Six weeks. Clear steps, clear asks, one clear result.

Data orientation

We review your raw system exports and map what exists, what is missing, and what can be trusted.

System extracts, a data contact, and a kickoff session.

Data spine — an inventory of your data and what can be trusted.

Field validation

We observe how work actually happens in the field — structured field walks, ride-alongs and early interviews. This grounds the diagnostic before any model is built.

Site access, selected ride-alongs or field observations, and first interviews.

A field validation brief: what is confirmed, what is unclear, and what is only internal folklore.

Operating-model reconstruction

We rebuild how the operation runs from request to resolution, then reconcile it against roles, systems, handoffs and the validated field context.

Stakeholder interviews and any existing process documentation.

One agreed operating map.

Pattern detection

SIGNAL reconstructs the operation event by event to identify repeat mechanisms, instability, leakage, and the root-cause hypotheses worth testing.

Access for clarification questions and operational sense-checks.

A root-cause register.

Value-at-stake modelling

We size each validated root cause in financial and operational terms, under conservative, base and stretch scenarios.

Financial assumptions, rate cards and cost inputs.

A value model by root cause.

Roadmap & executive decision package

We sequence the root causes by value, feasibility, dependencies and ownership. AI opportunities are ranked only where the evidence supports them.

One executive review session.

An executive decision package — yours to keep and act on.