Frequently Asked Questions

An ML Feasibility Study assesses whether a machine learning approach is technically, economically, and operationally viable for your use case. We evaluate data availability and quality, define success metrics, identify suitable ML techniques, estimate costs and timelines, and assess legal and regulatory constraints. This ensures you invest in ML only when it can deliver measurable business value.

We typically require access to historical and/or real-time data relevant to your business problem. This may include structured data (databases, CSVs, logs) and unstructured data (text, images, audio). During the initial phase, we analyze data quality, completeness, bias, and compliance with GDPR and other regulations before any model is built.

We design scalable and automated data pipelines that cover data ingestion, validation, transformation, storage, and monitoring. Pipelines are built using best practices such as versioned datasets, data quality checks, reproducibility, and auditability. This ensures reliable inputs for ML models and long-term maintainability in production environments.

We work with a wide range of machine learning models, including classical models (regression, decision trees, clustering), deep learning models (neural networks, computer vision, NLP), and hybrid approaches. Model selection is driven by the use case, data characteristics, explainability requirements, and performance constraints.

We prioritize model transparency and explainability, especially for regulated or high-impact use cases. Techniques such as feature importance, SHAP values, model documentation, and validation reports are used to explain model behavior, reduce bias, and support regulatory compliance.

Yes. We design ML solutions to integrate seamlessly with your existing infrastructure via APIs, batch processing, or event-driven architectures. Our focus is on minimizing disruption while enabling measurable performance improvements and faster decision-making.

Sovereign Cloud Hosting ensures that data and workloads remain under European jurisdiction, complying with GDPR and local data protection laws. It prevents unauthorized access by non-EU entities and provides stronger legal certainty for sensitive or regulated data.

Yes. We support deployment on EU-based and Luxembourg-compliant sovereign cloud infrastructures. This includes secure hosting, data residency guarantees, and compliance with European regulatory frameworks, making it suitable for public sector, finance, healthcare, and enterprise use.

Security and privacy are embedded at every stage of the ML lifecycle. We apply data minimization, encryption, access controls, secure model deployment, and continuous monitoring. Personal data is processed only when necessary and in full compliance with GDPR principles.

After deployment, we monitor model performance, data drift, and system stability. We provide retraining strategies, performance reports, and ongoing optimization to ensure the model continues to deliver value as data and business conditions evolve.