One platform. 12 modules. Every governance obligation.

AI Model Inventory

One source of truth for every AI system in your organization.

Catalog every AI / ML system, foundation model integration, and AI-assisted decision flow with risk classification, ownership, lifecycle state, and a full dependency graph. Required for OMB M-24-10 federal inventories, NAIC AI bulletin governance, and EU AI Act Annex VIII technical documentation.

Compliance Engine

Map every model to every framework that governs it.

RAG-powered question answering against ingested regulatory text with verifiable citations, plus per-model gap analysis across 32 frameworks. Conformity assessment workflows for EU AI Act high-risk systems, evidence packages exportable to regulator format.

Immutable Audit Trail

SHA-256 hash-chained logs of every AI decision.

Cryptographically tamper-evident audit log of every AI inference, decision, and human override. Each entry hash-chained to its predecessor — any tampering breaks the chain and is detected on verification. Exportable as FHIR R4 AuditEvent for healthcare and as evidence for regulatory examinations.

Bias & Fairness Monitor

Catch fairness regressions before they become violations.

Continuous fairness measurement across protected classes — Disparate Impact Ratio (4/5ths rule), Statistical Parity Difference, Equal Opportunity Difference, and chi-squared significance testing. Demographic differential drift detection. Auto-generated triggers for ECOA Reg B and FCRA adverse-action notices.

Drift & Performance Monitor

Statistical detection when your models stop working as designed.

Continuous distribution-shift and performance-degradation monitoring. Population Stability Index against rolling baselines, Kolmogorov-Smirnov test, accuracy / precision / recall degradation tracking, and demographic differential drift. Flags automatic retraining triggers per model.

Security Posture (MITRE ATLAS)

Threat-model your AI systems against the actual attack surface.

Continuous threat scoring against MITRE ATLAS (Adversarial Threat Landscape for AI Systems) — five primary threats with weighted scoring and critical override. Prompt injection detection, training-data poisoning monitoring, model-extraction surveillance, supply-chain provenance, and adversarial-example exposure assessment.

Explainability Engine

Per-decision explanations that hold up in front of regulators.

Multi-audience explanation generation for every decision — patient-language for member-facing notices, clinical / operator language for human-in-the-loop reviewers, and regulator-grade explanations for examination evidence. Feature-importance summaries grounded in the model's actual inputs.

Confidence & Verification

Calibrated confidence, multi-model verification, automatic escalation.

Every AI decision carries a calibrated confidence score with automatic routing — auto-approve, flag-for-review, or human-required. Multi-model verification runs the same input through a primary plus secondary model and surfaces disagreements; configurable to auto-accept, flag, or block based on delta thresholds.

Adverse Action Notice Engine

Automatic notices that satisfy ECOA, FCRA, and ACA §1557.

When an AI denies credit, coverage, or another regulated benefit, the platform auto-generates a notice that satisfies the applicable framework — ECOA Regulation B (30-day window with specific principal reasons), FCRA §615(a) (with credit-score disclosure), or ACA Section 1557 health-coverage notice requirements.

Evidence & Incident Workbench

Examiner-ready evidence packs and regulator-deadline-aware incident playbooks.

One-click evidence packages for any of 14 supported framework audits — with SHA-256 manifest tying each artifact to its source decision. Incident response playbooks auto-generated for six incident classes, each with the regulator deadlines that govern it (GDPR 72h, HIPAA 60d, FTC Safeguards 30d for 500+ consumers).

One-click evidence packs ship for 14 frameworks today, out of the 32 frameworks the platform supports. The rest are on the roadmap to parity.

Executive Dashboard

Board-ready governance scores, narratives, and roll-ups.

Enterprise-wide risk score weighted across four components (bias, drift, security, compliance — 25% each) with a board-ready narrative generator that summarizes posture in plain English and tailors output to four audiences: board, regulator, CISO, internal.

Agentic Governance Layer

Autonomous compliance, governance, and reporting agents — with MCP integrations and scheduled jobs.

Three autonomous AI agents using ReAct (Reason → Act → Observe) loops to handle gap analysis, evidence gathering, and narrative generation without manual orchestration. Five MCP servers expose platform data to external coding agents and tools. Six scheduled background jobs continuously monitor regulatory changes, drift, expiration, and platform health.