Case Study 01 — Fintech · AI · 0 → 1

OpraBills

Turning a founding vision for AI-powered personal finance into a validated, consent-first MVP.

Role: Product Manager & Product Designer
Timeline: Apr 2025 — May 2026
Company: OpraBills LLC · Michigan, USA (Remote)
Platform: Web
Tools: Figma · Notion

OpraBills is an AI-powered personal finance platform built for the US and Canada market — a single place to see your money clearly: unified financial dashboards, subscription tracking, and credit health monitoring, all enhanced by AI.

I joined as the founding product hire, wearing both the Product Manager and Product Designer hats. My mandate ran the full 0 → 1 arc: translate the founder's vision into a product narrative, prove people actually wanted it, define what the MVP should be, and work side-by-side with engineering and AI teams to bring it to life.

Personal finance is a crowded, trust-sensitive category. Three problems shaped everything:

Unproven demand. The platform was pre-launch, pre-product — nothing existed to test the idea against.
A fuzzy vision. "AI for personal finance" can mean a hundred things. It had to become one sharp, buildable product story.
A high bar for trust. The product touches bank accounts, subscriptions and credit data — the design had to earn consent, not assume it, and meet US & Canada privacy expectations for sensitive financial data.

Before writing a single line of MVP code, we needed proof that the market wanted what we were about to build.

Validate first — the waitlist site

Rather than build the product and hope, I designed and launched a waitlist site as the proof of concept. It had one job: communicate the product's value in plain language and give the market a way to raise their hand.

The waitlist became our first product decision made with evidence — validating market demand and securing early sign-ups before committing engineering effort to the full platform.

Defining the MVP feature set

With demand signal in hand, I defined the core features that would make OpraBills useful on day one:

Unified financial dashboards — every account in one honest view.
Subscription tracking — surface recurring charges people forget they pay for.
Credit health monitoring — make an opaque number understandable and actionable.

Each feature was framed around a user problem, not a technology — the AI layer existed to sharpen the experience, never to be the pitch.

Consent-first design

Because the product handles sensitive financial data, I prioritised user trust in the design itself: clear consent flows, transparent data-usage explanations, and no dark patterns anywhere in the account-linking journey — aligned with US & Canada privacy standards.

Building the MVP with engineering & AI

I collaborated directly with the engineering and AI teams through MVP delivery — writing specs, reviewing builds against designs, and making the daily trade-off calls that keep an early product coherent. My design background meant handoffs were sketches and conversations, not walls of documentation lost in translation.

Validated demand

The waitlist launch validated market demand and secured early sign-ups — de-risking the build before it began.

A defined MVP

Unified dashboards, subscription tracking and credit health monitoring went from founder's vision to a shipped, coherent MVP built with the engineering and AI teams.

Trust by design

Consent-first flows and transparent data handling built to US & Canada privacy standards — trust treated as a feature, not fine print.

Cheapest test first. A waitlist page answered the riskiest question — does anyone want this? — for a fraction of the cost of building the product.
Vision becomes product through narrative. Writing the one-line story users hear forced every feature decision to justify itself.
In fintech, trust is the interface. Every screen that asks for sensitive data must explain itself — clearly and before the ask.

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