Validating the
Inputs-First Thesis.
The industry operates on two expensive assumptions: that organic growth requires manual scale (more posts, more writers) and a link-building budget.
"I built Banksparency to determine whether a superior data architecture can capture high-intent demand without relying on either of those."
The Hypothesis
Incumbents like NerdWallet rely on massive editorial teams and site authority built up over many years to win generic, but high-value "Best of" keywords.
My thesis was to change the playing field. Instead of competing on "Authority" (a losing battle), I engineered a system to compete on Data Granularity. By ingesting live rates for 80+ institutions, I targeted thousands of specific, high-intent queries that incumbents are too broad to serve effectively.
The Validation
*Powered entirely by automated data pipelines.
Architecture as Strategy
Decoupled from editorial calendars, I built a programmatic engine designed to turn raw data into structured inventory—ensuring the answer coverage scales instantly with the dataset, not headcount.
Hybrid Rendering
Built on Next.js. Critical content is pre-rendered on the server for instant crawler access, while interactive elements hydrate selectively for users.
Automated Pipeline
A serverless Supabase pipeline ingests rates from 80+ institutions daily. Manual data entry is 0ms. The content maintains itself.
Data-as-Product
Instead of 1,000-word essays, I serve interactive, filterable data tables. This Utility creates an engagement moat that static content cannot replicate.
Different Battlefield, Same Rigor.
For Banksparency, the leverage point was a Programmatic Database.
For your organization, it might be Inventory Depth, Proprietary Data, or User-Generated Content.
We will identify your specific unfair advantage and architect the infrastructure to turn it into a dominant growth channel.
Find Your Leverage Point