Data & analytics
The data your reporting and your AI both depend on.
Platforms, pipelines and definitions, modelled for the questions people actually ask. With the lineage, validation and access control that let people trust the numbers and know where they came from.
Why this matters now
Most of what looks like an AI problem is a data problem first. Models are trained, prompted and grounded on whatever the business already holds — and in most businesses that data is duplicated across systems, defined three different ways, and trusted by nobody. The model is rarely what fails. What it was given usually is.
What we build
The foundation
Data platforms & warehousing
The warehouse or lakehouse that reporting and AI both depend on, modelled for the questions people actually ask rather than as a place data goes to be forgotten. Built as code, documented, and cost-controlled from day one.
Where it applies: consolidating sales, finance and operations data from five systems into one place · replacing a fragile reporting database everyone is afraid to touch.
Pipelines
Moving, transforming and validating data reliably between systems. Each pipeline comes with failure handling, retries, monitoring and alerts — which is what makes a pipeline trustworthy rather than merely working today.
Where it applies: nightly loads from the ERP and CRM into the warehouse · near-real-time feeds from an e-commerce platform into stock planning.
Big data & analytics engineering
Processing data at volume, in batch or streaming. The transformation layer that turns raw tables into documented, tested metrics a team can build on.
Where it applies: clickstream or IoT event data at millions of rows a day · a metrics layer that finance and product both trust.
Data contracts
Agreed, enforced schemas at the point where each system produces data. Checked in CI, so a breaking change upstream fails the build rather than silently breaking dashboards and AI tools downstream.
Where it applies: an app team renaming a column and breaking the document-extraction pipeline · several teams feeding one warehouse without a shared schema.
Trust and meaning
Data quality & governance
Lineage, validation, cataloguing and access control, so people can trust the numbers and know where they came from. The same accountability we bring to AI, applied to data.
Where it applies: finding why two reports show different revenue figures · controlling who can see personal or financial data in the warehouse.
Semantic & context layer
One governed definition of your business metrics and entities — revenue, active customer, margin. Dashboards and AI agents both query it, so agents stop calculating the same number three different ways or inventing joins.
Where it applies: staff asking data questions in plain language and getting consistent answers · aligning finance, sales and product on one definition of each KPI.
Business intelligence
Dashboards and reports that put the answer in front of the person who needs it. Each tied to a decision someone makes, and delivered in the tools they already use.
Where it applies: a daily operations dashboard for store or branch managers · board and investor reporting generated automatically each month.
What comes with it
- Lineage on every table — where it came from and what changed it
- Validation and tests on every pipeline, with alerts when something fails
- Documented definitions for every metric that matters
- Cost controls configured from the start, not after the first surprise bill
- Everything as code, in repositories you own
Built secure and evidenced, as standard
Built so every number can be traced.
- Security and compliance are not a separate service we sell. They are how the work is built, on every engagement.
- For data, that means three things in particular. Access is controlled down to the row and column, so personal and financial data is only visible to the people entitled to it — and AI systems querying the warehouse inherit the same limits. Every figure is traceable back to its source, so a disputed number can be explained rather than defended. And personal data is minimised, masked or excluded wherever it is not essential to the question being answered.
- Everything we build in this area is delivered against our published engineering standard, and ships with the evidence to prove it: a lineage map, data quality test results, and signed build provenance.
How the work runs
- 01
Scoping — which sources, which questions, who may see what.
- 02
Design — the data model and access model, written down.
- 03
Build — against the published standard, every merge reviewed.
- 04
Handover — in repositories you own, with documentation and a runbook.
Typical duration: 2 to 12 weeks. A focused dashboard sits at the short end; a full data platform consolidating several systems at the long end.
How we workWhat we will tell you
As often as not, a business does not need a model — it needs its data plumbed properly first. When that is the case, we say so, and we build the foundation before anything is built on top of it.
Tell us what you’re building.
Describe the problem in your own words. We will tell you honestly whether we are the right people for it.