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AI is only as effective as the governance behind it. Without clear risk management and oversight, AI models can create more challenges than solutions. We work with you to develop AI governance practices that keep your systems compliant, ethical, and built for long-term success.
Case studies
Case Study
iRobot – A Global Consumer Robot Company
Automating the platform infrastructure.
Case Study
Restaurant Services, Inc.
RSI’s machine learning platform can now support any workload regardless of volume, velocity, and variety of data. As a result, business users have more trust in their data.
Case Study
Lucia Health Guidelines
Solution creation that captures and analyzes EKG images using custom computer vision code. These EKG results are compared with patient data to determine a personalized risk assessment.
FAQs
A governance framework is a structured approach to managing and overseeing AI systems to ensure they operate ethically, transparently, and in line with business objectives. It defines the decision-making processes, roles, and responsibilities needed to mitigate AI risk, maintain compliance, and prevent harm. A strong framework helps organisations balance innovation with accountability, ensuring AI is used responsibly.
A regulatory framework is a set of laws, guidelines, and standards that govern the development and use of AI technologies. It helps businesses navigate legal and ethical obligations, from risk assessment and data protection to bias mitigation and accountability. Staying aligned with evolving AI governance practices ensures compliance while protecting against reputational damage.
The EU AI Act is a proposed regulatory framework designed to ensure the safe and ethical use of AI across the European Union. It categorises AI models based on risk levels, from minimal to high-risk applications, enforcing stricter regulations where needed. Businesses deploying AI in the EU will need to align with these governance structures to ensure compliance, transparency, and responsible AI governance.
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