Most business problems look like product problems, or marketing problems, or people problems. They're usually the same problem: parts of a system that have stopped talking to each other. That's the lens I bring to everything — and why I work across strategy, product, growth and AI rather than inside any single one.
About
Across customer-facing, revenue and operational roles within the PropTech industry, I've had the opportunity to watch businesses operate from the inside — the decisions, the trade-offs, the gap between what was planned and what actually happened. Every organisation I worked with taught me something different about how businesses create value, and where that value quietly leaks away.
The pattern I kept noticing was this: the most important things in a business are almost never contained within a single function. A product decision shapes what sales can promise. A sales decision shapes what operations can deliver. A delivery experience shapes what marketing has to explain. Pull one thread and the whole system moves. Understanding that — really understanding it — changes how you approach almost any problem.
That's why I work across product thinking, growth, analytics and AI rather than specialising narrowly. I find that I understand a business problem better when I can see it from multiple angles at once. I'm naturally curious about why certain businesses compound while others plateau, what makes a product genuinely useful rather than just usable, and where technology creates real leverage versus the appearance of it. This website is where I work through those questions.
How I Think
Not a list of values. Patterns I've noticed in how I actually approach problems — and keep returning to when things get complicated.
Perspectives
Each of these is a way of seeing. The interesting work happens at the edges — where one perspective meets another.
Case Study
SpeakUp interested me because the problem beneath the product is a behavioural one, not an educational one. The users understand English — they've studied it for years. What stops them is something else: the instinct to stay quiet, the fear of being judged mid-sentence, the gap between comprehension and confidence. Products that misread that distinction don't just underperform — they solve the wrong problem entirely, and then wonder why retention is low.
This analysis works backwards from that insight — through retention design, pricing psychology and growth strategy — to understand what it would actually take to build a product that earns consistent engagement from this kind of user.
The starting point wasn't the app — it was the user. Specifically, understanding why someone who already knows English would hesitate to use it. That meant looking at the social and psychological context first, before considering any product decision. The problem definition came from that, not from the feature list.
From there, the analysis built outward: how competitors are positioned, what they're missing, what retention actually requires when the barrier is emotional rather than informational, how pricing can reduce friction without undermining the product's perceived value.
The growth model at the end isn't a target — it's a structure. It maps the conditions under which a ₹1Cr+ ARR pathway becomes viable, and what would have to be true about retention and churn for those conditions to hold.
Resume
Customer-facing, revenue and operational roles across the PropTech industry — the detail behind what's on this website. Write to me directly and I'll send it across.
Get in TouchContact
If you're working on something in product, marketing or business strategy — or want to explore working together — I'd like to hear about it. I'm also open to conversations that don't fit a neat category.