Finance
Data & AI Strategy
Challenge: Leadership priorities are clear, but delivery sequencing and ownership are fragmented.
Integration path: Define a staged roadmap linking business outcomes, architecture milestones, and team responsibilities.
Expected outcome: A prioritised programme plan with measurable KPIs and clear decision rights.
Retail
AI Product Engineering
Challenge: Promising prototypes fail to become reliable products under production constraints.
Integration path: Engineer retrieval, orchestration, and evaluation layers directly into product and platform workflows.
Expected outcome: Production-grade AI services with observable quality, latency, and reliability behaviour.
Public Sector
AI Enablement & Integration Engineering
Challenge: AI outputs sit outside core systems and are not adopted by frontline teams.
Integration path: Integrate models and copilots into operational tools, APIs, and approval flows with rollback paths.
Expected outcome: AI-enabled workflows that are used in day-to-day decision processes, not side channels.
Education
Data Platform Modernisation
Challenge: Data is fragmented across systems, with weak contracts and inconsistent reporting.
Integration path: Establish modern platform patterns with robust contracts, lineage, and quality controls.
Expected outcome: Reliable analytics and model inputs across product, operations, and executive reporting.
Finance
MLOps & Operations
Challenge: Model and pipeline changes are difficult to release safely in regulated or high-impact contexts.
Integration path: Implement CI/CD, drift monitoring, incident pathways, and controlled rollout practices.
Expected outcome: Stable lifecycle management for models and pipelines with lower operational risk.
Public Sector
Governance & Risk Controls
Challenge: Governance requirements are documented but not embedded in engineering delivery.
Integration path: Embed risk-tiered controls into architecture, release gates, and runtime monitoring.
Expected outcome: Audit-ready delivery with practical controls aligned to business and regulatory expectations.