One number the whole room agrees on. Not a dashboard. A record everyone trusts.
Scattered systems consolidated into a single reporting layer that updates itself and stands up to an audit.
The symptoms, as clients describe them.
- Sales reports one number, finance another, and the meeting starts by arguing about whose is right.
- A dashboard breaks the moment someone renames a column upstream.
- Nobody can say what happened last quarter without asking the one person who remembers.
- Collecting the data by hand takes longer than acting on it would.
Concrete deliverables, not a capability list.
ETL pipelines & warehousing
Data pulled from wherever it lives and landed somewhere it can be trusted.
Semantic modelling & dashboards
Metrics defined once, so every report inherits the same definition of “revenue”.
Scraping & collection at scale
Custom collection clusters for data no API will hand over.
Data quality checks
Bad rows get caught before they reach a board deck, not after.
A disciplined process. No fixed opinion about the tool.
- 1
Discover
We sit with the people doing the work and map the process as it actually runs — not as the org chart says it does.
Process map
- 2
Diagnose
Root causes, volumes and the cost of the status quo, so the business case is arithmetic rather than opinion.
Business case
- 3
Build
Right tool for the job, built with error handling, logging and handover documentation from day one.
Working system
- 4
Launch
Piloted against real data and rolled out with your team, with training so adoption isn’t left to chance.
Handover pack
- 5
Operate
Monitoring, fixes and iteration — the system keeps earning after go-live, or we hear about it first.
Support line
Work we have already shipped.
38-node scraping cluster
Custom Puppeteer bots on AWS with PostgreSQL post-processing, designed for very high-volume collection.
OperationsPower BI semantic modelling
Labour-efficiency and participation measures plus a weighted executive scorecard for a multi-site operator.
D2C reportingDaily Looker Studio reporting
Amazon, Shopify, Google Ads and Zoho Books consolidated daily via Zapier and Pabbly for a growing D2C company.
- Power BI
- DAX
PostgreSQL
MySQL
MongoDB
Python/Pandas
- Looker Studio
- Puppeteer
Before you ask us.
Yes — Power BI, Looker Studio, whatever the team already reads. The work is in the pipeline and the model underneath, not in replacing the dashboard.
That's the usual starting point. Diagnose maps what exists and where it disagrees before a single pipeline is built.
You do, with documentation and handover as standard — or we do, on a Managed Partner retainer, whichever the team prefers.
Yes — the collection and processing architecture is built for volume from the first version, not retrofitted once it breaks.