Every engagement is led by Double Six operators — people who have built, rescued and scaled the thing they're assessing. Situation, problem, outcome — no slideware.
A well-funded private-equity data platform was building on assumption, not evidence. Product-market fit was asserted rather than proven, the roadmap was driven by a handful of voices, and investors wanted to see a return on their money.
We defined and evidenced product-market fit from the market in, then rewired prioritisation so every roadmap decision traced to validated demand and commercial pipeline — a defensible articulation of fit, and the foundation to sell the product rather than perpetually rebuild it.
Entering a crowded, price-driven category years behind well-funded digital natives — with a legacy cost base and a business case resting on customer assumptions nobody had tested.
We tested the assumptions to destruction, rebuilt targeting around real customer need rather than persona, and launched across two continents on an AI-native operating model built to scale to dozens of markets on a lean team — winning on trust, ease and reliability, not the cheapest megabyte.
Every new client and market made a critical platform more expensive to run, not less. Outages, offline deployments and an unmanaged codebase had stalled a flagship international programme for well over a year.
We rebuilt the roadmap around real business capability, installed release governance where there was none, and reset ownership across a complex global organisation. Scale stopped being a cost.
A genuinely novel AI product in a category with no established buying behaviour. Hard to explain, harder to price — and a funding raise depending on it.
We turned the technology into a defined, priced, sellable proposition with an investor-legible milestone plan — and designed the product organisation to be built the day funding landed.
[REVIEW — before publishing: confirm which of these engagements may be named publicly (e.g. the PE data platform, the telco eSIM launch), and whether any client logos or specific figures can be shown. Currently all are described by sector only, with no client names, no logos and no internal numbers.]
In each case, the job was converting an assumption everyone had agreed to stop questioning into something tested — and either proven or replaced.
The product is the visible output; the operating model is what makes value repeatable. We build functions that compound, not ones that simply get busier.
Investor milestones, ROI, board business cases, cost-to-scale. Every decision is argued in revenue, margin or enterprise-value terms.
If you're backing a business — or getting one ready to back — let's talk about what's really driving its value.
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