Holistic AI

AI Model Inventory

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

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.

LLM Red Teaming

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

Model Monitoring

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

Audit Evidence Collection

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

Regulatory Intelligence

Ongoing tracking of AI laws, standards, and enforcement, mapped to your model inventory.

Audit Logging

Tamper-evident logging of governance events (approvals, model changes, policy decisions) required by EU AI Act Article 12 and similar regulations.

Drift Detection

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

LLM Guardrails

Runtime input/output filtering for LLMs — PII redaction, toxicity blocking, prompt injection defense, policy enforcement.

Hallucination Detection

Automated scoring of LLM outputs for factual accuracy and groundedness, typically via retrieval-backed verification or reference comparison.

Explainability

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