The same customer exists three times in our system, spelled three different ways.
Duplicate records
Understand your numbers · The groundwork
Before you spend serious money on AI, make sure your data can carry it.
This is the groundwork every other technology investment depends on, and the step most organisations try to skip. We assess, clean, and organise your data so the reports and AI built on it give answers you can actually trust.
Interactive illustration · sample data
34/ 100
CRM1,204 records
Spreadsheets40 files · 18% duplicates
Accountingnames do not match CRM
Website leadsno owner assigned
3 systems disagree on the same 1,204 customers
When the numbers disagree
Two departments, two reports, two different numbers.
We ran an AI pilot last year. It is not something we talk about.
The quiet failure
Honestly, nobody is sure who owns which data, or who is allowed to see what.
No ownership
None of this means your team is careless. Data drifts in every growing business. The difference is whether you fix it before or after it costs you a big decision.
What we build
The groundwork we do.
A full audit of your data and its quality
Where it lives, how good it is, where it disagrees with itself, and what that is costing you.
Cleaning, deduplication, and enrichment at scale
Duplicates merged, gaps filled, formats standardised, across the whole estate rather than one spreadsheet at a time.
Data architecture and design
A clear structure for how your data should be organised, so the mess does not simply grow back.
A proper central store in the cloud
One place where your data lives, governed and backed up, instead of forty exports on personal laptops.
An honest AI readiness assessment
What you could build on your data today, what needs fixing first, and a roadmap in priority order.
Clear ownership and access rules
Who owns what, who can see what, and how long things are kept, written down and enforced.
The payoff
What solid ground looks like.
When the foundation is right, everything built on top of it gets easier, and most of the arguments simply stop.
One shared, trusted view
One shared, trusted view of your data across the whole business.
Reports that match on the first pass
Reports that match on the first pass, so meetings are about decisions instead of definitions.
A clean base for what comes next
A clean base for any dashboard, forecast, or AI feature that comes next.
NDPR-aligned handling
Data handled and retained in line with the NDPR, so compliance stops being guesswork.
A roadmap you can act on
A roadmap in priority order you can act on, with us or without us.
The unglamorous step first, so the exciting parts actually work.
How we work
How a readiness project runs.
01
Stage 01
The audit
A clear picture of what you have, what is broken, and what each problem costs, explained in plain language rather than database vocabulary.
02
Stage 02
The fixes
The cleaning, merging, and structuring happen in priority order, so the most expensive problems get solved first.
03
Stage 03
The governance
The rules and ownership that stop the mess growing back, because clean data is a habit, not a one-off event.
Cleaning data once is a project. Keeping it clean is a habit. We do both, in that order.
See the full approach →Industries
Where clean data is non-negotiable.
Clean, governed data is non-negotiable wherever a wrong record carries real consequences.
Financial Services & Fintech →
Secure banking, payments, and onboarding built to scale.
Healthcare & Clinics →
Records, scheduling, and patient care handled with care.
Insurance →
Policy administration, claims, and broker tools that move in days.
Government & Public Sector →
Citizen services and registries that issue in days, not months.
Logistics & Supply Chain →
Fleets, deliveries, and stock tracked from depot to door.
Manufacturing →
Production lines, quality, and output you can see live.
Questions
Asked before every data project.
How long does the audit take? +
Typically two to four weeks depending on how many systems you run. You get a written report: what you have, what is broken, what each issue costs, and a fix list in priority order. It is yours whether or not you continue with us.
Will this disrupt our daily operations? +
No. The audit reads, it does not change. The cleaning that follows happens in supervised stages with checks before anything replaces anything.
Is our data too far gone? +
We have not met that business yet. The further gone it is, the bigger the payback from fixing it, and the audit tells you exactly how big.
Do we need this before a dashboard or AI project? +
Sometimes light cleaning along the way is enough, and we will say so. But if your reports already disagree with each other, building on top of that just makes the disagreement faster. The audit settles the question with evidence.
How does this relate to the NDPR? +
Directly. Knowing what personal data you hold, where, and for how long is both good engineering and a legal requirement. The governance work covers retention, access, and consent records, so compliance stops being guesswork.
Build the foundation once, properly, and everything after it gets easier.
Tell us which systems hold your data today. We will reply within one business day with a way to measure exactly what shape it is in.
Book a data audit →