Inside ChatFin: How We Decide What's Worth Building

When Listening Isn't Enough

Traditional product development is built around clarity: define, scope, validate, build. But with AI, clarity often comes after creation.

Users can tell you what frustrates them. They can describe their workflows and pain points. But they can't always articulate the thing they've never seen, the leap that changes the category entirely.

It's like asking users in 2006 what they wanted in a phone. They might have said, "a better keyboard." Not "a slab of glass that redefines communication."

We think of AI features the same way. Sometimes, you don't build to meet demand; you build to reveal it.

The Problem with Over-Validation

If you only build what's validated, you'll never discover what's possible.

If you only ship what's certain, you'll never ship what's new.

We've seen teams trapped by analysis: endless surveys, integrations, and requirements meetings that slow learning to a crawl. In the time it takes to prove a hypothesis, the frontier has already moved.

In a world where AI models evolve weekly, speed of understanding beats certainty of planning.

Build to Think, Not Just to Ship

Our approach flips the usual sequence. Instead of waiting for perfect alignment, we build prototypes fast, lean, and focused, to make ideas visible.

Because something magical happens when people see a prototype.

Their language changes. Their imagination expands. They begin to understand not just what they wanted, but what they could want.

We call it "the next-level effect", that moment when a working demo turns a vague discussion into a shared vision.

Building, then, isn't just execution. It's collaboration in motion.

This iterative approach allows us to fail fast, learn quickly, and pivot when needed. Each prototype teaches us something new—not just about the technology, but about how people naturally interact with AI-powered tools. These insights are impossible to capture in focus groups or survey responses.

Architecture of Agility

Our decision framework rests on three simple pillars:

It's not chaos. It's a disciplined dance between intuition and validation. Between what's imagined and what's proven.

When AI Changes the Economics of Building

AI has fundamentally altered the cost curve of experimentation. What once took months now takes hours. What once required a team can be tested by one person with the right prompt.

That means the opportunity cost of not building is now higher than the risk of building.

The more we build, the more we learn, not just about the product, but about the people who will use it.

And when those two threads meet, user need and built insight, that's when breakthrough features emerge.

This economic shift has democratized innovation within our team. Engineers can now test complex ideas in days rather than quarters. Product managers can validate assumptions with working prototypes instead of static mockups. Designers can iterate on interactions in real-time with actual AI responses rather than simulated ones.

The Culture of "Just Build It"

Inside ChatFin, "just build it" isn't recklessness; it's respect for momentum. It's the belief that a good idea deserves a body, not just a slide.

We still prioritize based on user value. But we leave room for creative conviction, those moments when a feature feels right even before it's rationally proven.

Because in the long run, the features that matter most are the ones that teach us something new about our users, and about ourselves.

And that's how we decide what's worth building.

We build to learn, we learn to build, and we never stop moving.