We help you build a single home for the data your teams and agents read.
This is for you if...
Your AI gives answers your team quietly fixes by hand before anyone sees them.
You have data in five different places and no single source of truth.
Every AI project you start hits the same wall: the data is not there, or it is not right.
You have built on messy data before. You know how it ends.
Where your operational data lives, what it means in each place, and how it moves between them.
We look at your CRM, ERP, spreadsheets and whatever else is holding records. Each field is traced back to where it originates and to every place it has been copied, with the disagreements between those copies listed.
One agreed answer to what each of your business terms means.
We help you write this down, so that each definition names the systems it applies to and the person who owns it. Where two systems currently disagree, both versions are recorded next to the one you have decided is correct.
The definitions, rules and processes from the library, packaged so anyone (humans and agents) can use them.
What's our occupancy this month?
I can see 71%, 68% and 74%. Which one do you mean?
What's our occupancy this month?
71%. Source: Occupancy, in the definition library. Revenue owns it.
We build the context layer that enables the shared understanding of your data and metrics across any application in your business operations.
How to keep the layer current after the engagement ends, written for whoever on your team will be doing it.
We show your team how to keep it current: adding a system to the map, changing a definition, and what to check when an output starts looking wrong.
EUR 2.5M a month lost inside a spreadsheet
Quick-commerce operator, 200+ warehouses
A quick-commerce company running over 200 warehouses had outgrown its systems. Master data, inventory, supplier ordering and fulfilment all lived in ungoverned Google Sheets, so procure-to-pay was stuck, demand forecasting couldn't run, and supplier orders didn't match deliveries or invoices. The gap was costing an estimated EUR 2.5M a month. A unified commercial ops platform, integrated with suppliers' systems, gave all of it one governed home.
Start with a free call. From there we either run a full value iteration on your stack and hand you the plan, or we stay on as your fractional CTO for data and AI.
Free, 30 minutes
Bring one thing that's stuck. We tell you what's wrong with it and what to do first.
Book a checkpointScoped engagement, one-off
We analyse your whole stack and build a pragmatic plan: what's broken, what to fix first, and who should own it. Your team or any vendor you hire can run it.
Ongoing, fractional
We work with you a few hours a week on what moves the strategy: prioritisation, vendor calls, architecture decisions and hiring.
If your AI is producing outputs your team has to correct by hand, your data is not ready. Data readiness means an AI model can read your records and trust them: clean field definitions, consistent values, no missing context that only a human can fill in.
Cleaning fixes individual records. A data layer is structural: it defines what your data means, how it relates across systems, and how it flows. Cleaning fixes typos. A data layer gives your data a grammar.
Demo data is pre-cleaned and consistent. Production data has edge cases, missing values, and business logic that was never written down. The AI Data Layer makes your production data behave the way demo data does.
Usually not. We work with your existing CRM, ERP, and databases and add structure on top. A full replacement is only recommended when the foundation is fundamentally broken, and we will tell you that upfront before you commit.
Most engagements run 4 to 8 weeks: an audit phase, then the data model, the definitions and the sequence for putting them in place. The building is done by your team or a vendor you choose, and the plan is ordered so the first fixes land before the engagement ends.