One version of the numbers, ready for AI to build on.
If your weekly report takes a day of spreadsheet work and still starts arguments, the problem is not the charts. We build the pipelines, data model and dashboards that make your numbers dependable, using AI-assisted engineering to move quickly, and lay the clean foundations that AI projects need to work.
Analytics foundations, not just another dashboard tool
Buying a BI licence is the easy part. The value sits in the plumbing underneath and in definitions everyone agrees on.
Data pipelines
Automated extraction from your CRM, finance, e-commerce, product and marketing systems into one place, with alerts when a source breaks.
A modelled data warehouse
Raw data shaped into clear tables for customers, orders and revenue, version-controlled and tested like any other code.
Shared metric definitions
One agreed meaning for "active customer", "churn" and "gross margin", written down and used by every report.
Dashboards people open
A handful of dashboards designed around real decisions and owned by named people, instead of fifty that nobody looks at.
AI-assisted analysis
Natural-language questions over governed data, AI-written summaries of what changed this week, and forecasting where history supports it.
Access and privacy controls
Role-based access, personal data handled deliberately, and a clear record of who can see what.
The data analytics ladder most businesses climb
You do not need to reach the top to get value. Most SMEs see the biggest return from the second and third rungs, and skipping rungs is how expensive AI projects stall.
Spreadsheets and exports
Stage 1Reports assembled by hand from system exports. Workable for a small team, but slow, error-prone and dependent on one person.
Centralised data
Stage 2Automated pipelines load key systems into a cloud warehouse every day. Nobody copies CSV files around any more.
Modelled and trusted
Stage 3Tested transformations and shared metric definitions mean the finance and sales dashboards finally agree with each other.
Self-service analytics
Stage 4Managers explore data themselves within guardrails, and analysts spend their time on questions rather than cleaning.
AI-ready
Stage 5Clean, documented, permissioned data that forecasting, recommendations, AI assistants and automation can safely rely on.
Why most AI projects are really data projects
When a business asks for an AI assistant that answers questions about sales, or a model that predicts which customers will leave, the first weeks usually go on data: finding it, joining it, cleaning it and working out which of three "customer" tables is the right one. The model is often the smaller part of the work.
Large language models make this more visible, not less. Ask an AI tool a question over messy, undocumented data and it will give a confident answer built on the wrong table. Clear definitions, tested pipelines and sensible permissions are what turn "chat with your data" from a demo into something a manager can act on.
AI also changes how analytics foundations get built. Our engineers use AI coding agents to draft pipeline code, generate data tests and document tables and columns, which removes a great deal of slow, repetitive work. A senior engineer still designs the data model and reviews every transformation, because a subtle join error in a revenue figure is exactly the kind of mistake AI tools make with confidence.
Questions we answer before recommending any tooling
The right analytics stack for a 30-person company is very different from one for a 3,000-person company. We start here, not with a vendor shortlist.
Which decisions should the data support?
Pricing, stock levels, marketing spend, hiring. If a dashboard would not change a decision, it probably does not need building yet.
Where does the data live, and how clean is it?
An inventory of source systems, their APIs or export options, and the quality problems already visible.
Who will own it once the build is done?
A part-time analyst, an operations lead or an outside partner. The design should match the people who will run it.
What will it cost to run each month?
Warehouse, pipeline and BI tool costs estimated at your data volumes, so there are no surprises in year two.
Which AI uses are realistic in the next year?
Where your data can already support forecasting, AI summaries or an assistant, and what needs fixing first.
Questions we often hear
What does a data analytics consultant do?
A data analytics consultant helps a business collect, organise and use its data to make better decisions. That typically includes connecting source systems, designing a data warehouse, agreeing metric definitions, building dashboards and helping the team use them. A good consultant will also tell you which analysis is not worth doing yet.
Do small businesses need a data warehouse?
Not always. If your reporting comes from one or two systems and their built-in reports answer your questions, a warehouse is overkill. Once you are combining data from several tools, rebuilding the same spreadsheets every month or planning AI projects, a lightweight cloud warehouse usually earns its place.
How do I know if my data is ready for AI?
Data is broadly ready for AI when it is centralised, consistently defined, reasonably complete and access-controlled. If you cannot produce a trusted report on a topic today, an AI system will struggle to answer questions about it reliably. A short assessment shows which AI use cases your data supports now and which need groundwork first.
How long does it take to set up data pipelines and dashboards?
A focused first phase covering a handful of core sources and a few key dashboards can often be scoped in an Advisory Sprint and built over several weeks. Estates with many systems or poor data quality take longer, so we phase the work to put useful dashboards in front of people early.
Which BI and data tools do you recommend?
We are vendor-neutral and choose based on your size, existing systems, budget and skills. For many SMEs a managed cloud warehouse, a transformation tool that treats SQL as tested code and a mainstream BI tool is plenty. We steer clear of heavyweight platforms that need a dedicated team just to keep running.
Related reading and tools
Tell us where your numbers come from.
Share the systems you use, the reports you struggle with and any AI ideas on hold. We will reply within 24 hours with a view on what to fix first and what can wait.
Working with companies globally · Response within 24 hours