Good decisions need data people can trust, and most organisations do not have it — information is scattered across spreadsheets, CRMs and legacy systems, defined differently in each, and reconciled by hand. We fix the foundation first: consolidating, cleaning and modelling your data into a single source of truth with clear definitions everyone agrees on.
On that foundation we build analytics around the questions your team actually asks, not a generic dashboard pack. A metric is only useful if it changes a decision, so we design backward from the decisions and make the numbers legible, current and reliable. Where prediction genuinely shortens the path from data to action — forecasting, churn, segmentation — we add models; where a clear chart suffices, we resist the urge to over-engineer.
We also put the unglamorous foundations in place: data quality checks, governance, and documentation so the system stays trustworthy as it grows and as people come and go.
Inventory sources, quality and the decisions data must serve.
Consolidate into a single, well-defined source of truth.
Build tested, reliable ingestion and transformation.
Dashboards and metrics designed around real decisions.
Add ML only where it measurably shortens the decision.
That is the normal starting point. We begin with an audit and consolidation into a single modelled source of truth — most of the value comes from this before any advanced analytics.
Often not at first. A reliable foundation and clear dashboards solve most needs. We add ML only where prediction measurably shortens a decision — and we are honest when it does not.
We fit the stack to you — warehouses like BigQuery, Snowflake or Postgres, and BI tools you can maintain. We avoid lock-in and over-engineering.
AI, transformation and the odd product drop. No spam.