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Using AI with Machine Learning Intelligently
Knowing what you can do. Knowing how.
Artificial Intelligence (AI) isn’t as new as you might think. But only now are sufficient development data available, high computing power affordable, and algorithms and software advanced enough to make AI usable for a wide range of applications.
Currently, AI is often employed in small, integrated applications to eliminate errors in processes and take over monotonous, repetitive tasks from humans. AI has become quite adept at this. However, the real intrigue begins when machine learning and deep learning enable learning that truly enhances processes. That’s why our AI team, comprising computer scientists, mathematicians, and physicists, has focused on machine learning for several years. We specialize in:
Our Focus:
NLP Natural Language Processing
From the start, our goal was to use AI meaningfully in our client projects. We concentrated on machine learning because its models can be effectively trained for specific applications tailored to client needs. We train machines to recognize and process language, for example, using Retrieval-Augmented Generation (RAG). Here’s a practical example.
Our Focus: Computer Vision
We work on computer vision, the AI solution for automated processing and analysis of visual data, utilizing a wide variety of models. Depending on the task, we use Classification, Object Detection, Image Segmentation, traditional Convolutional Neural Networks (CNNs), modern multimodal deep learning models (e.g., LLaVA), or Vision Transformers.
In this context, we offer:
- Consulting, courses, and seminars at all technical levels, whether for AI tech enthusiasts, regular software developers, or non-technical field staff.
- Automated information extraction from documents using AI to reduce manual, repetitive work.
- Development of an AI assistant that can answer questions and provide advice based on internal documents or documentation (keyword “Retrieval-Augmented Generation”).
- Customized fine-tuning of local AI models for specific client tasks and execution within the client’s infrastructure.
- System-specific AI assistants with programmatic access to client-specific internal interfaces, capable of autonomously making intelligent queries to client systems (keywords: Function Calling or Agentic AI).
- Advice on purchasing AI-relevant hardware for training and deploying trained models.
Why Train Your AI?
Of course, the datasets used to train ChatGPT or Claude are extensive. However, for highly specific tasks, results improve when a local AI model is trained with application-specific data. Based on our client experience, training with your own data and local models is less complex than commonly assumed.
Your own AI:
• Meets all specific requirements.
• Ensures data sovereignty.
• Offers greater accuracy and efficiency.
Get advice now on how to achieve a whole new level of optimization in your IT project with AI.
Artificial Intelligence (AI), Machine Learning, and Deep Learning: What’s the Difference?
Artificial Intelligence
Machine Learning
Deep Learning
AI refers to the concept of computers performing tasks that usually require human intelligence. Often, the programming effort is substantial, as rules and logic need to be established and programmed, frequently in the form of algorithms.
Machine learning is a subfield of AI that takes a different approach: it relies on large datasets from which the machine learns according to specific algorithms. The type of data and the model rules must be precisely defined to achieve the desired results. Within these rules, the machine learns independently, correcting and improving its processes during training.
Deep learning, a subset of machine learning, is particularly suited for complex tasks like image and speech recognition. Neural networks are defined and trained, enabling the machine to recognize and interpret images and language.
Use Cases from Client Projects
Example Chatbot: A chatbot is designed to answer customer questions related to specific product details (e.g., functions of specific product series, unlocking a code, recognizing customer numbers, etc.). The chatbot is trained on the typical queries of this customer base and provides responses tailored to the company’s product lines.
Example Document Management: Certain documents need to be automatically sorted into specific areas within the internal database. Using a trained AI, documents can be identified based on their content and automatically allocated to the appropriate sections.
Identifying AI Potential
The optimization potential of AI applications and the possibilities of individual training are as diverse as our clients’ IT projects. The biggest advantages include:
- Saving resources (costs, time, and manpower).
- Improving quality (reducing error rates, shortening response times).
- Enhancing planning foundations (interpreting large datasets, making predictions).
Often, companies fail to recognize AI potential due to entrenched processes. That’s why it’s worth booking an AI check. Our AI experts examine the process to be implemented and suggest — where appropriate — ways to integrate AI.
I want to schedule an AI check.
AI Glossary:
- Algorithm: A defined sequence of steps or instructions executed to solve a specific problem or complete a task.
- Generative AI: The discipline where AI is enabled to not just reproduce learned content but also generate new content. This could include images, texts, music, videos, and other file types.
- NLP (Natural Language Processing): Focuses on the interaction between computers and human language. The goal is to understand, interpret, and generate natural language in a way that feels human-like and responds sensibly from a human perspective.
- RAG (Retrieval-Augmented Generation): A method to direct model outputs and ensure factual correctness. RAG combines querying large document collections with generating precise answers based on the available document contents.
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