Work our founders have delivered.
Three projects our founders delivered hands-on, from data ingestion and curation to the dashboards people use every day. Client names stay private under the agreements we worked under.
Open a case file.
From scattered spend data to live control of cash flow and DPO.
Procurement teams were flying blind. Spend data sat in disconnected systems and had to be pulled together by hand, so nobody could get an accurate, timely read on what was being paid to whom, or when. Leadership was reacting to historical figures instead of managing cash.
Every curated dataset was checked against the systems it came from before it fed a dashboard, so procurement and finance were working from the same reconciled numbers.
800 systems. One version of the numbers, worldwide.
The client's data was trapped across 800 disjointed systems around the world. Global reporting was chaotic and inconsistent, and every cycle took weeks of manual compilation. Teams in different regions were looking at different numbers.
Data from each source was curated and validated into the shared model before reaching a dashboard, so every region reports from the same figures.
A secure, AI-ready data foundation for a global pharma company.
The client's data infrastructure lacked standardisation and modern capabilities. Data sat in silos, ingestion was slow and manual, and there was no safe way to put modern AI tools to work on company data.
Each data source was ingested and curated to the agreed standards before being opened to teams and to Copilot, so AI answers draw only on governed data.
Every delivery drew on several of these.
How we built them.
How do you get 800 systems into one model?
Pipelines first, then curation. On One Global View we architected pipelines across all 800 sources, then handled the ingestion and curation needed to standardise data that arrived in very different shapes, and built a single source of truth on top of that rather than wiring dashboards straight to the sources.
How do you know a number on a dashboard is right?
Every curated dataset is checked against the system it came from before it is allowed to feed a dashboard. On Clear Spend that is what let procurement and finance work from the same reconciled figures instead of arguing about whose export was newer.
Which platforms do you build on?
AWS, Microsoft Azure, Google Cloud, Microsoft Fabric, Databricks and Snowflake. We build on what you already run rather than moving you onto something new, because migration cost usually buys you nothing on its own.
How do you make company data safe for an AI assistant?
By deciding what the AI is allowed to see before you switch it on. On AI Foundation each source was ingested and curated to agreed standards, and governance and access rules were set, before anything was opened to teams or to Microsoft Copilot. The AI answers from governed data or it does not answer.
How do you handle sources that contradict each other?
They get reconciled into one governed model, with the definition agreed once rather than per report. That is the work: not building another dashboard, but settling which number is the number and recording where it came from.
What happens to the pipelines after you finish?
You own the code and the documentation, and there is no lock-in. Our engine speeds delivery up, but nothing we build depends on us staying.
What they changed.
What actually changes when spend sits in one model?
You stop reconstructing the picture by hand. Clear Spend replaced a manual pull across disconnected systems with an automated, centralised view, and put cash flow and Days Payable Outstanding in front of leadership as they happen rather than after the quarter closed.
We already have dashboards nobody trusts. How is this different?
Trust is a lineage problem, not a visualisation one. Dashboards get ignored when the numbers never match the source system. We reconcile first and build the dashboard second, so the answer to "where did that come from" is a record, not an opinion.
How long before we have something we can use?
The three projects here ran five months, six months and a year, end to end, and the scope was a full platform each time. How long yours takes depends on how many systems are involved and how messy they are. We give you a straight estimate after a discovery call, not before.
Which of these three is closest to our situation?
Match on shape rather than sector. Many systems and no agreed global number is One Global View. Spend and cash visibility is Clear Spend. No safe foundation to put AI on is AI Foundation. If you are not sure, that is a good first question for a call.
What do you need from us?
Access to the people who own the source systems, whatever architecture documentation exists, and a sponsor who can give it a couple of hours a week. Projects stall on decisions, not on engineering.
Does this have to be one big programme?
No. These three were full platform builds because that is what each client needed. Plenty of work starts with one reporting line that nobody trusts and grows from there. We start where the pain is.
Want to talk through an approach?
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