Customer & cohort analysis
Who stays, who leaves, and what was different about them months before they went.
Data Analytics
The analysis that ends the argument.
Once the warehouse is trustworthy, somebody still has to ask it the hard questions. This is that work — cohorts, forecasts, pricing and experiments, written up so a decision can be defended in a meeting.
Capabilities
What exactly we do
Six kinds of question, each answered so the answer can be argued with rather than just believed.
Forecasting & demand planning
Demand, cash and capacity projected with the assumptions written down beside the number.
Experimentation & A/B
Tests designed so the result means something, and stopped when the evidence says to stop.
Pricing & margin analysis
Which products, customers and regions actually make money once cost to serve is included.
Analytics engineering
Metric definitions agreed and written in code, so revenue means one thing across the business.
Ad-hoc investigation
The question the board asked on Friday, answered properly by Tuesday with the working shown.
How we deliver
What you get, every time
Analysis is only worth paying for if someone acts on it, so we write for the decision rather than for the dashboard.
Talk to an engineer- A written answer, not a dashboard you have to interpret
- Assumptions stated, so the number can be challenged
- The query and the data kept, so it can be re-run next quarter
- An honest confidence level — including ‘we cannot tell from this data’
- Metric definitions agreed with finance before anything is published
- A walkthrough with the people who have to act on it
Next door
Not quite what you came for?
Analytics sits between two neighbours. If the numbers do not agree yet, start on the left. If you need people to see them every morning, start on the right.
Data Engineering
The pipelines, warehouse and tests that make the numbers trustworthy in the first place. Start here if your reports disagree with each other.
Build the foundation →
Business Intelligence
Dashboards, a metrics layer and self-serve reporting for the people who need the numbers daily, not on request.
See the reporting layer →
Our process
How we work
From a question somebody keeps asking to an answer that settles it.
Proposal sign-up
Scope, timeline and a fixed number in writing — agreed and signed before any build starts, so nothing moves later without a conversation.
Strategy & planning
We turn a vague worry into a question the data can actually answer.
Design & development
The data pulled, joined and sanity-checked against something you already trust.
Testing & deployment
The work itself, with intermediate findings shared rather than saved up.
Support & maintenance
A recommendation, its confidence level, and what would change our mind.
Before you ask
Questions we get asked
Why do we need a warehouse before AI?
Because an AI agent is only as good as what it can look up. Without one clean, governed copy of your numbers, an agent confidently repeats whichever spreadsheet it happened to read.
Ready to talk about Data Analytics?
Thirty minutes with an engineer, not a salesperson. You will get a straight answer on scope, timeline and cost.