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We work with you to analyze where and how you can use AI to automate manual activities and how (semi-) autonomous processes can be implemented. Always with the security of your data in mind.

We help with the selection, licensing and integration of AI applications. The criteria for training the AI are defined together. The AI will only deliver the planned results if it is well thought out and based on your experience and data.
The database with all relevant and necessary data points from the past is crucial for success. Data availability and data quality are challenges and limiting factors. The effort involved in extracting this data and using it with AI can be very high, especially if different IT systems, plants, locations and possibly even company boundaries are crossed and standards have to be adhered to. This phase therefore requires extremely meticulous processing and, if necessary, the analysis results are iteratively influenced and adapted.

AI training may require very high computing power that is not available in your company. We support you in having the required resources available at the right time. We work together with cloud service providers, because increasing your IT capacities usually makes no economic sense, as the training capacities are only required for a limited period of time.
 

Testing under supervision and detailed testing of the procedure and the results that the AI delivers lead to deployment after acceptance. Here, the AI should not simply be left to run by itself, but should be thouroughly tested in order to be reliable and trusted.
This topic is often massively underrated. You need human expertise to spot and correct the not correct output. But what often is missed out is to find the root cause of the not correct (sometimes not so nice in tone, misleading, contradicting or in the worst case false) output, but this is key in order to have a reliable AI.

We use the insights gained to support you in the ongoing optimization and expansion of your AI deployment. From the first step to a digitalized company.
After all, if the first AI applications successfully deliver the expected results (and often exceed them), there are further areas of applications that go beyond the field of data security, but are closely linked to it. For example, downstream processes such as archiving can also be automated with AI, ensuring that archived information is protected as well, guaranteeing availability and regular checks while ensuring industry-specific retention regulations. From here it’s not far to have a look at your GRC/ESG programs and further to risk management (another underrated topic when it comes to AI).