Custom machine
learning models.
We develop custom machine learning models that are perfectly suited to your data, goals, and processes – powerful, maintainable, and directly integrable into your system landscape.
Where we help
Challenges
we solve for you.
Many promising ML projects fail due to a few common, solvable issues. We will help you overcome them.
Inadequate Results
Standard solutions deliver inadequate results for special requirements.
Untapped Data
Your own data is not used optimally or remains untapped.
Integration Issues
ML models cannot be seamlessly integrated into existing systems.
Lack of Expertise
A lack of expertise or capacity to implement in-house AI projects.
The solution:
Custom ML models from Dentro.
Machine learning models are developed individually for you. We would be happy to show you what is possible with your data.
Quick Facts
ML Model Development highlights at a Glance.
Our custom ML solutions offer far more than standard tools – they are flexible, powerful, and can be seamlessly integrated into your existing infrastructure.
Custom model architecture
We select and develop model structures that are an exact fit for your use case—no compromises, no unnecessary overhead.
Trained on your data
Instead of using generic datasets, our model training is based on your real, company-specific data for maximum relevance and precision.
Different tasks – one partner
Whether it’s classification, regression, anomaly detection, or NLP, we support a wide variety of use cases.
Easy to integrate
We build your ML models so they can be directly embedded into existing applications or processes—via API or as a module.
Focus on maintainability and scalability
Our models are not only high-performing but also robust and future-proof—built to be modular and well-documented.
Iterative development with real-time feedback
Through agile collaboration, we deliver initial results early and continuously improve them based on real-world usage.
FAQs
Answers to the most
frequent questions.
What are custom ML models particularly suited for?
Custom ML models are ideal when your requirements go beyond what standard solutions can offer—for instance, with complex classification tasks, custom forecasts, anomaly detection, processing specific language or images, or when your data is highly domain-specific. They allow you to create a concrete competitive advantage from your existing data.
What is the advantage over pre-trained AI models?
Pre-trained models are often general-purpose and trained for broad applications, causing them to lose precision on specific tasks or with industry-specific data. With a custom model based on your data, you significantly increase accuracy, relevance, and control—and you avoid “black box” decisions that cannot be easily explained.
What data do I need?
That depends on the use case. You can use structured data (e.g., numbers, tables), unstructured data (e.g., text, PDFs), images, time series, or sensor data. We help you identify, prepare, and utilize the right data sources—even if your data doesn’t seem “ML-ready” at first glance.
How long does it take to develop a model?
The duration depends on the scope and complexity of the project. Initial results—such as a working prototype—are often possible within a few weeks. A fully mature, production-ready model can take between 1 and 3 months, depending on the requirements. We work iteratively, so you see value early on.
What technologies does Dentro use?
We work with modern ML frameworks like TensorFlow, PyTorch, scikit-learn, spaCy, and Hugging Face. We primarily program in Python, sometimes supplemented by R or specialized tools. However, we also adapt to your system landscape—whether you prefer open-source solutions or want to integrate established enterprise solutions.
Can we operate the model ourselves later?
Yes, you retain full control. We deliver documented, maintainable code and, if necessary, a container setup (e.g., via Docker) that you can run in your own infrastructure or cloud. We can also handle hosting and monitoring upon request, but there is no dependency—the model belongs to you.
Is the solution secure and GDPR-compliant?
Data protection is an integral part of our work. Your data either remains entirely within your environment or is processed in a GDPR-compliant manner—for example, on encrypted servers within the EU. We treat sensitive information with strict confidentiality. Additionally, you can transparently track all access and processing activities.
How is model quality ensured?
During development, we rely on thorough evaluation with appropriate metrics like Accuracy, F1-Score, Precision/Recall, or ROC AUC, depending on the use case. Before the final integration, we test the model intensively for robustness and practical suitability. We also offer the option to incorporate human feedback into the optimization process (Human-in-the-Loop).
What happens if our data changes later?
Not a problem—our models are prepared for that. They can be retrained regularly or as needed. Integrating new data sources or adjusting the task requirements is also possible. This way, your system always stays up-to-date—both technically and in terms of content.
Can multiple models be combined?
Yes, and it is often a sensible approach. This is referred to as ensemble learning or modularization. For example: one model classifies texts by topic, while another detects sentiment—both results can be combined. Or you could separate preprocessing, decision-making, and post-processing into specialized models that work together.
ML Models in Action
Practical Examples & Use Cases.
From intelligent quality control to personalized forecasting models—our custom ML solutions demonstrate their strength wherever standard solutions fall short.
Ready?
As you can see, custom ML models are definitely something we can support with. Ready to discuss your use case and see what’s possible?