Deep expertise where
AI risk is highest
We focus on industries where AI governance isn't optional. High-stakes decisions, regulatory scrutiny, and customer trust demand rigorous frameworks.
Financial Services & Fintech
AI-powered credit decisions, fraud detection, algorithmic trading, and customer service automation face intense regulatory scrutiny under the EU AI Act.
Key Challenges
- Credit scoring algorithms classified as high-risk under EU AI Act
- Explainability requirements for automated decisions
- Bias detection in lending and underwriting models
- Data governance across multiple AI vendors
- Integration with existing compliance frameworks
Our Approach
- Risk classification mapping for all AI systems
- Algorithmic impact assessments
- Model governance frameworks
- Vendor due diligence for AI providers
- Board-level reporting structures
Technology & SaaS
B2B software companies adding AI features need governance frameworks before enterprise sales and funding rounds. Investors and customers demand it.
Key Challenges
- Rapid AI feature deployment outpacing governance
- Enterprise customer due diligence requirements
- Investor expectations for AI risk management
- Multi-jurisdictional compliance (EU, UK, US states)
- Third-party AI integrations (OpenAI, Anthropic)
Our Approach
- AI governance as competitive advantage positioning
- Investor-ready compliance documentation
- Enterprise sales enablement materials
- Scalable governance frameworks
- Continuous monitoring systems
Professional Services
Law firms, accounting practices, and consulting firms using AI for research, analysis, and client work must demonstrate governance to maintain trust.
Key Challenges
- Client confidentiality with AI tools
- Professional liability for AI-assisted work
- Quality assurance for AI outputs
- Staff adoption and training
- Client disclosure requirements
Our Approach
- AI acceptable use policies
- Client communication templates
- Quality control workflows
- Professional indemnity considerations
- Staff training programs
Healthcare & Life Sciences
AI diagnostics, clinical decision support, and patient data systems require the most stringent governance frameworks in any sector.
Key Challenges
- Patient safety requirements
- Medical device regulations (MDR/IVDR)
- Clinical validation requirements
- Data protection and GDPR compliance
- Integration with existing clinical workflows
Our Approach
- Clinical AI risk assessments
- Human oversight mechanism design
- Regulatory pathway mapping
- Documentation for conformity assessment
- Post-market surveillance frameworks
Retail & E-commerce
Personalization engines, dynamic pricing algorithms, and inventory AI affect millions of customers daily. Governance protects brand trust.
Key Challenges
- Pricing algorithm fairness
- Personalization without discrimination
- Customer data usage transparency
- Recommendation system bias
- Supply chain AI governance
Our Approach
- Fairness audits for pricing algorithms
- Transparency documentation
- Customer-facing AI disclosure
- Vendor management frameworks
- Incident response planning
Insurance
AI underwriting, claims automation, and risk assessment models face growing regulatory attention and customer scrutiny.
Key Challenges
- Underwriting algorithm explainability
- Claims decision fairness
- Price optimization ethics
- Protected characteristic handling
- Regulatory reporting requirements
Our Approach
- Algorithmic impact assessments
- Fairness testing frameworks
- Customer communication standards
- Regulatory engagement preparation
- Model governance integration
Don't see your industry?
AI governance principles apply across sectors. Contact us to discuss your specific needs and challenges.