A record of the kinds of estates and situations we have worked in. Anonymised, but honest. The point is not the metric. It is the shape of the problem, and the shape of the answer that tended to come out of it.
Sectors where we have worked repeatedly, at scale, and where the estate patterns are familiar to us.
Banks, asset managers and payments. AI consulting for financial services means data estates weighed down by history and regulatory pressure on every architectural choice.
Providers, payers and health-tech. AI implementation in healthcare demands complex data, strict privacy posture, and use cases that deserve doing properly.
Product organisations where the platform decisions and the engineering operating model decide the P&L.
Consumer businesses where data, cloud economics and personalisation intersect, and where the bill can move quickly.
Large content and infrastructure estates. Usage-driven costs, high concurrency and long-lived architectural choices.
Programs where the technology answer has to survive procurement, transitions of leadership and a public standard of scrutiny.
Diligence, post-close and value-creation work across portfolio companies, delivered at operator pace.
Small and medium-sized organisations whose core business is not technology, and who now want AI and modern data workflows brought into the work properly. End-to-end implementation, plainly explained.
Anonymised sketches. If any of these feels familiar, the conversation tends to be a good one.
The board had asked for an AI strategy. Twelve months in, nothing was in production. The real issue was three data teams building the same pipelines against three copies of the same customer record. We wrote the read, consolidated the pipeline, and the AI use cases quietly started shipping on their own.
Bill was up 40% year on year. The platform team was being blamed. The actual cause was a product team using a usage-based service in a way it was not designed for. Fixed once at the source, the run rate fell and stayed fallen.
Two competing dashboards were quoting different numbers for the same metric in the same meeting. Getting to one number involved a semantic layer, some governance, and telling the truth about which of the definitions had actually always been right.
Two engineering organisations, one product roadmap, no shared architecture. We ran integration as a program, wrote the target state in plain English, and stayed for the six months it took to become real on the ground.
Twelve overlapping vendors, four of them renewing within the same quarter, no owner reviewing them. A methodical read of usage and contracts turned into a plan that consolidated the estate and funded the next year of platform work.
Twelve engineering squads, plenty of activity, very little shipping to end users. The plan called for more process. What actually helped was fewer priorities, clearer platform boundaries, and one round of quiet re-organisation.
If it does, the conversation is short and free. If we can help, we say so. If we can not, we can usually point you to who can.