labs@atheron:~/services/data$ pipeline run --check --load

Data and analytics

One set of numbers everyone trusts.

Your sales, your books and your operations each keep their own figures. We bring them into one place, check them on the way in, and give you dashboards and reports that agree with each other.

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// what we build

From scattered data to clear answers.

01

Data warehouses

One place for the data from your apps, store, CRM and books, modelled so questions are easy to ask.

02

Pipelines

Data moved on a schedule or as it changes, retried when a source is down, and logged so you can see each run.

03

Dashboards

The figures your team checks every day, on one screen, with the one that matters most first.

04

Reports

Monthly and board reports built once and filled in automatically, in the format you already use.

05

Data quality

Checks for missing, duplicated and impossible values. Bad rows are held back and shown, not hidden in a total.

06

Definitions

One written meaning for each figure, such as an active customer or monthly revenue, so every report counts it the same way.

// how we build it

Checked on the way in.

We agree what each figure means with the people who use it before we build a single chart.
Every pipeline run is logged. When a number looks wrong, you can trace it back to the row it came from.
Personal data is kept out of the warehouse unless a report needs it, and access is set by role.
We build on the tools you already pay for where they fit, rather than adding new ones.

// stack

What we usually build with.

warehouse: PostgreSQL, or the warehouse you already use
pipelines: TypeScript or Python jobs with a queue for retries
sources: Your apps and databases, Stripe, Shopify, QuickBooks Online, your CRM or ERP, through their own APIs
dashboards: Next.js dashboards, or the reporting tool your team already uses
checks: Data quality tests on every run, with alerts

// a typical first project

A dashboard on your own data.

Two or three of your systems joined into one dashboard, with sign-in and quality checks on the way in. Open it in the estimator to see what it would cost.

Estimate this

// questions

Questions about data projects.

Do we need a data warehouse?

Not always. If your questions come from one system, a report on that system may do. A warehouse earns its place when the answer needs several systems together.

Can you use the reporting tool we already have?

Yes. If your team already works in a reporting tool, we feed it clean data rather than asking them to learn another.

Who owns the data?

You do. The warehouse runs in your account or on hosting we run for you, and it is handed over with documentation either way.

What if two systems disagree?

We find out why before we build. Then the warehouse follows the rule you choose, and the quality checks flag the records that break it.

// industries

How it fits your industry.

See what this looks like in your industry: the questions buyers ask, and example projects with a price range.

// related

Often built together.

~/automate

Find your market.

See this work from your city: what changes there, the questions buyers ask, and prices in your currency.

Every market

// start

Not sure which you need?

Tell us the problem, not the solution. We will tell you what we would build, and what we would not.

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