Data pipelines & enrichment
Pulling data from scattered sources on a schedule, cleaning and de-duplicating it, enriching it against external records, and landing it somewhere usable.
Scope a buildWhat this is
Reports and decisions are only as good as the data under them. We build the pipeline that gathers data from your sources — apps, exports, third-party APIs — normalises it, removes the duplicates, fills the gaps from external records where allowed, and writes a clean, current dataset your team and your dashboards can rely on. It runs on a schedule, logs what it did, and flags the rows it couldn’t resolve.
What you get
- Scheduled extraction from your sources with retry and logging
- Cleaning, de-duplication and normalisation to one shape
- Enrichment against external records where terms allow it
- A clean dataset in your warehouse, database or sheet
- A record of what changed and which rows need a human
How we start
We take one messy source and one clean output you want, build that path end to end, and add sources once the shape is proven.
Who owns the code?
You do, completely. The extraction jobs, the cleaning logic and the schedule are delivered as code you can run anywhere, plus documentation of what each step does. There's no proprietary pipeline engine you're locked into — swap the destination or the schedule whenever you need to.
How is our data handled?
The pipeline writes to a warehouse, database or sheet you already own — we don't build a copy of your data on infrastructure we control. Enrichment against external records only happens where the source's terms allow it, and secrets live in a vault, never in code.
How long does it take?
One source into one clean output typically takes one to two weeks, including de-duplication and a first pass at normalisation. We prove that path end to end before adding more sources, so the scope and the schedule stay predictable as it grows.
What happens to rows it can't resolve?
They're flagged, not silently dropped or guessed at. Every run logs what changed and which rows need a human — a missing enrichment match, a duplicate the rules couldn't resolve confidently — so your team reviews the exceptions instead of trusting a black box.
Have something like this?
Tell us the workflowWondering who owns the code or where your data goes? See how we work.