Defining a New Category
Category-defining companies arise from time to time in certain sectors, when technology, opportunity, culture and a long-standing problem to be solved come together in a unique space and time.
Working on MercuriDash over the years, I have always wanted to create a company that stands apart: one that defines territory and innovation and pushes culture forward, rather than iterating in increments or improving on existing solutions.
It's an ambitious goal, but that is the aim and the north star for me as a founder. Not just to do something, but to do a unique something, in a space that is exploratory and pioneering but still grounded in real value.
Here is why I think what we're building, which we call product decision intelligence, is category worthy.
1. Moving Beyond Efficiency
That sounds strange from a technology company, because the premise of most software is doing things faster, more easily and more cheaply. We do that too. But we expect efficiency from technology now, and AI has accelerated it so much that we can almost take it as read.
The first wave of technology and AI optimised for efficiency within a function. We're now in an era where intelligence can drive outcomes: business success, growth, competitiveness and enterprise value. In product, that means using technology to differentiate from competitors, enter new markets, build customer engagement and strengthen brand equity.
Systemising strategic decision-making, and learning from those decisions, unlocks outcomes people weren't focused on, or that weren't possible before. Efficiency is part of it, but it isn't the endpoint. Long-term success is.
2. Bringing Every Lens Together
Most product software is designed around one function and sees the world the way that function does. Merchandising software takes the merchandiser's view, creative software the creative's, production software the operational view. All important, but each is one worldview.
What we're building is closer to simulating a board of experts sitting around a table looking at the same product together: a creative director, a merchandiser, a production specialist, a market analyst and a brand strategist, each bringing different lenses and priorities, embedded in one evaluation instead of a decision fragmented across different tables at different points.
There's a second advantage: how information gets used. Each function over-indexes on the data that matters most to it. Creative teams lean on trends but may ignore cost and feasibility. Merchandising teams look at margins and sales but may give less weight to constraints or brand identity. That's natural, but it builds bias into decisions. Bringing all the relevant data in at once, through every lens that matters, guards against it.
3. Collapsing the Sequence
This may be the most transformative part, because it isn't automating an existing process or making the same steps happen faster. It changes the relationship between analysis, creation and evaluation.
Traditionally, product development is sequential. You gather information, develop a brief, create concepts, evaluate them, discover what works, go back and change things, and maybe analyse performance for next time. You move backwards and forwards between stages, people, tools and teams, each with fixed lead times, and a lot of the learning gets lost.
Collapse evaluation and development, and a product is evaluated while it's being developed. Your criteria actively shape what you develop instead of being checked afterwards.
Strategy stops being something you formulate and then implement. It informs development as development happens, and development can expose inconsistencies in the strategy that need revisiting. The feedback loop collapses.
Why now
Despite all the tools and all the efficiency AI has unlocked in the last three years, brands are still making products that should never have been made, and still missing opportunities to create stronger ones. That shows up downstream: stock that doesn't move, competitive opportunities missed, brand equity slowly eroded.
The problem has persisted because these decisions are complex. No single person holds every lens, and there are so many kinds of evidence and expertise to consider while also being creative and hitting commercial targets. Historically, all of that has been fragmented across stages, people and software.
Advances in AI now make it possible to bring it together, and to turn the strategic thinking behind which products should exist into a repeatable, evidence-led system that compounds because it learns.
That is the convergence a new category needs: the technology, the opportunity and a long-standing problem, at the right moment. It's what I set out to build, and why I think it's worth building.