Experienced AI agency for the development of customised AI software solutions

We cannot give a generalised answer to this question. It is true that many AI systems require a large amount of high-quality data in order to produce reliable results. However, this does not mean that you have to wait until you have expanded or improved the quality of your database. Depending on the planned use case for the AI, you have alternative options for training your algorithm that require little or no data of your own. Buzzwords here include "few-shot learning" and "zero-shot learning". As an AI agency, we will be happy to advise you individually on which methodology is best suited to your case.

Very good question. With AI projects, you should define clear, measurable goals in advance in order to be able to objectively demonstrate the success of the introduction. The specific KPIs naturally depend on your particular use case. You should differentiate between quantitative and qualitative metrics. Examples of quantitative metrics: Throughput time of a process, error rate, use of resources, costs, accuracy of predictions.

To calculate the ROI, compare the total costs of AI implementation, including employee training and maintenance, with the cost savings or financial gain made possible by the use of AI. Again, examples of qualitative metrics: Employee satisfaction, improved user experience in work processes, organisation's ability to innovate. It is best to survey the status quo of the qualitative metrics before introducing AI in order to be able to determine changes after three and six months via a new survey.

There are also many areas of application for artificial intelligence in which you do not have to comply with data protection regulations because no personal data is processed. For example, you can use AI to monitor the wear and tear on parts of your production machines or to minimise waste in manufacturing processes. You do not have to observe any data protection regulations here. What must always be ensured, however, is the security of the processed data.

If you use personal data for your AI systems, you must ensure that your company still complies with the legislation, especially the GDPR. This can lead to major technical challenges if your users request the deletion of their data, for example. Because once data has been fed into an AI, it cannot be easily removed from the data model. To avoid any nasty surprises here, seek individual advice on data protection and data security from us or another AI agency in the run-up to the introduction of AI.

As an AI agency, we are often confronted with this question. Of course, the introduction of artificial intelligence not only changes some work processes, but is also a highly emotional topic for all employees.

The announcement that AI will be increasingly integrated into the organisation in the future may trigger enthusiasm in some, but can also fuel fear in others, e.g. of losing their job in the near future. However, in our experience as an AI agency, this is not a realistic scenario in most cases: instead, the responsibilities and tasks of employees will often change. As an employer, you can support your employees by offering training so that they can successfully master the changed way of working with AI. If some jobs do become redundant due to AI, you should offer the affected employees retraining opportunities in good time. There will often be other areas of the company where your employees' skills and qualifications can still be utilised.

We take a proactive approach to the development of our AI models. As an AI agency, we have always focussed on transparency and document our process comprehensively. By working closely with our customers, we are able to identify risks of bias and errors at an early stage, both at a technical and process level, and integrate suitable protective measures into systems and workflows.

In our experience, a well thought-out strategy is essential if you want to utilise the potential of artificial intelligence. The strategy does not have to be oversized: it is sufficient to start by working with a small core group to consider how AI will change your business model and in which areas it can deliver significant added value. In this way, you can quickly identify potential applications for AI that are not only possible but also make strategic sense.

Many AI use cases are challenging in terms of technical implementation. They require a broad database and adjustments to the data infrastructure. It can take months before a measurable benefit is realised. To gain internal support for more use of AI, you need to be able to deliver initial results quickly and demonstrate a positive return on investment. Choose your first AI use case accordingly: Start with a manageable project that is feasible and contributes to your company's strategic goals.

Artificial intelligence has so many different forms and applications that there are probably use cases for a sensible use of AI in your company too. Small companies with a limited budget may not be able to afford customised AI development, but they can significantly simplify their day-to-day work with off-the-shelf AI applications. Use the expertise of external partners at the beginning before you spend time building up your own team. As an AI agency, analysing AI opportunities is part of our daily business.

It is important to use artificial intelligence strategically: Just because a chatbot can be quickly integrated into your process with the help of an AI agency does not mean that the investment is worthwhile in your specific case and that the added value is in proportion to your costs. Start with your goals and then check whether and how you can achieve them faster or more cost-effectively with the help of artificial intelligence.

Artificial intelligence describes digital applications that can perform tasks that normally require human intelligence, such as understanding language, making decisions or learning from experience. For this purpose, algorithms are developed that no longer give the computer fixed instructions for action, but instead set it a target. The AI algorithm then finds its own way to the solution based on data, can evaluate its result and gradually improve it.

Generative AI is a subcategory of artificial intelligence that generates content - in text, images, sound or video. The best-known application is probably ChatGPT, which has rapidly popularised AI among the general public. ChatGPT has been pre-trained on a huge database so that the application is able to chat with users via text chat in natural language, for example to answer questions, explain information or analyse files. Generative AI models can be used by companies in many areas, for example to optimise data analysis, automate customer support, make employee training more cost-efficient and accelerate content creation for marketing and sales.