Rita Sheth — Founder, Product Architect & Strategist

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.

Brands are still crucial when it comes to building a company. People may say UI and design is becoming less important as AI changes how we interact with companies and products, but that doesn't remove the need for brand value, resonance and distinctiveness. If anything, it makes brand recognition matter more: when an AI agent sits between a customer and a product, brand is often what gets that product chosen. Working on MercuriDash, we're focused on consumer products, but brand is an intrinsic part of creating good product. Here are a few things I think are important when thinking about what I call a magical brand, as opposed to a generic one, and the things they have in common.

The first is deep resonance with a niche subculture or a specific customer segment. Here's a test. Imagine offering a Haribo lover "Horribo": exactly the same, only cheaper, and it tastes better. A true fan would say, "No thanks, I'll stick with Haribo." That is how committed you want your core customers to be.

The second is a clear and unwavering vision of who you are and what you stand for. Resonance with customers over a long period comes from that clarity. Rick Owens doesn't care if trend agencies say yellow is the new black this season. When you walk into his store, you know you'll see black, asymmetry and avant-garde silhouettes. Haribo speaks to the kid in all of us. Products change, but the vision has to stay consistent, and every ad, every piece of marketing and every product has to communicate it.

The third is emotion, and this is the part people miss because you can't measure it on a spreadsheet. Leave it out, though, and you miss what captures people's imagination and makes them fans. Haribo is nostalgia, fun, innocence and playfulness. Rick Owens is being alternative and edgy, part of a counterculture with a little danger and rebellion. Apple is being an independent thinker, a self-starter and a creative entrepreneur.

The magic in a product comes from the brand having a sense of magic about itself in the first place. Resonance, clarity and emotion are the core of the brand, and they get distilled into the product's DNA. That's why every product needs to be brand aligned. Drift off-brand on individual products and you slowly lose the magic. Over a series of product drifts, you lose your core fan base. That is how so many brands end up rebranding and hiring new creative directors.

Product strategy needs to consider brand at the concept stage. This is why, at MercuriDash, brand is part of evaluating a product from the very beginning. Good product decisions compound brand equity; bad ones erode it. The balance is keeping your core customers happy and your brand strong while evolving with the market you're in.

Defining your brand is ultimately the founder's and the team's job. It's a very human thing, and I don't think AI should conceive of what a brand stands for. That comes from your values and your creative standpoint. What technology can do is help translate that identity into products, put it into words and keep you aligned with it.

You don't need it all figured out from the beginning. New brands can refine their identity as they build. For established and legacy brands, the challenge is often remembering what made them special in the first place and bringing it back to the fore.

At MercuriDash we are in the middle of a shift from being a platform to being infrastructure. As a platform, product strategy lives in one place and you have to come to it. As infrastructure, product strategy is something any agent can call, over any interface, and in return the agents are called on, connected and integrated at MercuriDash. The product stops being a destination and becomes a capability that lives wherever the work is happening.

This is what it means to truly live in a connected world. It is no longer about integrated tools, but about working seamlessly with them across any of our favourite interfaces, with agents finding and applying data wherever it lives, and applying it in appropriate ways. The value moves from the place where the data sits to the intelligence that knows what to do with it.

Viewed this way, platforms are probably short-lived, because we no longer need fixed workflows. Workflows are evolving into custom workflows: each person's own path through the work, shaped by what they are trying to do rather than by what the software decided in advance. Agents make that possible because they can compose the steps on demand instead of following the one flow a product ships with.

The second big shift follows from the first: it is a move from execution to strategic direction. Where can you add value when anyone can get and analyse data? The loop is recursive: once you can get and analyse the data, you can suggest directions, and eventually execute on them autonomously. For our product decision layer this becomes connective, callable, transferable and always on. And the same pattern applies to many other areas. Imagine an always-on CFO fetching data, tracking live spending and suggesting what you should spend that day. Or always-on counsel: live, real time and proactive rather than reactive.

This is the emergence of a new kind of AI, more than agentic. Agentic implies agents carrying out tasks on our behalf. What comes next is less a tool you operate and more a standing capability you consult, one that watches, connects, reasons and acts across everything you already use. It does not have a name yet. We need a new name for it.

Everyone keeps saying there is no moat, that general AI is eating everything. But there will always be opportunities to build moats. I am talking here specifically about technical moats, particularly the kind that develops when a company gets really good at one specific problem.

Companies can base a moat on owning proprietary data on how their users use their platform - but knowing how people use a platform is only useful and moat-creating when it is applied to active outcome improvement. With general AI encroaching more and more on things like memory and context, the moat is not simply whether you hold your company's context and can reason with it. It is how deeply you understand not just the brand, but the brand in the context of a category, market and space, based on thousands of relevant actions and their outcomes. Having general or even brand-specific context is not the same as having accumulated expertise in a particular domain.

General AI is general because it learns across broad sets of knowledge and tasks, with the aim of improving general reasoning. It can handle context too and hold user memory - but there is a distinction between specificity at the user level, where you understand one client's circumstances and particular problem, and specialism at the domain level, where you learn deeply within one area across many relevant cases.

The combination is powerful: interpreting a client's specific context through accumulated domain expertise. Where that involves learning across clients, it needs to be permissioned and sufficiently generalised so that IP and privacy is safeguarded, but this is the foundational premise of much specialist AI, that the category network effect will in turn fuel better outcomes over the network.

General reasoning is also not the same as proprietary learning and decision methods. How much weight should something carry? Which factors matter in this particular use case? What errors in reasoning lead to suboptimal outcomes, and what should change as a result? Those are questions that can inform updates to models and processes around a specific problem. A general model does not automatically possess a company's exclusive feedback, outcome history or methods for making those decisions. It can use agents to connect to more context but it relies on those connectors doing their job.

So today's moat is applying proprietary data and processes relevant to one area and one point in a workflow, and getting really great at that. It is not just creating something that performs better than general AI with context inputs or surface-level reasoning deployed through prompts. It is how that system develops, reacts and evolves through learning, and whether the improvement it produces is difficult for someone else to reproduce.

And that is just the technical moat. Operational advantages, distribution and network effects can create moats too. General AI becoming more capable does not mean every source of defensibility disappears.

The founder's journey is a unique path because, to succeed, founders often have to deny themselves the very things that people most value. Quite sanely, most people value peace, fun, leisure and personal relationships. Founders are not a monolith, they are all different to each other, and of course they are not robots, so they do value these types of things and understand they are good things to experience.

However, because they are driven by both a broader vision and a frighteningly narrow focus at the same time (a focus on a future no one sees, and a tunnel-visioned, short-sighted approach to the present), they approach life, decisions, relationships, pleasure, enjoyment and satisfaction from a fundamentally different point of view. It can look like these things are not important to them. They can seem odd, because they appear to enjoy things like hard work and suffering (which are seen as 'bad') and shun things like fun and leisure (seen as 'good').

But the truth is, it's not that they can't see that things like leisure and personal relationships are positive things to experience. It's more that they just organise priorities differently on a consistent basis.

What this very different approach to life means is that founders, over a long period of time, end up leaving things behind. The shift in priorities, and a different set of decisions arising from those priorities made again and again, lead to a shift in identity, core beliefs and fundamental values. And because people are drawn to and resonate with others who share similar values, it's no surprise that entrepreneurship redefines and restructures connections to people and environments.

For founders who haven't already experienced this, there may be people who become angry, remain in denial about shifting priorities, call them snobbish or selfish, or are simply baffled by how much they have changed. And the truth is that they have changed, even without realising it. It happens slowly yet surely, and is often more immediately apparent to others than to the founders themselves.

The change is incremental, not sudden, because entrepreneurship changes core identity and establishes a new set of habits and responses only over a long period of consistent action. It also happens not just because of small, consistent changes in behaviour, but because of the things founders go through: pain, rejection, lack of support from people, and constant learning. All of this leads to fundamental changes in core values and shifts in identity that aren't a conscious choice, but a by-product of the journey. This is what moulds founders into new characters.

Choosing to stay in to work, rather than going out at the weekend, for example, leads to certain consequences. Making these kinds of choices again and again leads to compounding consequences, until it becomes inevitable that founders end up in a totally different place from where they started. To take another example, learning about their core procrastination triggers and having to break those habits, or learning who in their old environment felt threatened by their attempts at success, and refining their understanding of people and the world, are all examples of learning as a by-product of this journey. That learning shifts their values and identity and leads them to leave things behind: not just people and places, but their old selves too.

At some point there may be a grieving of an old self, an old life and relationships that didn't make it. Then there may be a long period of walking alone, and no one really tells founders when they start a business that this will definitely happen to them, in small or bigger ways. The walking alone part is the bridge between the old self and the new life. It is not pleasant. It's hard, and it's where a lot of painful pruning and hard-learned lessons take place. But on the other side is a fresh perspective on what they experienced and where they came from, a viewpoint from which they can look back and appreciate that although life isn't necessarily better, it is right, because if they were following their path, it was always meant to be this way.