A selection of engagements across AI governance, ISO 42001 implementation, needs assessment, and SME transformation. Actual engagements are anonymised. Illustrative scenarios show what is achievable in each sector.
A multinational logistics corporation needed a governance framework for its AI activities, including a bilingual customer service chatbot operating across two languages.
The organisation had deployed AI tools including a custom RAG-based GPT and was building a bilingual customer chatbot — without any formal AI governance structure, policy, or risk assessment in place.
A major energy company was using multiple AI tools across departments with no governance structure in place. Leadership needed to understand their current state and a clear path forward.
Five AI systems identified operating without formal risk assessment, policy, or oversight. The organisation had significant exposure as a critical infrastructure provider — and no visibility of its AI governance gaps.
A business advisory practice needed an AI-powered agent to extend its reach — serving the global startup community with business guidance, built on responsible AI principles from the ground up.
Building an AI agent that genuinely helps startups — while ensuring transparency, ethical use, and responsible AI principles are embedded by design rather than added as an afterthought.
A regional bank deploying AI for customer onboarding, fraud detection, and credit scoring needed a formal governance framework before regulatory scrutiny intensified.
Three AI systems in production — none with formal risk assessment, policy, or accountability structure. Regulatory environment tightening. Board seeking assurance that AI use was defensible and compliant.
A private healthcare group using AI for diagnostic support and patient triage needed governance controls appropriate for high-risk AI — where errors have direct patient safety implications.
AI diagnostic systems classified as high-risk under emerging regulation. Patient data privacy obligations. No human oversight mechanisms documented. Clinical staff using AI outputs without formal guidance on when to override.
A growing e-commerce business wanted to adopt AI to improve operations and customer experience but had no structured approach — and no understanding of where AI would genuinely add value.
Leadership had attended AI training that was too technical to apply. Multiple AI tools purchased with limited results. No business mapping, no governance, no clear link between AI spend and business growth.