One set of numbers everyone trusts.
We connect the systems and spreadsheets your business runs on, model the data properly and put clear dashboards in front of the people who make decisions.
From scattered sources to clear answers.
Good reporting is mostly plumbing. We build the plumbing properly so dashboards stay right after the people who built them have moved on.
Data audit
An inventory of your sources, owners, definitions and quality issues, with a prioritised plan.
Ingestion pipelines
Scheduled, monitored syncs from apps, databases, APIs and spreadsheets into one place — with retries and alerts.
Warehouse design
A cloud warehouse sized for your volume and budget, with sensible security and cost controls.
Modelling with tests
Business logic written once in version-controlled models, with tests that catch broken data before it reaches a report.
Dashboards & self-service
Focused dashboards for each team, and a governed layer so analysts can explore without breaking things.
Data for AI
Clean, documented tables ready to feed forecasting models or AI assistants.
What you walk away with.
A documented data platform, not a pile of one-off reports.
- Source inventory and data audit
- Automated ingestion pipelines
- Cloud data warehouse
- Tested, documented data models
- Agreed metric definitions
- Team dashboards
- Data-quality alerts
- Handover and training sessions
Where it pays off.
Common data problems we're set up to solve. These are illustrations, not client case studies.
Board and management reporting
Monthly packs that used to take days of copy-paste, generated automatically from source systems.
Single customer view
Orders, support tickets and marketing data joined on one customer record, so teams stop arguing over whose number is right.
Operational dashboards
Near-real-time views of stock, jobs, bookings or deliveries for the people running the day.
Forecasting
Demand, cash-flow or capacity forecasts built on clean history, with clear explanations of their uncertainty.
How a data project runs.
Audit
Interview stakeholders, list sources and agree the questions that matter most and how each metric is defined.
→ metric definitionsFoundation
Set up warehouse, ingestion and the first models, with tests and documentation from the start.
→ first pipelinesDashboards
Ship the first dashboard for one team quickly, gather feedback, then extend to the next.
→ first dashboardHand over
Training, runbooks and monitoring — or an ongoing plan where we keep it running for you.
→ self-sufficient teamTools we reach for.
Open, well-documented tools that avoid lock-in and keep warehouse costs predictable.
Ingest & orchestrate
- Airbyte
- Fivetran
- Dagster
- Airflow
- Python
Store & model
- PostgreSQL
- BigQuery
- Snowflake
- DuckDB
- dbt
Visualise
- Metabase
- Looker Studio
- Power BI
- Evidence
- Streamlit
We mostly use spreadsheets. Is that a problem?
Not at all — it's a very common starting point. We can ingest spreadsheets on a schedule, then gradually replace the most fragile ones with data pulled straight from your systems.
Which BI tool should we use?
The one your team will open. If you already have Power BI or Looker, we build on it; if not, we often suggest an open-source option such as Metabase to keep licence costs low.
How do you make sure the numbers are right?
Business rules live in version-controlled models with automated tests, metric definitions are agreed and documented with the people who own them, and alerts fire when source data looks wrong.
Is our data secure?
Data stays in a warehouse in your own cloud account, with role-based access, encryption and audit logs. We follow UK GDPR principles such as data minimisation and retention limits.
Tired of reconciling spreadsheets?
Tell us where your data lives and which questions you can't answer yet. We'll sketch a pragmatic first step.