How we work
We approach data collection as an extension of your ML workflow, focusing on real conditions and model behavior
Weakness on edge cases
Model performance drops on rare, complex, or atypical real-world scenarios
Limited attack coverage
The dataset includes insufficient attack scenarios, reducing robustness against adversarial or harmful inputs
Misaligned training data
Training data does not accurately reflect real production behavior or user interactions
Production-aligned data
Data collection is designed around your production pipeline and mirrors real user behavior
Data collection principle. Each project starts with understanding the target scenarios and system behavior. Based on this, we organize a data collection approach that reflects real usage conditions and relevant attack cases.
Quality and consistency. Data is collected with a focus on consistency across scenarios and conditions, ensuring it can be effectively used in model training and evaluation.
Data Handling and Ownership. All collected data is treated as project-specific and prepared for direct use in ML workflows.
All data rights are transferred to the client
Datasets are not reused, shared, or resold across projects