Data & AI
Your numbers live in spreadsheets, your documents live in inboxes, and your systems don't talk. We build the data platform, the reporting and the automation that turn that mess into decisions, and where AI genuinely pays off, we build that too. In-house, onshore UK, supported after launch.
Proof
Stonewater is a housing association that manages about 33,600 homes. Its documents were dispersed across repositories. Search was unreliable. Invoice handling ran on manual effort.
A SharePoint document-management platform that applies metadata automatically. A standalone .NET supplier portal. Purchase-to-pay processing where Azure AI reads each incoming invoice, validates amounts and work-order codes against the financial system, and flags exceptions or triggers payment under business rules.
Tens of thousands of documents are in one structured environment. Suppliers manage their own submissions. The system validates invoices before approval, without manual filing. Subject Access Request compliance is complete across the document estate.
Most of what you'll read about AI is written by someone selling it. Here's the version we'd give you in a scoping call:
Where it pays off today: reading and validating documents at volume, matching and de-duplicating records, surfacing exceptions for a human to decide, and answering questions over your own well-organised data. The common thread, a narrow job, high volume, and a human still owning the judgement calls that matter.
Where it doesn't yet: anything where a confidently wrong answer is expensive and nobody checks. If your underlying data is scattered and inconsistent, AI amplifies that problem before it fixes anything, which is why most "AI projects" that fail were actually data-platform projects that got skipped.
The order that works: sort the data foundation first, automate the obvious workflows second, apply AI where a measurable job justifies it third. If you ask us for AI and what you need is a data platform, that's what we'll quote for.
Data work rarely arrives alone. If the answer involves a system that doesn't exist yet, a portal, a line-of-business build, an integration layer, that's our bespoke practice, same team, same building. If your estate is already Microsoft, Dynamics 365, Dataverse, Power BI, the Power Platform page covers how we work inside it.
Exactly there. The scoping call starts with where your data lives today, not where a vendor wants it to end up. The usual first step is small: one workflow, one report, or one document process that proves the value before you commit to a platform.
With UK hosting and onshore delivery, the people building and supporting the system are our own UK team, and we're upfront in scoping about where every component runs.
By scoping the job narrowly and keeping a human in the loop where it matters. In the Stonewater build, AI validates invoices against the financial system and flags exceptions to people, it doesn't get to guess unsupervised. That pattern, automate the volume and escalate the judgement, is our default.
Possibly not yet. If the data foundation isn't there, a platform-and-reporting project usually returns more, faster. Scoping tells you which one you're actually in.
Are you not ready to talk yet?
Talk to us
Thirty minutes, no pitch deck, you'll leave with a view of what's trapped, what it would take to free it, and whether AI belongs anywhere near it yet.
