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.
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
Before you ask
Questions about data engineering & analytics
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