Data pipelines that turn messy exports into numbers you trust.
Clean data, on time, in one place.
Most companies don't have a data problem. They have eleven data problems: a CRM export, a billing system, three spreadsheets, a WhatsApp group with photos of receipts. We build the pipelines that pull all of it together, clean it (duplicates, formats, missing fields, inconsistent names) and land it in one warehouse the whole business reads from.
- Nightlypipelines, no reminders
- 1warehouse the whole business reads
- Traceableevery number back to its source
Built around how the work really flows.
The pipelines run on a schedule, alert someone when a source breaks, and keep a history so a number can always be traced back to where it came from. Once the data is clean, the interesting work starts: dashboards that match reality, forecasts, and AI assistants that can finally answer questions about your business accurately.
Data cleaning, ETL and reporting pipelines. We pull from the systems you already run, de-duplicate and normalise the mess, and land it in a warehouse your dashboards and AI can rely on, on a schedule that never needs a reminder.
What we build into it
- ETL / ELT pipelines from CRMs, ERPs, APIs, files and databases
- De-duplication, normalisation and validation rules
- Warehouse and reporting layer (PostgreSQL, BigQuery, ClickHouse)
- Scheduling, monitoring and alerts when a source breaks
Who this is for
Typical stack
Four ones. Zero theatre.
One hour to reply, one day to blueprint, one week to prototype, one month to ship. Every data cleaning & etl engagement starts in this shape.
- IWithin the hour
A real person reads your note.
The engineers who will build it are the ones who reply. No forms routing to forms.
- IIWithin the day
We sketch the system out loud.
A short conversation and a one-page blueprint of what we think you actually need.
- IIIWithin the week
Something you can touch.
A working prototype, not a Figma file. Your team presses the buttons and we adjust.
- IVWithin the month
Live. Quietly.
First version in production, your team has the keys, and we are still on Slack.
Before you get in touch.
Our data is spread across spreadsheets and old systems. Can you still work with it?
That is the usual starting point. We connect to whatever you have, from Excel files and Google Sheets to legacy databases and SaaS APIs, and build the cleaning rules around your real data rather than an idealised schema.
Is this a one-off cleanup or an ongoing pipeline?
Either. Some teams need a one-time migration and cleanup before a new system goes live. Most need a pipeline that runs every night so reports stay correct without anyone re-keying data. We scope which one you actually need.
Which tools do you use for ETL?
Mostly Python with dbt for transformations and Airflow or Dagster for orchestration, landing in PostgreSQL, BigQuery or ClickHouse depending on volume. We prefer boring, well-documented tools your own team can maintain.
Tell us the messy version first.
You don't need a perfect brief. Tell us the problem in your own words and we'll figure out the shape of the project together.