ETL & Data Pipelines

ETL and data pipelines that give you one set of numbers

When sales, finance and ops each quote a different number for the same metric, meetings turn into arguments. We build pipelines that pull data out of every app, clean it and land it in one database you own, so reports agree and your AI projects finally have data they can use.

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Sound familiar?

Problems we fix

If your numbers don’t match from one tool to the next, these will sound familiar.

The numbers don’t match

Sales, finance and ops each bring a different figure for the same metric, and the meeting becomes about whose is right.

Manual exports

Every week someone downloads a pile of CSVs and rebuilds the same report by hand.

Reports take days

By the time the report is ready, the numbers in it are already out of date.

AI needs clean data

The AI project stalls because the data it needs is scattered across six tools and a shared drive.

What we build

What you get

ETL / ELT pipelines

Data from APIs, databases, files and even email attachments, landed in PostgreSQL on a schedule.

Reporting models

One agreed definition of each metric, so every dashboard shows the same number.

Data quality checks

Automatic checks that flag missing or odd data before it reaches a report.

Event-driven syncs

Near real-time updates for the places where minutes matter.

Dashboards

Reports in Looker Studio, Metabase, Power BI or Retool, whichever your team already opens every day.

AI enrichment

Records tagged, classified and summarized by AI as they arrive.

Real project · Cloud services company

WALT Labs: a reporting app for every department

WALT Labs, a cloud services company with 51 to 200 people, wanted each department to have its own internal app and reporting tool, built on its existing Google Cloud data.

Each department now uses its own reporting app as its daily source of information, and the client reports the work was delivered on time.

RetoolBigQueryGoogle Cloud

Read the verified review on Clutch →

What we built

Internal apps and reporting tools for each department, built in Retool
Reporting connected to BigQuery on Google Cloud
One consistent source of information each team can open every day

How we build pipelines

One trusted set of numbers, built step by step

The goal isn’t more dashboards. It’s one set of numbers that everyone agrees on, updated on schedule and checked before anyone sees it.

1
Agree the definitions
We sit down with the people who own each metric and agree exactly what “revenue”, “active customer” or “churn” means, in writing.
2
Map every source
We list where each piece of data really comes from: which app, which API, which spreadsheet, and how often it changes.
3
Build the pipelines
Scheduled, logged jobs that pull data in, clean it and land it in a database you own. Each one can be rerun safely.
4
Add quality checks
Automatic checks for missing, duplicated or odd-looking data, with an alert before a bad number reaches a report.
5
Ship the first reports
We rebuild the reports your team argues about most, on the new data, and check them against the old ones line by line.
6
Hand over or look after it
Documentation of every pipeline and metric, then a handover to your team or ongoing monitoring by us.

Cost & timeline

What affects the price

Pipelines are scoped after a free call, usually starting with a fixed price for the first set of sources and reports.

How many sources: Two well-documented APIs are a different job from ten apps, a legacy database and a folder of CSV files.
How fresh the data must be: Daily updates are simple. Hourly or near real-time updates need more engineering and monitoring.
How clean the source data is: Duplicates, inconsistent IDs and free-text fields all need rules before the numbers can be trusted.
Where reports live: Feeding a BI tool you already have is quicker than building new reporting apps from scratch.
Ongoing care: A handover with documentation, or ongoing monitoring as your sources and questions change.

Industries

Data pipelines by industry

All industries →

Logistics & Supply Chain E-commerce & Retail Marketing & Agencies SaaS & Startups Professional Services

Proof

Related case studies

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n8n · Gemini · Supabase

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AI document extraction from Drive files with n8n

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Zapier · Gemini · PostgreSQL

AI automation · Finance

AI invoice and expense automation with Zapier and Gemini

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ETL & Data Pipelines FAQs

Which data sources can you connect?

Most SaaS APIs, databases, spreadsheets, CSV or SFTP drops and email attachments.

Batch or real time?

Batch for most reporting; event-driven pipelines where minutes matter.

Where is the data stored?

In a database you own, usually PostgreSQL or Supabase in your own cloud account.

Who maintains the pipelines?

We can hand over with documentation and training, or look after them on an ongoing basis.

Do we need a data warehouse?

Not always. For many companies a well-structured PostgreSQL database is plenty. We recommend BigQuery or another warehouse only when your data volume or tools call for it.

Which BI and reporting tools do you work with?

Whichever your team already uses, from Looker Studio and Metabase to Power BI, or reporting apps we build in Retool.

What happens when a source system changes its API?

The pipeline alerts us instead of failing silently, and we update it. That’s part of ongoing support if you choose it.

Can you start with just one report?

Yes, and we usually recommend it. One trusted report built properly is the best way to prove the approach before connecting everything.

Often paired with

Related services

All services →

Let’s get your data into one place you can check.

List the apps your data lives in. On a free 30-minute call we’ll map the pipeline, then follow up with a fixed-scope estimate.

Official Retool Agency Partner 5.0 on Clutch, 8 verified reviewsBuilding software since 2015Reply within one business day

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