AI & Data
Assist operators and analysts—do not substitute opaque models for accountable systems.
Enterprise AI assistants, document intelligence, RAG, analytics, and decision support—applied where they improve clarity without replacing deterministic logic that must remain correct and auditable.
What this includes
Enterprise assistants grounded in approved operational and document corpora.
Document intelligence for retrieval, classification, and structured extraction with human review paths.
RAG architectures that prioritize provenance and permission boundaries.
Analytics and decision-support layers that surface exceptions and context.
Governance for data quality, access, logging, and explainability.
Where AI assists—and where it must not decide
AI is appropriate for search, summarization, triage, and decision support with clear escalation.
Deterministic business rules, safety logic, and compliance-critical calculations remain explicit system logic.
We do not place black-box models on paths where an auditor or operator cannot explain the outcome.
Engagement discipline
Use cases start from an operational bottleneck with an owner—not from a model catalogue.
Evaluation criteria and fallback behaviour are defined before deployment.
Intelligence features inherit the same access control and audit requirements as the systems they sit on.
Key deliverables
- Use-case and risk assessment
- Data and corpus governance design
- Assistant or RAG architecture
- Analytics / decision-support views
- Evaluation and operations controls
Related sectors
Planning a system in this area?
Discuss a system →Discuss a system
We start by understanding how your system works today—and where risk accumulates.