Every AI programme in a regulated industry needs to be governed, auditable, and measurable. We design the governance frameworks, compliance reviews, and value measurement structures that let you demonstrate returns to boards and satisfy regulators.
The regulatory landscape for AI in financial services and insurance is complex and still evolving. FCA AI principles, Lloyd's Blueprint Two, EU AI Act risk classifications, and ICO guidance create overlapping obligations that need a single, coherent framework — not four separate compliance projects.
Regulators require that AI-assisted decisions be explainable, traceable, and auditable. Systems deployed without these capabilities need to be retrofitted — which is significantly harder and more expensive than designing them in from the start.
Without clear baselines, agreed KPIs, and attribution models established before deployment, AI investment becomes difficult to justify and impossible to scale. Every subsequent AI programme becomes harder to fund when you can't prove the last one worked.
Regulated AI requires human oversight on material decisions — but HITL designed as an afterthought creates bottlenecks rather than accountability. The governance framework needs to define exactly which decisions require human review, and how that review is documented and audited.
PRA SS1/23 and FCA model risk guidance require that firms identify, assess, and mitigate the risks associated with AI and machine learning models. Most organisations have no framework — and are exposed on their next supervisory review.
Strategy engagements that are fixed-scope, time-boxed, and leave you with something you can act on immediately.
Most organisations treat AI governance as a constraint. We treat it as an architecture requirement — designed in from day one, not bolted on before a regulator visit. The result is AI that's faster to approve, easier to audit, and more trusted by the people who use it.
We begin every governance engagement with a structured audit of your existing AI use cases, systems, and processes — mapping each against the applicable regulatory requirements. The output is a prioritised gap register with a clear remediation plan, not a generic compliance checklist.
XAI, audit trails, HITL workflows, and model risk controls are most effective when designed from the first day of architecture — not added in response to a regulatory inquiry. We embed governance requirements into every system specification, so compliance is built in, not bolted on.
Value measurement needs baselines established before deployment — not retrospective estimates after the fact. We define KPIs, baseline measurements, and attribution methodology at the start of every AI programme, giving you a credible ROI narrative from day one.
The deliverables from our governance work are designed to be presented to your board and to regulators — not just filed in a compliance folder. Policy templates, risk registers, and evidence packs are written for the audiences that need to approve and scrutinise your AI programme.
AI governance is not a one-time exercise. We design the monitoring frameworks and regulatory tracking processes that keep your governance posture current as both your AI systems and the regulatory landscape evolve.
Our governance work applies to AI systems built on all major platforms. We've worked with Lloyd's, FCA-regulated firms, NHS trusts, and mid-market enterprises across every major cloud and AI stack.
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Build the AI strategy and roadmap that governance frameworks are designed to protect and enable.
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AI agents built with XAI and HITL by design — every output explained, every decision auditable.
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Governed, quality-controlled data foundations that give your AI systems reliable, compliant inputs.
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We cover FCA, Lloyd's Blueprint Two, EU AI Act, and GDPR in one fixed-scope engagement. 3 weeks. Clear gap register. Board-ready deliverables.