01 The problem
Choosing where to live in North Texas means comparing eighty-odd municipalities on things a listing never shows you: which elementary school your address actually feeds, what the tax rate does to your monthly payment, whether the commute is thirty minutes or fifty-five at 8am.
National platforms spread thin across 30,000 cities with shallow data and infrequent updates. Nobody was going deep on one metro.
The MLS is the menu. Nobody was printing the nutrition label.
02 One score, always decomposable
Eight dimensions — prosperity, schools, safety, lifestyle, affordability, market, commute, growth — each normalized onto a shared 0–100 scale, then weighted into one number.
The design decision isn’t the composite. It’s that the composite always decomposes. A single score is cheap to render and easy to distrust; showing the parts turns an opinion into an argument someone can disagree with specifically.
Missing data scores 50, never zero. A city without a walkability figure isn’t a bad city, it’s an unknown one. Scoring absence as failure is the most common way these models lie.
Nothing scores 100. The ceiling is 98. A perfect score invites the question of what perfect means, and there isn’t a good answer.
The prestige adjustment is documented, not hidden. Median price signals desirability the eight dimensions systematically under-read, so there’s a deliberate thumb on the scale — written into the code where anyone reviewing it will find it.
03 The pipeline that feeds it
Twelve APIs — census, schools, crime, tax, commute, walkability, EV infrastructure, college readiness, rents, price trends — normalized into 288 fields per city across 84 markets, refreshed weekly.
Some run on a schedule. Some are Python scripts on a calendar. Eleven fields are still entered by hand at the city level, which the diagram labels rather than hides.
04 The human still in the loop
Every AI case study claims a clean autonomous pipeline. Here’s what one actually looks like halfway through being built:
An employee writes a prompt. Pastes the result into a Google Sheet. A process picks it up, writes it into the linked WordPress field, and republishes. A person is still the runtime.
We’re removing that person one step at a time — which is slower and less impressive than a diagram, and it’s the honest version. The field instructions do real work in the meantime: “List 3–5 potential drawbacks. Being honest builds trust with users.” A prompt written into the schema is a design decision, not a technicality.
05 Asking in plain English
City pages answer “tell me about Plano.” Visitors are asking “where should I live?” Those are different questions and I built the wrong one first.
The answer is natural-language search: describe your constraints — best value near Frisco with A-rated schools — and get ranked cities back with the reasoning shown.
Rendering a number is deterministic. Interpreting a request isn’t. The open questions are all about what happens when the model is uncertain: how much reasoning to expose before it becomes noise, what to show when nothing matches, how to signal confidence, and what a wrong answer looks like when the user needs to correct it.
Head-to-head comparison is the same feature with a different interface — and it’s the one that makes the scoring model legible, because you can see exactly where a higher-scoring city loses.
06 Where it stands
Built end to end by directing AI rather than writing every line — the same leverage question as a design system, applied to code.
07 What I'd do differently
I built it backwards. Normally I start by making a thing look right and work outward. This started with the pipes — APIs, scoring, database — and the front door came last. The data layer has been solid for months while the homepage still said “Campaing.”
That order isn’t wrong exactly. It meant the product was real before it was presentable, which beats the reverse. But it also meant anyone who visited during that window judged it on the wrapper, and they were right to.
My own site contradicted itself. The homepage claimed 130 cities while the data said 84. Two numbers on one site and I never checked them against each other — the exact failure I’d catch immediately in someone else’s work.
What I’d keep: deciding early that every score decomposes. It constrained the interface in useful ways and it’s the thing that makes the product arguable rather than authoritative.