Before founding StructureFlow, I spent eight years as a corporate lawyer at Slaughter and May and Farrer & Co, advising on highly complex, high-stakes transactions.
These were multi-billion-pound deals involving layers of entities across multiple jurisdictions, with ownership, control and risk constantly shifting as negotiations evolved.
To manage that complexity, we relied on structure charts, timelines and diagrams. They were essential. They imposed order on something that would otherwise be overwhelming. But they were also static and slow to build. Worse, every time a deal changed, someone had to rebuild the picture.
Documents and diagrams made things readable, but not truly navigable. They flattened dynamic relationships into snapshots. As structures evolved, those snapshots quickly became outdated. That’s where risk crept in. Not because anyone was careless, but because the tools weren’t built for constantly shifting complexity.
The blind spot inside elite dealmaking
Early in my career, I noticed that structural complexity initially lived in the heads of senior partners. Experience allowed them to hold intricate ownership chains, governance dynamics and risk exposures mentally. For a long time, that worked.
But as transactions became larger, more international and more layered, that approach began to strain. Structures grew denser. Regulatory expectations increased. The number of entities and moving parts expanded. Complexity was no longer something you could reliably carry in your head.
That’s when things started to change. Instead of relying purely on memory, I began writing structures down, building charts and diagrams to externalise what had previously been internal knowledge. I quickly saw how powerful that was. Visualising relationships between entities and governance layers allowed senior partners to reason more clearly.
I began developing increasingly detailed structural models in PowerPoint, refining them as deals evolved. But as complexity increased, the limitations became obvious. Each change required manual updates. Every new layer added fragility. We were stretching 2D tools to manage 4D reality.
It became clear we needed something purpose-built for reasoning about structures that were constantly evolving. That was the inflection point.
From insight to structural intelligence
Leaving a secure legal career wasn’t a decision taken lightly. Law offers predictability and a well-trodden path. Building a company offers none of that. But I had reached a point where the opportunity felt too significant to ignore.
I wasn’t trying to build a better diagramming tool. I wanted to create something fundamentally different: a platform designed for reasoning about complex, constantly evolving structures. What emerged from that thinking was what I now describe as structural intelligence. The ability to model relationships, governance, control and dependency in a way that reflects how they actually operate in the real world.
Having initially worked on the idea in my spare time, it soon became clear I needed to commit fully. What was required was a system designed to model complex structures properly. One that could connect relationships, map dependencies and surface implications in real time. Crucially, when one element changed, that change had to ripple automatically across the entire structure. So in 2018, I launched StructureFlow.
The early days were about education. When you create a new category, the first challenge isn’t building the product; it’s helping the market articulate the problem. Many firms felt friction. They knew their world was becoming more complex and that navigating the information needed for strategic decisions was getting harder. But they hadn’t labelled the root cause.
Our role was to show that the issue wasn’t a lack of data. It was a lack of structural clarity. Once that insight landed, the value became obvious.
When professionals could see a live, connected model of a structure rather than a static slide, they could test scenarios, understand how changes would ripple through the system and make decisions with far greater confidence. What had previously taken days of planning and hours of manual cross-checking, became intuitive.
Meet ‘The Relentless Fox’ – the former NCC CIO now building Relentica
Scaling clarity in an AI world
Today, StructureFlow works with global law firms including Linklaters, Slaughter and May, Norton Rose Fulbright and Holland & Knight, and we are expanding into alternative finance and corporate tax. The common thread is complexity. Where structures are dense, high-value and constantly evolving, clarity has measurable impact.
The rise of artificial intelligence has only reinforced the original insight. Organisations are racing to introduce AI into decision-making, but AI can only work with the information it is given. If that structure is fragmented or incomplete, it accelerates confusion and risk. Before you can automate strategic judgement, you need a clear, connected model of how the underlying relationships fit together.
It’s the same in software engineering. AI can generate impressive code, but unless it understands how the wider system fits together — how components connect and what depends on what — even well-written code can destabilise the application. Without context, intelligence becomes risk.
What began as a way to solve friction inside legal dealmaking is increasingly seen as foundational infrastructure for enterprise decision-making and AI implementation more broadly.
My biggest lesson
Looking back, the biggest lesson for me is this: some of the most valuable business opportunities sit inside everyday processes that have outlived their usefulness. If an entire industry is using workarounds to compensate for a structural limitation, that limitation is likely hiding significant value.
The organisations that win won’t be those that avoid complexity, but those that can see and navigate it clearly. That was the blind spot, and turning it into a practical, scalable solution is what built my multi-million-pound business.