Data & Cloud
From data swamp to decision supply chain
Pipelines and platforms that make your data arrive clean, on time, and decision-ready.
Reports disagree with each other, analysts spend days exporting and cleaning, and 'the data team' is a queue. Analytics fails upstream — in pipelines, models, and definitions — long before the dashboard renders.
- Monday numbers ready Sunday night, untouched by hand
- One version of truth across every report
- Analysts doing analysis instead of plumbing
Capabilities
What the work actually involves
Data platform architecture
Warehouse/lakehouse design with governance and cost discipline from day one.
Pipeline engineering
Reliable ELT from your operational systems — monitored, tested, and late-data-tolerant.
Semantic layer & metrics
One definition of 'revenue' and 'active customer' that every tool inherits.
Dashboards that get opened
Management, ops, and floor-level views designed around decisions, not chart catalogs.
AI-ready foundations
The clean, governed data layer your future ML and LLM systems will stand on.
Data quality and contracts
Expectations declared and tested at ingestion — schema, ranges, referential integrity, freshness — so a broken upstream feed fails loudly instead of silently poisoning a dashboard.
Governance, lineage and cataloguing
Where a number came from, who may see it, and what breaks if a column changes — the questions every audit and every migration asks.
Deliverables
What you are handed.
Yours to keep, and written so another team could pick them up.
Governed pipelines with lineage
Every number traceable to its source, which is what makes a dashboard arguable-with rather than ignored.
A semantic layer
One definition of revenue, one of active customer. Most reporting disputes are definition disputes.
Data quality tests in the pipeline
Bad data fails the run rather than reaching a board pack.
Reporting your team can extend
Documented models, so a new question does not require us.
We do not publish prices — scope drives them. Everything else, here.
- Starts with
- A source audit and a definitions workshop
- Typical duration
- 8–16 weeks to first governed domain
- Who you get
- A data architect, two data engineers, an analyst
- Commercial model
- Fixed-scope first domain, then phased
How to decide
What the answer depends on.
Two sets of conditions. Read both against your own situation — most organisations recognise themselves in one column within a sentence or two.
This is the right call when
- Two departments produce different numbers for the same thing.
- Month-end reporting is assembled by people, from spreadsheets.
- You want AI later and have been told your data is not ready.
A different approach fits better when
- One system, one source, and a report would do — you do not need a lakehouse.
- Nobody will own the definitions. The semantic layer needs a decision-maker.
- You want a dashboard by Friday.
Proof
Where we have done this
A custom ERP that ended month-end chaos for a steel manufacturer
Replaced five disconnected systems and a wall of spreadsheets with a single custom ERP — order-to-dispatch in one flow, month-end closing down from 9 days to 2.
9→2 daysMonth-end closing time
A live control tower for a national logistics fleet
Real-time visibility over 3,000+ vehicles with exception-first alerts — detention hours cut 41%, and customers now track shipments without calling.
41%Reduction in detention hours
Comparing banks on the data they already publish
Publicly available bank data gathered and compared, so a bank can read a competitor's position quickly. Built to be sold to banks as a product.
Before you ask
Questions about data engineering & analytics
Sectors
Where this comes up most.
The regulatory context and the systems already in the building change the build. Each sector page says how.
Next step
Bring us the problem. We will bring the architecture.
A discovery call takes forty-five minutes. You leave with our read on the problem, the shape of the system we would propose, and a straight answer on whether we are the right team for it.
- No sales deck
- An engineer on the call, not an account manager
- NDA before you share anything