AI Governance for your entire AI landscape
Organisations are adopting AI faster than they can govern it. Systems are being built, bought, and embedded into critical processes across the business, often without a clear view of where they sit, who owns them, or what regulators expect.
As AI becomes central to decision-making, governing it responsibly has become far more complex.
Yields provides a clear and consistent approach to AI governance, so teams can stay in control of their entire AI landscape, from first use case to full-scale deployment.
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From model risk management and performance monitoring to AI governance. Yields provides one clear platform to manage every model across its lifecycle.
Why is AI governance important?
AI governance teams face a fast-expanding landscape of expectations and challenges:
Without structure, AI risk increases and oversight becomes harder to maintain. Yields supports the full AI governance process so teams keep control of their AI landscape as it grows.
AI systems Yields helps you govern
Yields serves as a comprehensive inventory for AI use cases, governing the full AI lifecycle. We provide an integrated solution that supports risk management throughout the entire process, including the development of AI models.
Predictive & analytical AI
Generative & language-based AI
Operational & decisioning AI
High-risk & regulated AI
A structured AI governance process
Inventorize AI use cases
Upload your documentation. Our AI Assistant will build your AI inventory for you.
Risk score & evaluate
Yields maps your use case to a selected regulatory framework and shows you your risk and compliance scores together with what’s missing.
Mitigate risks
See the highest-risk tasks first, with step-by-step actions to close the gaps.
Prove compliance
Generate structured, traceable reports and export them, ready to be shared with regulators or clients.
How AI governance connects to model risk management
Many AI use cases rely on underlying models. Yields connects AI governance with model risk management in a simple way.
Validators review the model, while governance teams assess the broader use case and its controls. Both teams work with the same information, which avoids duplication and improves clarity.
Why companies choose Yields
Clear oversight
One place to understand every AI system and model.
Faster workflows
No more scattered files or manual tracking.
Audit-readiness built in
Evidence, documentation, and reporting with minimal effort.
Trusted expertise
Years of experience in regulated model risk environments.
Managing AI risk in practice
Download our practical guide to AI governance and managing AI Risk, built on a decade of real-world experience. Discover how to operationalize AI governance with clarity, structure, and confidence.
Yields Named Category Leader in Chartis AI Governance Report 2025
Yields achieved ‘Best-in-class’ scores across nearly every capability category, including Governance, Data Management, Model Management, and Workflow.
Govern every AI system with yields
Discover how our platform brings structure, transparency, and confidence to your AI landscape, without consultants, spreadsheets, or guesswork.
FAQ
AI governance is the structured practice organisations use to oversee, assess, document, and control the risks of AI systems throughout their lifecycle. It ensures AI is transparent, accountable, compliant, and aligned with organisational policy and regulatory expectations. Yields provides a technology platform to bring structure and oversight across the full AI landscape.
AI adoption is accelerating while regulation, led by the EU AI Act, sets clear expectations for transparency, risk management, and accountability. Without structure, organisations struggle to identify high-risk systems, prove compliance, and manage AI risk consistently. Governance turns AI from an unmanaged exposure into a controlled, auditable capability.
Yields maps each AI use case to the relevant regulatory framework, supports risk-based classification, and helps generate the documentation and evidence required for audits, including fundamental rights impact assessments for high-risk systems. This makes it easier to demonstrate compliance and stay aligned as the regulation evolves.
AI governance and model risk management are complementary. Model risk management focuses on the technical validation and monitoring of individual models, while AI governance assesses the broader use case, its controls, and its regulatory context. Yields connects both so teams work from the same information and avoid duplicating effort.
AI governance typically involves risk teams, compliance officers, model owners, data scientists, legal, IT, and senior management. Yields provides a shared framework that connects all stakeholders with clear roles and responsibilities.
