Machine learning & data science
Predictive modelling, anomaly detection, time series, prioritisation and analytical systems designed around operational decisions.
Technical expertise
Depth across the AI system—not only the model.
Enterprise AI succeeds when modelling, data, retrieval, software architecture, evaluation and governance work as one system.
Generative & agentic AI
LLM applications, retrieval-augmented generation and bounded agents that use approved tools, sources and stopping conditions.
Knowledge graphs & GraphRAG
Connected models of entities, events and evidence that improve context, relationship-aware retrieval and traceability.
Explainable & causal AI
Methods that help people understand model behaviour, question recommendations and reason about interventions rather than correlations alone.
Enterprise AI architecture
End-to-end design across data flows, model services, retrieval, identity, permissions, observability and integration with existing workflows.
AI strategy & delivery
Roadmaps, technical due diligence, use-case selection, proof-of-concept design, delivery governance and the path from pilot to production.
Knowledge graphs
Connected intelligence—from meaning to million-entity operations.
Two focused guides cover how graphs support AI agents, causal reasoning and context engineering—and how to architect, operate and analyse them at scale.
A complete system
Reliable AI is an organisational capability.
Models matter. So do data ownership, access control, evaluation datasets, feedback loops, incident handling and the people accountable for decisions.
Explore further
Technical ideas, made concrete.
Read practical perspectives on context graphs and agentic GraphRAG, or see how the expertise appears in delivery work.
Have a difficult AI architecture decision?
Bring the business question, the constraints and the evidence. We can work from there.