AI agents and copilots
Assistants that reason over approved knowledge and execute multi-step tasks across systems, with a person accountable at the decision points.
AI & automation
Practical intelligence, embedded where work happens.
We design AI around trusted information, clear permissions, human oversight and measurable usefulness — then take it into production and keep it observable.
Retrieve → Reason → Review
GovernedSolution areas
Where AI creates practical value.
Each of these is shaped around a specific workflow, its data and the cost of getting an answer wrong — not around a model vendor.
Document intelligence
Extraction, classification and validation that replace manual intake in claims, onboarding and clinical or legal paperwork.
Predictive intelligence
Classification, forecasting and anomaly detection built on real operational data, with drift monitoring and scheduled retraining.
Knowledge retrieval and RAG
Grounded answers across policies, research and operational guidance — with permissions respected and sources cited.
Workflow automation
Approval routing, exception handling and data validation, combining rules and models where each performs best.
AI-enabled product features
Search, discovery, personalisation and summarisation designed into your product rather than bolted onto it.
Trust framework
AI people can understand, govern and trust.
Every solution is shaped around privacy, security, human oversight and measurable accuracy.
Evaluation
Accuracy, relevance, safety and edge cases measured before and after release.
Observability
Quality, latency, cost and drift visible in production, not inferred.
Governance
Ownership, access, review and audit defined before launch.
Human control
Accountable decisions stay with people, with clear escalation paths.
Delivery stages
Prototype quickly. Productionise carefully.
A prototype proves desirability. Production requires quality, safety, latency, cost and adoption to hold up together.
01
Frame the use case
Value, users and what unacceptable failure looks like.
02
Prove with real data
Representative questions and genuine operational content.
03
Harden
Quality, safety, latency and cost brought within bounds.
04
Operate
Governance, monitoring and adoption after go-live.