Two reports, two answers, one very long meeting.
Most organisations do not have an AI problem. They have an agreement problem: no single definition of revenue, customer or headcount that every system respects.
Dashboards nobody believes
Two teams present different numbers for the same month and the meeting becomes an audit.
Pipelines held together by people
Critical data movement depends on a scheduled task and one person’s memory.
AI pilots that stall
A promising model never leaves the notebook because nothing about it was built to run in production.
Reporting backlog
Analysts spend their week rebuilding extracts instead of answering questions.
Four workstreams, sequenced deliberately.
Data engineering
Ingestion, transformation and DataOps pipelines that are monitored, versioned and documented.
Semantic layer
One governed definition per metric, so every report and model draws from the same source.
AI & ML
Predictive models and GenAI agents scoped to a decision, deployed properly and monitored for drift.
BI & reporting
Power BI reporting built for decisions, with self-service where the data is trustworthy enough for it.
Twelve weeks, stated up front.
Source systems reviewed and a single agreed definition produced for each core metric.
Pipelines, semantic layer and monitoring in place, with the first certified dataset published.
First decision dashboards and one production model live, with the run book handed to your team.
We work in your stack, not our favourite one.
Most SA enterprises are already part-way into Microsoft or a hybrid estate. We build on that rather than proposing a migration you did not ask for.
Questions we get asked first
Bring the two reports that disagree. We’ll start there.
Thirty minutes with a data engineer who will tell you honestly how big the gap is.