Data pipelines and dashboards your whole team can trust
One set of numbers, refreshed on its own, that every team agrees on.
Use case · A retail chain with stores and an online shop
The Monday report nobody trusted
A composite of the projects we take on: the problem, what we built, and what changed. Pick an act to jump the film to it.
Who it is for
Is this you?
- Teams that spend Monday building last week's report
- Companies with data spread across many tools
- Founders who need clean metrics for investors or the board
What you get
- Pipelines from your apps, tools and databases
- Cloud warehouse set up and modelled
- Shared metric definitions with tests
- Dashboards for each team
- Alerts when a source breaks or data looks wrong
- Clean data ready for AI and forecasting
Built with
- Python
- SQL
- dbt
- Airflow
- Airbyte
- BigQuery
- Snowflake
- Metabase
What we cover
Data & analytics, in detail
Pick one part or the whole set. We scope only what your problem needs.
Pipelines
- Batch and real-time ingestion
- ETL and ELT
- Airflow and Dagster scheduling
- Kafka streams
Warehouse
- BigQuery, Snowflake and Redshift
- PostgreSQL for smaller data
- Modelling with dbt
- Data tests and documentation
Analytics
- Dashboards in Metabase, Looker Studio or Power BI
- Product and marketing analytics
- Self-serve exploration for teams
- Board and investor reporting
Data for AI
- Document pipelines for AI search
- Feature tables for models
- Forecasting and demand planning
- Data quality checks before training
How it runs
From first call to data & analytics in production
You see working software early and can change direction while it is still cheap.
- 01
List the questions
We start from the decisions your teams make and the numbers they argue about. - 02
Connect the sources
We connect each tool and database and land the raw data in one warehouse. - 03
Model and test
We define each metric once, in code, with tests that catch bad or missing data. - 04
Dashboards and handover
We build the dashboards, train your team and document how each number is made.
Questions
Data & analytics: what people ask
Which warehouse should we use?
BigQuery or Snowflake suits most growing teams, and PostgreSQL is enough for smaller data. We pick by data size, team skills and cost.
Can you work with the tools we already have?
Yes. We connect to your existing databases, SaaS tools and BI software, and only replace what is causing problems.
Is our data safe?
The warehouse runs in your cloud account, access is set per role, and personal data can be masked before it reaches dashboards.
Planning data & analytics work?
Tell us where things get stuck today. We will reply with a plan, not a pitch.