In-House AI

Analysis of processes and requirements

In most cases, it is very clear and recurring processes that are to be supported by artificial intelligence. Would you like to generate text, make predictions, create images or optimise code? Is voice input necessary or not? Which database should the AI include? First, the requirements must be clearly defined in order to select the right models.

Customised solutions

The same applies to AI: the more tailored the models and working methods are to the customer's needs, the greater the benefits they deliver. That is why we work closely with our customers to identify the most suitable solutions and determine how they can be optimally integrated into the corporate environment.

Selection of the required hardware

Once the requirements have been clarified, the hardware is selected. The dimensions of the hardware depend on the number of parallel queries, also known as prompts, on the one hand, and on the AI models used on the other. Normally, usage patterns can be covered with affordable hardware that has a powerful GPU and sufficient RAM.

Installing the hardware

We take care of the entire installation of local hardware and the necessary AI models. If required, we also implement processes and interfaces or integrate the systems into existing structures.

Security

All systems and hardware are configured to ensure that data is protected at the highest level. Continuous monitoring during operation and the secure integration of the hardware into the infrastructure are essential and implemented on an individual basis.

Collecting data – and then what?

In the industrial sector in particular, the amount of data is growing exponentially, and this data can be valuable for a variety of scenarios. In the maintenance and servicing of machines and equipment, pattern recognition can be used to predict when parts need to be replaced or where wear and tear will occur. By analysing manufacturing processes, optimisations can be achieved and errors detected at an early stage. Utilisation can also be improved.

Deriving benefits from the data

If the available data is prepared correctly, AI models can be used to improve processes, increase efficiency or generate valuable predictions. However, this requires not only preparing the data correctly, but also integrating training of the AI models and ongoing monitoring.

Expanding business models

AI is revolutionising industries with data-intensive applications and manufacturing. Every area of the value chain - including efficient production, predictive maintenance and smart logistics to building technology and mobility - is entering a new era of innovation. This affects production processes and decision-making processes alike. For this reason alone, AI enables completely new business models.

Selection and training of models

Before AI models can be used, the data must be prepared and, in many cases, cleaned up, as only high-quality data will deliver the success you envision. If data is flawed, incomplete or outdated from the outset, the results of the AI will also be unreliable. We therefore work with our partners to develop solutions that ensure data quality from the outset and train AI models to meet the desired quality criteria.

Cross-divisional

Developing and implementing AI solutions requires expertise in several areas, such as machine learning, data analysis and IT infrastructure. We therefore work with external service providers and specialised partners to provide companies with a comprehensive solution.

EU AI Act

With the EU AI Act, the European Union has adopted a directive to make the development and use of AI systems safe and trustworthy. Here, too, we work with partners and experts as needed to ensure compliance with the AI Act.