AI engineering throughout the entire engineering process

Why AI-powered engineering is crucial for machinery and plant

Artificial intelligence is transforming the way industrial software is developed, tested and operated. AI-supported engineering refers to the targeted use of AI methods throughout the entire engineering process – from requirements analysis and code generation, through quality assurance, to data-driven optimisation during operation. This opens up considerable potential for companies in the mechanical and plant engineering sector: faster development cycles, higher software quality and new data-driven product and service offerings.

ITQ helps companies to integrate AI into their engineering processes in a targeted manner and with a clear strategy. The focus is not on AI as an end in itself, but on its practical benefits: Which processes can be simplified using AI? Where can self-learning algorithms replace manual tasks? And how can AI be embedded reliably, securely and in a way that allows for maintenance within industrial software systems?

Current developments such as large language models for code generation, AI-powered test automation, predictive maintenance based on sensor data, and AI inference on edge devices are opening up new opportunities for mechanical engineering firms and plant operators. At the same time, these approaches require in-depth technical expertise in software architecture, data quality and system integration. ITQ offers both: industrial engineering experience and AI expertise.

The combination of in-depth system knowledge, many years’ experience in industrial software development and a structured approach to AI implementation enables ITQ to develop AI solutions that work in practice – solutions that are reliable, scalable and secure.

Your benefits at a glance:

Targeted identification of AI potential in engineering and operational processes
Faster development cycles through AI-powered code generation and analysis
Improved software quality through AI-based test automation and anomaly detection
New data-driven products and services through predictive maintenance and process optimisation
Scalable AI inference on edge, IPC or cloud platforms
A structured roll-out with a clear strategy, data foundation and quality assurance
Tried-and-tested AI solutions with an understanding of industrial systems

Finding the right solution together

Your personal contact for software engineering will be happy to advise you on your specific enquiry.

Get in touch now for a no-obligation consultation.

Luis Zollbrecht

ITQ Branch Manager, North Rhine-Westphalia

„AI only realises its full potential in an industrial setting when strategy, data infrastructure, system architecture and quality assurance are considered as an integrated whole right from the start.“

AI engineering requires strategy, data and industrial systems expertise

Many companies recognise the potential of AI for their products and processes – but fail when it comes to putting it into practice. A lack of data, unclear requirements, poor integration into existing system landscapes and a lack of expertise in selecting suitable AI methods are typical hurdles. Pilot projects remain just that – pilots – and never make it into live operation.

At the same time, development is accelerating: generative AI, LLM-based development assistants and AI inference on edge devices are now ready for production. Companies that make strategic use of AI at an early stage gain measurable competitive advantages – through shorter development times, better product quality and new digital business models.

ITQ helps companies to systematically identify opportunities for AI, select appropriate methods and take AI solutions from the initial idea through to operational deployment. System architecture, data availability, security requirements and integrability are taken into account right from the start.

Making targeted use of AI’s potential in AI engineering – from the initial idea to production-ready applications

Typical challenges within a company:

An unclear AI strategy and a failure to prioritise areas of application
Insufficient data set or poor data quality for AI training
Lack of integration of AI into existing software architectures and processes
Pilot projects with no clear path to operational deployment
Uncertainty regarding security requirements and the reliability of AI-based decisions

Only a structured implementation with a clear strategy, a suitable data foundation and consistent system integration can ensure that AI is used effectively and sustainably in an industrial setting.

AI Engineering Services

ITQ supports companies in integrating AI into engineering processes and products in a targeted manner and with a clear strategy – from analysing potential and preparing data, through the development of AI-based solutions, to scaling up to production systems. We take an open approach to technology and have a thorough understanding of industrial systems.

An overview of our services

Analysis and identification of AI potential in engineering processes and products
Selection of suitable AI methods and technologies for the specific use case
Development and preparation of training data and simulation environments
Development and integration of AI-based software components into industrial systems
Scaling and deploying AI solutions on edge, IPC and cloud platforms

Technological expertise

Machine Learning and Deep Learning for Industrial Applications
Generative AI and LLM integration into development and engineering processes
AI-powered image and pattern recognition for quality assurance and process monitoring
Predictive maintenance and anomaly detection based on machine data
AI inference on edge devices, IPCs and cloud platforms
Reinforcement learning for process optimisation and robotics applications

Procedure for the introduction of AI engineering

The introduction of AI in industrial engineering requires a structured approach – from strategy and the data foundation right through to production-ready implementation. ITQ supports companies in this process with a clear, tried-and-tested implementation approach.

Project phases

Analysis and identification of potential

Systematic mapping of processes and evaluation of suitable areas of application for AI, based on data availability, effort involved and expected benefits.

Selection of the AI method and architectural concept

Selecting suitable AI methods and defining the system architecture for training, deployment and operation.

Setting up a database and training environment

Collection, processing and quality assurance of training data, as well as the development of simulation environments for data generation and model validation.

Development and integration of the AI solution

Implementation of the AI model and its integration into existing software systems, taking into account real-time, safety and reliability requirements.

Validation, deployment and operation

Systematic identification, assessment and rectification of security vulnerabilities, as well as the continuous optimisation and safeguarding of software released to customers.

AI Engineering in Practice

Sector: Logistics, Mechanical Engineering

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Further ITQ solutions

The services described form part of ITQ’s comprehensive service portfolio. ITQ combines technological expertise, a methodical approach and a deep understanding of processes to deliver sustainable solutions throughout the entire life cycle.

FAQs – AI Engineering

What is meant by AI-assisted engineering?

AI-supported engineering refers to the targeted use of AI methods throughout the engineering process – from requirements analysis and code generation, through automated testing, to the data-driven optimisation of machinery and plant during operation.

In which specific applications is AI particularly well suited to mechanical and plant engineering?

Typical areas of application include predictive maintenance based on sensor data, AI-supported quality assurance through image processing, process optimisation using self-learning algorithms, and generative AI to support development teams with code generation and test automation.

What are the requirements for a company to implement AI?

Key factors include a sufficient data set, clear requirements for the use of AI, and a system architecture capable of integrating AI components. ITQ provides support in establishing these prerequisites in a structured manner and identifying suitable starting points.

How does AI inference on edge devices differ from cloud-based AI?

Edge inference runs AI models directly on the device or gateway – with low latency, without relying on the network, and with greater data privacy. Cloud-based AI offers greater computing power for training and complex models. Depending on the use case, hybrid approaches may also be appropriate.

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How does ITQ ensure that AI solutions operate reliably and securely?

ITQ integrates requirements for reliability, security and explainability into the AI architecture right from the start. This includes systematic model validation, operational monitoring and clearly defined processes for updates and model maintenance throughout the entire lifecycle.