Data Solutions That Make Businesses Better
// AI Readiness
Get Your Business Data AI-Ready
Data scattered across the ERP, a dozen spreadsheets, and whatever the machines spit out on the shop floor. None of it in a state that any AI tool could actually use, even if you wanted to put one to work on it.
AI Doesn't Fix Bad Data. It Exposes It.
Point an AI tool at messy data foundations and it doesn't fail because the AI is weak. It fails because there's no single, trustworthy version of the numbers underneath - so it guesses, and guesses confidently.
Anthropic's own data team has written about exactly this: getting reliable AI-driven analytics came down to dimensional modelling, canonical datasets and treating documentation and lineage as an engineered product, not the AI model doing the asking. Good AI-powered analytics doesn't remove the need for a solid data platform. It raises the bar on one.
Building solid data foundations - the unglamorous, unshakeable groundwork that makes analytics and AI trustworthy - is what Johnny's been doing for over 20 years, across data architecture, data engineering and analytics engineering. Not a bolt-on AI feature. The groundwork that has to exist before AI is worth trusting on the numbers at all.
Most Businesses Aren't Even At The Starting Line
65%
of UK SMEs don't properly store their own company data in the first place
That's not an AI problem. That's the foundation problem AI adoption actually depends on - and it's worth fixing regardless of which platform ends up solving it.
Three Ways To Close The Gap
There's no single right platform - the right one depends on the business. Data platforms get delivered on whichever of these actually fits:
Microsoft Fabric
Meets a Microsoft-first business where it already lives. Unified with Power BI, SaaS-simple, less to own day to day.
Databricks
For a business with real engineering capability already in-house, or the ambition to build it. More control, more maturity, runs natively wherever the cloud estate already lives.
Oriel
The cost-conscious option, and Greyskull's own build. A fully managed platform on infrastructure that costs a fraction of the big enterprise names, with the fractional expertise to run it - without hiring anyone.
How It Actually Gets Built
Three steps. No jargon, no bloated project plan - just a straight path from spreadsheet chaos to a data estate that's actually ready for AI.
Assess
A clear-eyed audit of what's already there - the platform, the architecture, and how AI-ready it really is.
Build
A right-sized platform on whichever of the three fits best - Fabric, Databricks, or Oriel - solid enough to trust, flexible enough for what AI needs next.
Run
Fractional support that keeps it running, without hiring a full in-house team. Already got people in place? Training them to run and extend it themselves is just as valid a path.
What This Actually Costs
Nobody in this industry gives a straight answer on cost. Here's one anyway.
Building It Yourself
A small-business build - one data platform, a handful of source integrations, core modelling and a set of dashboards - typically runs £25,000 to £60,000 upfront.
Add the first year of running it, £62,000 to £125,000, and the real year-one number lands somewhere around £90,000 to £185,000 before the platform's even twelve months old. After that it settles into that same £62,000–£125,000 run-rate every year the business stays that size.
The Oriel Alternative
A fully managed data platform, built the right way from day one, and the expertise to run it - without the hire. Oriel runs on cost-effective infrastructure that, properly integrated, costs a fraction of a DIY build on a major enterprise platform.
That's not a shortcut - it's what knowing what you're doing looks like. Picking the right tool for a business your size, and structuring it properly so it's ready for AI, not a set of dashboards that'll need rebuilding the moment you want to do more with it.
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