The whole geocoder runs in your browser. You type an address, you get a rooftop coordinate, and no server ever sees your query — the gazetteer it resolves against is a SQLite database sitting on a CDN, and the page reads it with HTTP range requests, a few kilobytes at a time. It's a lovely trick. We were proud of it. Then we counted the requests it took to find a single city, and the number was 243.
Overnight I taught the geocoder Canada. Or I thought I did. I pulled 843,000 Canadian postcodes, computed a centroid for every one, spot-checked the result — M5H 2N2, downtown Toronto, 43.652, −79.382, dead on — and validated the database every way I could read it: every postcode present, every coordinate right, and no other data disturbed. Green, top to bottom. This morning, before flipping it live, I asked the demo to find a Toronto address. It dropped the pin in Ohio.
Open the demo, open your browser's network tab, and type an address. It resolves to a rooftop coordinate: 1600 Pennsylvania Ave lands on the actual building, within about ten meters. The network tab shows no request carrying your address. The parser ran in the page. The gazetteer it resolved against is a file on a CDN, and the page read it a few kilobytes at a time. The query stayed on your machine.
Every geocoder turns an address into a coordinate. Almost none of them will tell you where that coordinate came from. You get a latitude, a longitude, and a vague confidence enum, and when it's wrong you have no thread to pull — no way to know whether the point came from a federal data release, a county GIS office, or a straight line drawn down the middle of a street. Mailwoman keeps the source on every point. Here's New York, every dot colored by the open dataset it came from.
Three public datasets land on your desk. The national provider registry — NPPES, every NPI in the country. A federal telecom-funding file from the FCC's Rural Health Care program, one part of the Universal Service Fund. A state list of licensed nursing facilities from Texas HHSC. You want to know which records describe the same provider, and not one of the three shares an identifier with the other two. The NPI is internal to NPPES. The funding file keys on its own SPIN. The state list has its own facility ID. There's no crosswalk, because none of the three publishers ever built one.
Suppose you have a set of records and no key to join them on. A clinic appears in the federal provider registry, again in a state licensing export, and a third time in a funding-program spreadsheet that someone typed by hand. The files share no identifier. The provider number is internal to one publisher, and the facility ID is internal to another. You must judge which records describe the same place for each pair, repeated a few million times.
We're trained to be suspicious of a score that looks too good. The scores that say you failed get a free pass — and this week three of them were lying, including one that had us reporting kilometers for a product that lands in meters.
We taught our parser the hardest street cases it gets wrong, the aggregate score climbed, and we almost shipped it. Then a held-out probe caught it forgetting what a town is.
Mailwoman ships two address parsers in the same box.
Our failure taxonomy now ranks boundary instability first among the parser's weaknesses. We