future of intelligent automation.
- Get in touch
Request a demonstration Demonstration date
As soon as we have received your enquiry, we will get in touch with you to discuss the next steps together.
Get in touch with us and experience the future of ‘Batch Size One’ development
- Questions / Answers
Questions about AI and digital twins
Where is AI used in a modern automation solution?
AI operates on two levels: in engineering and whilst the system is running.
In the engineering department, she helps to develop, test and optimise new functions.
In production, she analyses sensor data in real time, makes decisions and automatically optimises processes.
What are the benefits of using a digital twin right from the development phase?
A digital twin shifts large parts of the design, commissioning and troubleshooting processes into the virtual world. This eliminates the need for physical test cycles, reduces the risk of errors and significantly shortens development times. Teams can simulate processes, reproduce critical situations and compare variants without needing the physical hardware. This results in a faster, safer and more cost-effective development process.
In what specific ways does AI support the engineering process?
AI provides support at an early stage of the engineering process by analysing requirements, generating concepts, creating and testing models, optimising CAD designs, speeding up simulations and automating tests. It also assists with code generation, documentation and knowledge management, shortens development times and improves decision-making throughout the entire product development process.
In what specific ways does AI support the operation of automation solutions?
AI detects anomalies, automatically evaluates sensor data and suggests better solutions based on empirical data. This applies, for example, to motion sequences, image analysis, quality checks and safety-critical situations. Whereas in the past every test had to be set up manually, AI now handles the analysis, prioritisation and automation of numerous development steps.
What are the benefits of an automation solution using AI and a digital twin?
It combines secure virtual testing with machine learning models in production. This results in faster development cycles, fewer iterations, less downtime and higher quality standards – whilst simultaneously reducing costs.
Any further questions ...
How does a digital twin work in combination with AI?
The digital twin creates a virtual representation of the real-world system. AI uses this simulation to train models, test processes or identify fault patterns. During operation, the AI continues to learn and improves the digital twin using real-world field data. This creates a continuous learning cycle.
What data does AI need in order to function reliably?
AI uses sensor data, camera images, process values and status information. Basic data is sufficient to get started – the longer the plant runs, the more accurate the AI’s predictions and optimisation recommendations become.
Can an AI-powered automation solution be integrated into existing systems?
Yes. The architecture is modular. AI modules, digital twins and sensor technology can be added in stages without having to overhaul the entire system. The system scales to meet your requirements.
How safe is the use of AI in sensitive or complex systems?
Before any real-world deployment, workflows can be tested in the digital twin. In addition, automatic validations, AI-based safety checks and secure communication protocols ensure that processes remain stable, transparent and reliable.
What measurable effects are realistic?
Companies typically report on:
- a significant reduction in development time
- lower error rates and fewer downtimes
- safer processes
- improved plant availability
- more efficient maintenance processes
For which sectors is this approach particularly suitable?
Regardless of specific sectors, the solution is ideal wherever precision, product variety and automation come together:
Examples of this include, in particular, mechanical and plant engineering, robotics, automation, smart farming, environmental monitoring, quality assurance, research and industrial production cells.