Glossary
Plain-English data & AI definitions
The vocabulary — lakehouse, RAG, MLOps, semantic layer — defined without the jargon, so everyone in the room is arguing about the same thing.
100% delivery success — delivered worldwide across seven regions
What you get
Why we wrote this
Half of the confusion in data projects is two people using one word to mean different things. This is our attempt to fix that.
- Plain-English definitions
- Written for mixed audiences
- Kept up to date
Delivery model
How the work actually runs
One delivery model, whatever the engagement. You always know what is shipping this week and what it is worth.
Discovery
We map your sources, decisions and blockers, understand the business goal, and agree what success looks like before anything is built.
Solution design
We design the architecture, data models and use cases — right-sized to your team and your cloud — and cost the work before you commit.
Development
Weekly sprints against a visible backlog: pipelines, models, dashboards and applications, all in version control and yours from day one.
Testing & validation
Every pipeline and model is tested — with frameworks like PyTest and Great Expectations — so the numbers are trustworthy before they ship.
Deployment & training
We deploy into your cloud and train the people who will run it, so the capability lands with your team, not just our code.
Support & optimisation
Observability, alerting and continuous improvement keep the system fast, accurate and cost-efficient long after go-live.
Proof
Related work
Marketing and CRM data unified across 20+ EMEA countries
A digital-transformation programme that consolidated marketing and CRM data across the region on Azure and Power BI, with a custom lead-management automation built on Power & Logic Apps.
Workforce analytics for Nestlé’s supply & demand planning
HR data marts on SAP feeding automated daily ingestions through AWS Glue and Databricks, on a medallion architecture, so planners could see available resource against production targets.
One source of truth from 50+ scattered sources
Data strategy and cloud architecture on Google Cloud, ingesting 50+ sources through Stitch, modelled in dbt and surfaced to the CEO and Head of Product in Looker Studio.
FAQ
Questions buyers actually ask
Straight answers. If yours is not here, ask us on a call — we will tell you if we are the wrong fit.
Ask a questionTwo weeks to a costed roadmap, and a first use case in production in about six weeks. We work against a public backlog, so you always know what is shipping this week and what it is worth.
You do — code, models, dashboards and infrastructure sit in your accounts and repositories from day one, and your team pairs with ours through the build so the handover is a formality.
That is the first conversation. If the numbers do not support it, we will say so before you spend on it — straight advice is the advice you are actually paying for.
Next step
Ready to find out what your data is worth?
Thirty minutes with a delivery lead, not a salesperson. You leave with a point of view on your highest-value use case and what it takes to ship it.