Data & Analytics
Build the data layer everything else depends on.
Data engineering, warehousing, reporting, analytics and forecasting, from the source systems through to the numbers your board actually trusts.
What this is
Engineering first, dashboards second.
Most reporting problems are not reporting problems. They are data problems wearing a dashboard.
When two systems hold the same record and nobody agreed which is authoritative, no BI tool will fix it. We work from the source systems up: pipelines, definitions, quality and a modelled layer, then the reporting on top of it.
The same layer is what applied AI runs on. Retrieval, agents and models are only as reliable as the data beneath them, which is why this work often has to happen before an AI initiative can reach production.
What you get
From source system to decision
Delivered in stages, so reporting improves while the rest is still being built.
A map of where every number comes from
Which system owns each record, where it is duplicated, and which of the two conflicting reports is actually right.
Pipelines into a single store
Scheduled, monitored ingestion from your operational systems, so nobody is exporting to CSV to build a report.
A modelled warehouse
One definition per metric, agreed with the people who use it, so revenue means the same thing in every report.
Reporting people trust
Dashboards and automated recurring reports, including board packs, funder returns and regulatory submissions, that build themselves.
Forecasting and analysis
Demand, capacity, cost to serve, churn risk. Statistical or machine-learning models where a chart is not enough.
Monitoring and a named owner
Alerts when a pipeline fails or a number moves unexpectedly, plus documentation and one person inside your business who owns it.
How it runs
We start from the decision, not the data
Building a warehouse before agreeing what it answers is how these projects end up unused.
Agree the questions
We start from the decisions you need to make, not from the data you happen to have. That determines what is worth building.
Trace the numbers
We follow each metric back to the system that produces it and find where definitions diverge.
Build the pipelines and model
Ingestion, transformation and a modelled layer with one agreed definition per metric, tested against known-good figures.
Ship the reporting
Dashboards, scheduled reports and alerts, built with the people who will actually read them.
Hand it over
Documentation, training, monitoring and a named owner. You can change a report without calling us.
Questions
Before starting a data project
Why do our reports disagree?
Almost always because two systems hold the same record and no one agreed which is authoritative, or because the same word means different things in two teams. Both are fixable, and finding out which it is takes days rather than months.
Do we need a data warehouse?
Not always. If you have a handful of systems and modest volumes, connecting them directly may be enough. We would rather not sell you infrastructure you do not need.
Can you work with the BI tool we already have?
Yes. Power BI, Tableau, Looker, Metabase or something built in. The layer underneath matters far more than the tool on top, and that is where the work usually is.
Do you do forecasting and machine learning?
Yes, where the question genuinely needs it, such as demand, capacity, cost to serve or risk scoring. Most reporting problems are not model problems, so we will tell you when a simpler method answers it.
How does this relate to AI?
Directly. Retrieval, agents and applied AI are only as good as the data underneath them. This is often the work that has to happen before an AI initiative can reach production.
Who owns it when you leave?
You do. The pipelines, the models, the definitions and the documentation are yours, with one named owner inside your business before we finish.