A complete method. Not a black box.
Matr skips no step.
From understanding the business need to monitoring the deployed model, every phase follows the conventions of a rigorous data science team. And every phase is validated by your teams before moving to the next.
The cycle doesn't end at deployment. Continuous monitoring ensures the model remains performant over time. See the Model Lifecycle page for the full detail.
Five steps. The same standard a senior data scientist would apply.
Step 1. Context extraction
Before modeling, Matr extracts the context from three sources: your team's definition of the problem, your documentation (data catalog, dbt, Notion), and your data itself. The triangulation automated tools skip.
Step 2. Data preparation
100% of a data scientist's work, handled by Matr. Leakage detection, rigorous train/test separation, class imbalance handling, feature engineering. Every transformation is traceable.
Step 3. The modeling stack
Your predictions rest on proven ML models: regression, gradient boosting, specialized neural networks. The LLM understands, orchestrates, explains. It never predicts.
Step 4. Explainability
Two levels, with standard mathematical methods. Global: which variables matter most, with what weight. Local: why this prediction, for this customer. Returned in plain language. Your teams understand, challenge, trust.
Step 5. Bias detection
Matr detects biases before deployment: imbalances, overrepresented categories, performance gaps between segments. The same variable can be a legitimate signal in one context and an unacceptable bias in another. Gender can be meaningful for a cosmetics campaign and inadmissible in candidate screening. Matr surfaces. Your teams decide.