A PoC (Proof of Concept) takes 4-8 weeks. A production-ready system takes 3-6 months. It depends on: problem complexity, data quality, performance requirements, existing infrastructure. We work in agile mode with iterative releases: you get a first working model within 1 month, then we progressively improve accuracy and features.

Each project starts with a Proof of Concept (PoC) phase, which is useful for validating technical feasibility and potential business impact. Following validation, we develop comprehensive and scalable solutions tailored to the scope and complexity of the project. Activities include data engineering, model development, testing, deployment, documentation, and training. We also manage evolutionary maintenance, continuous monitoring, and any necessary model retraining. For projects with measurable KPIs, we adopt collaboration formulas based on success fees. In any case, we always evaluate the expected ROI before launch to ensure sustainable investments and concrete results.

Yes. We implement XAI (Explainable AI) with techniques such as SHAP, LIME, and attention maps to make model decisions transparent. For critical applications, we use interpretable models (decision trees, GAM). We manage bias and fairness with dataset audits and equity metrics. Privacy: federated learning, differential privacy, model encryption when necessary. GDPR compliance guaranteed.

We implement complete MLOps pipelines: monitoring performance in production (accuracy decay, data drift), automatic alerts, scheduled or on-demand retraining. We manage model versioning, A/B testing for new versions, and secure rollbacks. We provide real-time dashboards for model KPIs. Maintenance is included in the contract with defined SLAs (e.g., monthly retraining, incident response < 4 hours).

No. We take care of everything: development, deployment, maintenance. We train your team on how to use the system (no technical background required).