PiR2-IT
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PiR2-IT
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AI governance, data foundations and deployable operating models for production AI in regulated and complex environments.
Scaling needs governance, data control and architecture that pilots never required.
Quality, ownership and access constrain AI more than model sophistication — often discovered too late.
It is written as documentation after deployment decisions, when the risk is already built in.
Roles, governance structures, decision rights, lifecycle ownership and human-in-the-loop controls.
Data sources, ownership, quality, lineage and access patterns.
Pipelines, operational controls, drift monitoring and release governance within platform and security constraints.
Explainability, review gates, bias controls and governance evidence for regulators.
Banks, insurers and financial infrastructure deploying AI under DORA, SR 11-7 and the EU AI Act.
National agencies and international organisations where accountability and explainability are essential.
Mission-critical AI requiring data provenance, model integrity and human oversight.
Current state, delivery friction, architecture debt and the decisions that must be made first.
Reference architecture, governance model, control points, sequencing and evidence structures.
Design authority, prototype validation and assurance reviews as the programme proceeds.
No. It covers the full range, from classical machine learning to large language models.
Designing it together with architecture and data decisions, instead of documenting it afterwards.
When pilots exist but scaling, auditing or governing them is unclear — or when regulation, complexity or data quality block deployment.
Deliberately yes, because AI systems introduce distinct security surfaces.
PiR2-ITIndependent consulting for institutions, regulated sectors and high-trust programmes.