Engineering-grade AI advisory for organisations where models touch regulated outcomes, safety-critical systems, or public trust.
MRBF engages on AI where the question is not 'can a model be built' but 'should it be deployed, how is it governed, and who is accountable when it fails'. We work alongside in-house data, engineering, and risk teams rather than replacing them.
Model inventories, risk classification, human-in-the-loop design, and the documentation regimes that survive audit and regulator scrutiny.
From pilot to production — data pipelines, MLOps governance, accountability structures, and the operating model that owns models in the field.
Support to government on AI procurement standards, evaluation frameworks, and sectoral guidance where general-purpose policy meets specific industry risk.
Independent review of AI ventures and AI components — model defensibility, data rights, infrastructure costs, and credible path to regulatory acceptance.
Where AI enters control loops, safety cases, and certification environments — review of assurance evidence, failure modes, and operator interface design.
Executive and board-level programs that build the technical literacy required to govern AI, not just authorise its purchase.
From GenAI pilots to autonomous agents — retrieval architectures, tool-use safety, human-on-the-loop design, and the controls required before agents touch production systems or customer-facing decisions.
Forward-looking posture work for organisations exposed to rapidly improving foundation models — capability tracking, scenario planning, dependency mapping, and the institutional changes required as model capability compounds.
Sovereign model and compute strategy — training and inference footprint, data residency, energy and grid interface, and the long-cycle infrastructure choices that decide who controls the stack.
Illustrative scenarios drawn from the kind of problems MRBF is equipped to engage on in this domain. Anonymised by design — specific principals and outcomes are confirmed in scoping and governed by confidentiality.
Designed evaluation criteria, evidence requirements, and post-award assurance obligations for AI-enabled tooling, aligned with emerging national policy.
Illustrative · scoped under confidentiality
Independent review of an ML-based predictive maintenance system before fleet-wide rollout, covering data integrity, drift management, and operator override design.
Illustrative · scoped under confidentiality
Engineering-led diligence on data moats, infrastructure economics, and regulatory exposure for a growth-stage AI company entering regulated markets.
Illustrative · scoped under confidentiality
Engagements begin with a scoping conversation. We confirm the problem, the senior practitioners or specialists who would deliver, and whether MRBF is the right counterpart before any work starts.