Data engineering so everyone argues about decisions, not numbers.
When sales, finance and operations each bring a different number to the same meeting, the problem isn't the people. It's the plumbing. We build pipelines and dashboards that give everyone one version of the truth, and the clean data AI projects need.
The problem you're living with.
You have plenty of data. What you don't have is data you trust, in one place, when you need it.
- Monthly reporting means someone spending three days in spreadsheets.
- Different teams report different numbers for the same metric.
- Dashboards break silently, and people stop trusting them.
- Your AI project stalled because the data it needs is scattered and messy.
Slow, disputed numbers lead to slow, cautious decisions. And every AI or automation project built on bad data inherits its problems.
What we build.
Pipelines, warehouses and dashboards that give everyone the same numbers.
Data pipelines (ETL/ELT)
Reliable, scheduled pipelines that pull from your apps, databases and SaaS tools, with checks that fail loudly.
Data warehouses
A central warehouse such as BigQuery, Snowflake, Redshift or Postgres, modelled so questions are easy to answer.
Metric definitions
One agreed definition of revenue, churn and active users, documented and used everywhere.
BI dashboards
Dashboards in Metabase, Power BI, Looker or a custom app, built around decisions, not vanity charts.
Data quality monitoring
Freshness, volume and validity checks with alerts, so a broken feed is noticed in minutes, not at month end.
AI-ready data
Clean, well-described datasets and document stores that RAG and AI agents can use safely.
How it works, step by step.
No big bang. Small, measured steps with a working demo every week.
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List the questions
We start with the decisions you want to make and the questions behind them, not with the tools.
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Map the sources
Where each number really comes from, who owns it, and how reliable it is.
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Build the pipelines
Incremental, tested pipelines into a warehouse, with a documented data model.
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Agree the metrics
One definition per metric, signed off by the teams who use it.
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Ship dashboards and alerts
Dashboards for the decisions that matter, plus monitoring that tells you when data is late or wrong.
What changes for your team.
What we aim for, measured against how things run today. We don't promise numbers before we've seen your baseline.
Reporting in minutes, not days
Month-end numbers are ready when the month ends.
One version of the truth
Meetings about what to do, not whose spreadsheet is right.
Trustworthy dashboards
Data quality checks mean broken feeds are caught fast.
A base for AI
Clean, governed data makes every future automation project cheaper.
What we'd tell a friend about data engineering & analytics
Before you buy another BI tool, write down the five decisions you'd make differently with better data. If you can't name them, a new tool won't help. If you can, you've just written your first data project brief.
What we won't do
- Build a warehouse full of tables nobody asked for.
- Ship dashboards without data quality checks.
- Copy personal data around without a clear reason and a retention plan.
Data Engineering: your questions.
Do we need a data warehouse?
If you're answering questions across more than two or three systems, usually yes. For smaller setups, a well-modelled Postgres database can be enough. We'll recommend the simplest option that works.
Which BI tool should we use?
The one your team will actually open. Power BI fits Microsoft-heavy companies, Looker and Metabase suit others, and sometimes a small custom dashboard beats all of them.
How do you keep data quality high?
Tests on every pipeline run, like freshness, row counts, uniqueness and valid values, with alerts to the owner. Problems get fixed at the source, not patched in a dashboard.
Can you help us get data ready for AI?
Yes. That usually means consolidating sources, cleaning and documenting them, handling personal data properly, and making documents searchable for retrieval.
What about GDPR and privacy?
We design for data minimisation, access control, retention rules and the ability to find and delete a person's data when they ask.
Want this for your team? Let's scope it together.
Tell us how the work runs today. We'll come back within one business day with an honest first take and a suggested first step.
- Weekly demos
- No lock-in
- You own the code
- 1 business day