ARCHITECTURE DIAGNOSTICMake the decision visible.
Clarify current state, decision drivers, constraints, architecture risks and a prioritised path forward.
Outputs: decision map, risk register, target principles, transition options and executive readout.
DATA QUALITY · AI READINESSTest fitness before commitment.
Assess whether data is understandable, representative, traceable and fit for migration, analytics or AI use.
Outputs: profiling baseline, source comparison, remediation priorities, quality gates and acceptance evidence.
SEMANTIC · RELATIONALResolve meaning and ownership.
Untangle inconsistent terms, entities, relationships and ownership across systems or organisations.
Outputs: controlled concepts, semantic mappings, logical or relational model, integrity rules and stewardship decisions.
AI ASSURANCEDefine what good means.
Connect dataset quality, model metrics, operational constraints and human oversight to the actual decision.
Outputs: evaluation framework, metric rationale, test design, failure conditions and acceptance criteria.
MISSION-CRITICAL REVIEWExpose cross-layer risk.
Examine capability, requirements, interfaces, dependencies, evidence and delivery alignment.
Outputs: findings, traceability gaps, decision points, treatment options and governance recommendations.
R&D TECHNICAL EVIDENCEReconstruct the engineering progression.
Build a reviewer-ready technical chronology and evidence trail behind complex R&D work.
Boundary: technical evidence work, not tax or legal advice.
A controlled first step
Most advisory work should begin with a fixed-scope assessment. Before accepting an engagement, we agree the decision, evidence available, confidentiality boundary, stakeholders, outputs and acceptance criteria.