Our offer in the area of AI

Chatbot-integration: We develop and implement chatbots to improve your customer service and make internal processes more efficient.
Use of language models: We use advanced language models to analyse internal documents and other sources and gain valuable insights.
Architectural support: Our team supports you in selecting and implementing the right architecture, whether in the cloud, in hybrid environments or on-premise.
Smooth implementation: We ensure seamless integration of our solutions into your existing infrastructure to enable smooth operation.
Optimisation of your processes: Our aim is to increase your company's performance, whether through improved customer service, automation of internal processes or increased efficiency in data analysis.

Efficient information retrieval with ChatGPT-based document retrieval

Our team has developed an innovative demo that is based on ChatGPT and makes it possible to perform targeted queries within large documents (Word, PDF). It is important to emphasise that ChatGPT does not and should not have any knowledge of the content of internal documents. There is no training to ensure that sensitive information is not leaked. Instead, only ChatGPT's application programming interface (API) is used to ask questions, which are then answered by the content of the documents. Customers benefit from this solution by saving a considerable amount of time when searching for information in large documents and by being able to obtain precise answers to complex questions without having to carry out time-consuming manual searches. An additional added value is that the query is language-independent. It does not matter in which language a document is written, as the answer is provided in the same language in which the question is asked.

How AI can also improve your business processes

Automation of routine tasks: AI can automate repetitive and time-consuming tasks, such as data processing, document management, invoicing and reporting

Optimisation of decision-making processes: By analysing large amounts of data, AI can help to make informed decisions by recognising patterns and trends and providing forecasts for future developments.

Personalised customer interaction: AI-supported systems such as chatbots and virtual assistants can offer personalised customer care by processing individual enquiries, making product recommendations and providing support in real time.

Proactive risk management: AI can help identify potential risks and opportunities at an early stage by analysing data and identifying patterns that indicate risks, such as fraud, supply chain disruptions or market fluctuations.

Improving product quality and development: By analysing customer feedback, production data and market trends, AI can help to optimise products, reduce errors and drive innovation.

Increasing efficiency in logistics and supply chains: AI can help optimise supply chains by forecasting delivery times, managing stock levels, planning routes and predicting bottlenecks.
HR management and talent acquisition: AI-supported systems can support companies in candidate selection by analysing CVs, evaluating qualifications and identifying potential candidates who best match the requirements.
Predictive maintenance: By analysing sensor data and historical maintenance data, AI can predict when machines and systems need to be serviced in order to avoid breakdowns and maximise uptime.
Cost optimisation and budget planning: AI can help to optimise costs by analysing expenditure, identifying potential savings and allocating budgets in line with business objectives.
Improving customer satisfaction and customer loyalty: By analysing customer behaviour and offering personalised services, AI can help to increase customer satisfaction and strengthen customer loyalty.

What is AI?

Artificial intelligence (AI) refers to the ability of computers to perform tasks that normally require human intelligence. This includes the ability to recognise patterns in data, solve problems, draw conclusions and make decisions. AI can be used in various fields, including image recognition, language processing, medical diagnosis, financial analysis, robotics and much more. It makes it possible to solve complex problems, increase efficiency and drive innovation. From chatbots to self-driving cars, AI is revolutionising the way we work, communicate and live.

What is LLM (Large Language Model)?

A Large Language Model (LLM) is a powerful artificial neural network that has been trained to understand and generate natural language. These models are able to analyse huge amounts of text data and recognise patterns and correlations in the language. An LLM can perform various tasks such as text generation, translation, text comprehension, question-answering systems and more. These models have enabled significant advances in AI research and are used in a wide range of applications, from chatbots to automatic text correction and machine translation.

What is machine learning?

Machine learning (ML) is an area of artificial intelligence (AI) that enables computers to learn from data and make predictions without being explicitly programmed. It involves three main steps: Data preparation, model training and evaluation/application. During training, the model learns patterns and relationships in the data to make predictions about new, unlabelled data. ML is used in various applications, including image recognition, speech processing, financial analysis and medical diagnosis.

What is NLP (Natural Language Processing)?

Natural Language Processing (NLP) is an area of artificial intelligence that deals with the interaction between computers and human language. The aim is to give computers the ability to understand, interpret and generate natural language. NLP includes tasks such as speech recognition, text processing, translation, question-answer systems and sentiment analysis. It uses machine learning and statistical modelling techniques to recognise and understand patterns in language data.

What is deep learning?

Deep learning is an advanced method of machine learning based on deep neural networks. These networks consist of many layers of artificial "neurons" that work together to recognise and learn patterns in data. Through training with large amounts of data, these models can perform complex tasks such as image recognition, language processing and much more. Deep learning has led to significant advances in artificial intelligence and is used in many applications to develop intelligent solutions.

What is RAG (Retrieval Augmented Generation)?

Retrieval Augmented Generation (RAG) is an innovative approach in the field of artificial intelligence (AI) that aims to improve the quality and relevance of generated texts by drawing on a variety of external sources or "retrievals". Essentially, RAG combines generative models such as GPT (Generative Pre-trained Transformer) with information retrieval techniques to create more accurate and informative texts. This allows the model to access a wide range of knowledge and integrate it into the generated text in real time. RAG is used in various areas such as automated writing, content creation, question-answering systems and improving the quality of information in texts.

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ai for business processes