Proven Governance Outcomes

Practical examples of how we navigate the intersection of technical innovation and regulatory compliance.

Client Profile

UK-Based Mid-Market Asset Manager

AUM: £15bn. Facing pressure from institutional investors to provide AI transparency reports.

Services Engaged

  • AI Risk Assessment
  • Policy Framework Design
  • Technical Control Implementation

The Challenge

The client had deployed three pilot LLMs for research summarisation without a formal risk framework, leading to a "red" flag during a routine internal audit regarding data leakage and model hallucination.

Our Approach

We implemented a tiered governance structure, classifying models by risk level. We established automated technical guardrails to filter sensitive PII and provided a custom reporting template for the Board Risk Committee.

Key Outcome

Fully compliant AI inventory established within 8 weeks, passing subsequent external audit with zero high-risk findings.

Client Profile

Tier-2 European Fintech Bank

High-frequency lending environment. Pre-empting EU AI Act requirements.

Services Engaged

  • Technical Controls Architecture
  • AI Incident Response Planning

The Challenge

Automated credit-scoring models were operating as "black boxes," making it impossible for the legal team to explain specific lending rejections under GDPR and MiFID II requirements.

Our Approach

We deployed explainability wrappers around existing models and integrated a human-in-the-loop review process for high-value loan appeals, ensuring clear audit trails for every decision.

Key Outcome

Achieved a 40% reduction in compliance review time while ensuring 100% explainability for rejected credit applications.

Client Profile

Global Insurance Provider

Multiple international jurisdictions. Rapidly escalating AI cloud costs.

Services Engaged

  • AI Cost & Model Governance
  • AI Literacy Training

The Challenge

Uncoordinated model acquisition across different business units led to duplicated efforts, escalating API costs, and varying standards of model reliability.

Our Approach

We centralised the model inventory and established a "Gatekeeper" protocol for new AI spend, combined with intensive training for department heads on cost-effective model selection.

Key Outcome

Identified and eliminated £250k in redundant annual API spend while upskilling 45 senior leaders in AI risk management.