Structure governance so it can be operated, evidenced, and improved. Our platform provides the foundation to connect AI assets to processes, roles, risks, controls, and documentation.
Book a DemoCapture AI systems and use cases in one place. Link each item to key processes and data inputs for visibility.
Apply risk tiers consistently across AI use, then route reviews to the right roles for approval. Record decisions and exceptions with traceability.
Register internal AI tools in one place, with clear ownership and visibility. Apply usage tiers and approvals for higher-risk use.
Use dashboards to report to leadership. Generate audit-ready evidence showing what changed, who approved it, and what controls apply.
Maintain a single inventory of every AI system with purpose, owner, lifecycle stage, risk level, and approval status.
Connect AI use cases to datasets and enabling systems, and assign clear roles so ownership and impacts are visible.
Centralize governance requirements and map them to your AI systems and processes using recognized frameworks.
Track risks and controls, assess residual risk, and identify compliance gaps and remediation work without spreadsheets.
Record incidents, investigations, decisions, and corrective actions to support oversight and continuous improvement.
Produce exportable reports and evidence packages for governance committees, executives, audit, and regulators.
Embed data protection and ethical safeguards into every stage of the AI lifecycle.
Prevent unauthorized use or data leaks by mapping how personal information is collected, used, and stored.
Innovate responsibly without compromising trust or security.
Make sure systems are used only for approved purposes with clear guardrails around how AI is developed, deployed, and managed.
Reinforce accountability through documented approvals and automated checks that prevent unauthorized models or data use.
Maintain control and avoid costly legal issues and reputational damage while improving the reliability and integrity of AI-driven decisions.
Embed fairness, transparency, and accountability into how AI systems are built and used.
Ensures decisions made by algorithms are explainable, consistent, and based on accurate, representative data.
Have more reliable and equitable outcomes by Reducing legal and regulatory risks, improving data quality,
Book a demo to see how AI governance works in practice, including how to register your AI systems, assign accountability, manage risk and controls, and produce audit-ready evidence packages without adding unnecessary overhead.
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