DataRobot

AI Model Inventory

Centralized registry of all AI/ML models in use across the organization, with ownership, lifecycle stage, and risk classification.

Policy Management

Authoring, versioning, and distribution of AI usage policies mapped to regulations.

Risk Assessment Workflow

Guided workflows for completing AI impact assessments, risk scoring, and approval routing.

Bias & Fairness Testing

Automated statistical testing for disparate impact across protected attributes, with audit-ready reports.

Explainability

SHAP, LIME, counterfactual, and feature-importance explanations for model decisions.

Model Monitoring

Production monitoring for performance, drift, data quality, and fairness regressions.

LLM Red Teaming

Automated adversarial testing of LLMs for jailbreaks, prompt injection, and unsafe outputs.

Audit Evidence Collection

Automated collection, hashing, and retention of evidence (model cards, test results, approvals) for audit.

Data Lineage

Tracking the origin, movement, transformations, and consumers of data used to train and serve AI systems — required for EU AI Act Article 10 data governance and GDPR Article 30 record-keeping.

Drift Detection

Automated detection of distribution shift, feature drift, prediction drift, and performance degradation in deployed ML/AI models.

LLM Evaluation

Systematic testing of LLM outputs for correctness, relevance, safety, and consistency using automated scorers, rubrics, or human review.