AWS · Azure · Google Cloud
Data engineering, analytics, BI reporting and AI consulting for mid-sized companies. We build pipelines, lakehouses, dashboards and models on AWS, Azure and Google Cloud — documented and handed over.
Apache KafkaApache Airflow
BQThe stack we build with, daily
DatabricksBQ
Apache AirflowApache Kafka
dbtDatabricksBQApache AirflowApache KafkadbtSnowflakeFivetranTableau
SnowflakeFivetranTableauThe platform
This is what we build. Not every client needs all five stages on day one, but everything we deliver is designed to fit this shape, so the next stage is always an extension rather than a rebuild.
STAGE 01 · SOURCES
Your systems are already producing the data you need — it's just trapped in formats and schedules nobody can work with. We start by connecting to them read-only, without asking you to change how any of them run.
Select a stage above to see what we build there
Cloud platforms
Everything runs under your accounts and your billing, in your source control. We're equally at home on all three major providers — and we'll tell you honestly if the one you're on is the wrong fit for what you're trying to do.
Our most common landing zone for lakehouse builds, especially where document processing or event streaming is involved.
The natural home if you already run Microsoft 365 and Power BI — the licensing and identity integration saves real money.
Where BigQuery's pricing model fits the workload, this is often the fastest and cheapest route to a working warehouse.
Also delivering on Snowflake, Databricks and on-premise infrastructure.
Business intelligence
We're tool-agnostic on purpose. In most cases the tool isn't the problem: the numbers underneath were never agreed on, so people stopped trusting the report. We usually keep the licence you already pay for.
Deep integration with Excel, Teams and Azure identity. Strong when finance teams need to keep working in familiar tools.
Still the strongest tool for visual analysis when your people want to interrogate the data rather than just read a number.
Per-session pricing makes it the sensible choice when you need reporting inside your own product, or for occasional viewers.
The situation
Most companies we meet already have plenty of data. What they don't have is a number everyone agrees on, arriving early enough to act on.
Someone exports three systems into a spreadsheet, reconciles them by hand, and by the time the deck is ready the month is already gone.
Sales says one revenue figure, finance says another, and the meeting becomes an argument about definitions instead of a decision.
A promising model sits in a notebook because nobody owns the pipeline that would feed it or the system it should write back to.
How we work
Every engagement starts small and fixed-scope, so you can judge the quality of the work before committing to more. Each phase has a deliverable you keep.
We audit your sources, interview the people who need the numbers, and write down the decisions the data has to support. No tooling debates yet — just the honest picture.
One narrow slice, end to end: pipeline, model, dashboard or prototype. Running in your cloud, committed to your repository, reviewed with you every Friday.
We widen coverage to the next domain, or we train your team and step back. Documentation and runbooks are written as we go, never bolted on at the end.
Results
Production, quality and sales data lived in separate systems. We built nightly pipelines on Azure and a Power BI operations view the floor managers open daily.
Read the case →No reliable view of which customers were drifting away. We shipped a churn model on BigQuery that scores the base weekly and pushes at-risk accounts into their CRM.
Read the case →Analysts were retyping figures from PDF statements. We built an AWS document extraction pipeline with a human review queue for the edge cases.
Read the case →Engagement models
Three ways to work with us. Most clients begin with the pilot and expand once they've seen a deliverable land.
For teams who know something's wrong but not where to start.
For teams ready to prove one use case end to end.
For companies who need a data function without hiring one yet.
Before you ask
No — it's the normal starting point. Nobody's data is clean before someone does the work of cleaning it. The two-week audit exists precisely to tell you how bad it is, what it will take to fix, and which parts you can safely ignore for now.
Usually the one you're already on. Migrating clouds to solve a data problem is almost always the expensive wrong answer. If you're genuinely starting fresh: Azure if you're a Microsoft shop, GCP if BigQuery's pricing suits your query pattern, AWS if you need breadth or you're embedding analytics into a product.
Whichever your people will actually open. Power BI for Microsoft-first finance teams, Tableau where analysts want to explore, QuickSight when you're on AWS or embedding reports in your own product. We build in all three and have no reseller relationship pushing us either way.
Pilots are fixed-price and quoted after the audit, because quoting before we've seen your systems would either be padded or wrong. The audit itself is a small fixed fee, and if you decide not to continue you keep the findings. We'll give you an honest range on the first call.
No. Everything runs in your cloud accounts, under your billing, in your source control. We use standard open tools rather than anything proprietary to us. If you want to take it in-house or move to another partner, nothing has to be rebuilt.
Almost always reporting first. A model trained on inconsistent data produces confident nonsense, and it's the fastest way to lose the organisation's trust in data work altogether. We'll tell you plainly if we think an AI project is premature — even though it's the bigger engagement for us.
A 30-minute call. You describe the reporting or data problem you're stuck on, we tell you how we'd approach it and what it would realistically take. No deck, no pitch.
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