AI is only as good as the data beneath it. Scattered, conflicting data is the number-one reason pilots fail. We build one clean, trusted, AI-ready source — in open formats you own.
The reason most AI pilots fail sits beneath the model, in the data. Gartner puts the average cost of poor data quality at $12.9M a year; MIT Sloan estimates 15–25% of revenue lost to it. Fix the foundation and everything above it starts to work.
Reports disagree, month-end is a manual reconciliation marathon, and every AI pilot stalls because the data underneath is scattered and unowned. You are likely losing 15–25% of revenue to bad data — a cost that stays invisible precisely because the data is unreliable.
Untrustworthy decisions, AI investments that fail on bad inputs, and a costly re-platforming later when the patchwork together does not finally give way.
A representative planning scenario for a GCC mid-market organisation, built from the industry benchmarks cited below.
How to read this: recovering a fraction of a 15–25% revenue leak dwarfs the build cost — and because the work unplugs alternative fixing of reconciliations, it pays twice. Open formats mean you own the asset with no lock-in.
A deliberate sequence that proves value early and de-risks each step.
Inventory sources, profile quality, and the highest-value domain to fix first.
Design the warehouse and model in open formats, define governance.
Build pipelines, clean and reconcile the domain, stand up trusted reporting.
Add governance, then extend domain by domain across the estate.
We build on open formats (Iceberg, Parquet) so the data you own is yours to move — no lock-in. And we prove the pattern on one high-value domain before scaling, so you are not funding a multi-year platform on faith.
You own and can move the data — no lock-in to our tools or a vendor’s.
The pattern is proven and paying back before the next domain is funded.
Ownership and quality standards are set with the first domain, not bolted on later.
Every AI project you start on messy data joins the 95% that fail, while a 15–25% revenue leak runs unseen. Fixing the foundation first is what makes everything above it pay.
“Take back control of your data: own it, govern it, in open formats — so every AI you buy actually works.”
No — we build on open formats, so you own the data and can move it freely. No proprietary trap.
Often yes. Most AI pilots fail on the data beneath them, not the model — fixing the domain that pilot needed is frequently enough to bring it back.
With the single highest-value domain — usually finance or the core operational data — proven and trusted before we extend to the next.
Field-tested perspectives and practical guidance for this capability.
Technical Paper TrendingWhy every AI investment depends on a single, governed, AI-ready data foundation — and how to build one in open formats you own.
