Machine learning and AI

The hard part of model development is not the model but keeping it in production. If nobody tracks where the training data came from, which version produced which decision and how behaviour drifts over time, even a well-performing model is not an asset the institution can carry forward.

Scope

• Problem definition and clarity about the question the model actually answers
• Data preparation, feature engineering and data quality checks
• Training, evaluation and versioning
• Productionisation: serving, fallback path and monitoring
• Auditable storage of decision records

How we work

A model's output is a recommendation, not an automatic verdict. Who makes the decision, where the model enters the process and when it hands back to a person are defined up front. If a model cannot be explained, it does not go to production.