Making AI regulation operational.
RuleBridge Advisory helps organisations turn regulatory expectations, business priorities and technology change into AI governance that works in practice, can be evidenced and holds as the rules and the systems keep changing.
Rules do not operationalise themselves.
Regulation reaches deep into how organisations design, acquire and operate technology — yet the hardest work begins after the rules have been interpreted. Regulation can set duties. Policies can set intent. Technical controls can shape system behaviour. None of them, on its own, determines how responsibility will work when AI is used day to day.
As AI moves into decisions, workflows and third-party services, organisations need more than principles or a compliance map. They need clear ownership, real authority, governable handoffs and evidence tied to action.
RuleBridge works at that operating layer — translating confirmed requirements and risk decisions into governance structures that people can operate and leaders can stand behind.
One discipline. Three places where accountability must hold.
RuleBridge Advisory focuses on operational accountability for AI at the three points where it most often breaks: inside the organisation, at the human–system boundary, and across the external ecosystem.
Organisational AI Governance
When AI use expands faster than ownership, RuleBridge designs the mandates, decision rights, lifecycle processes and evidence that make governance part of normal operations.
Explore Organisational AI Governance →Human Oversight & Decision Accountability
When people are expected to oversee AI without real authority, RuleBridge defines what must be understood, challenged, overridden, stopped and recorded.
Explore Human Oversight & Decision Accountability →External & Agentic AI Governance
When AI depends on — or acts through — external models, platforms, vendors and autonomous agents, RuleBridge maps responsibility, delegated authority, change control and exit.
Explore External & Agentic AI Governance →Organisations engage RuleBridge when a material AI decision has no clear owner, human oversight exists only on paper, responsibility for a vendor, model or agent is fragmented across functions, or a regulatory requirement has to become an operating process.
Whatever the starting point, the engagement is shaped by the problem — and runs on the same method throughout:
Operating experience behind the practice.
RuleBridge is led by Maja Kurek.
She brings an implementation-focused perspective to AI governance, grounded in close to a decade of hands-on experience across Google, Photomath and Uber — where regulatory change had to be carried through live operations, across borders and at platform scale.
Her record includes leading regulation-driven operational change in a platform business; full-lifecycle responsibility for the human layer of an AI-powered learning product — 10,000+ external experts; post-acquisition integration under EU regulatory requirements; and a cross-border provider transition focused on data protection, access controls, payment security and operational continuity.
That experience sets RuleBridge's central test: real authority behind formal responsibility, accountability that holds across handoffs, and decisions that stay traceable as systems change.
About the Principal →AI Change Accountability & Reauthorisation
AI systems do not remain fixed after approval — new models behind the same product name, provider changes, wider permissions, drifting use. A practitioner-derived method with ready-to-use templates for every record. Applied in a first organisational field pilot; the field evidence is published as a field study on SSRN.
Open the tool →When accountability needs a design.
RuleBridge works with organisations that have moved beyond whether to use AI and now need to determine how responsibility will work around it. For a scoped governance question, a design engagement or an executive discussion: