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Drive-time isochrones for ambulance coverage gap analysis

Drive-time isochrones for ambulance coverage gap analysis
Every EMS director knows the eight-minute benchmark. It is not a law in most jurisdictions — it is a clinical observation baked into decades of cardiac-arrest outcome data, and it is the number that appears in every city council budget presentation about why a new station needs funding. What most EMS directors do not have is a repeatable, programmatic way to answer the follow-up question: how man...

HIPAA-safe patient address pipeline with no_record and Divisions

HIPAA-safe patient address pipeline with no_record and Divisions
Geocoding patient addresses sounds like a one-afternoon integration. You have addresses. The API takes addresses. It returns coordinates. Ship it. It is not a one-afternoon integration. HIPAA's minimum-necessary rule applies to every system that touches protected health information, including a geocoding vendor. The moment a patient's home address transits your geocoding provider's infrastructure,...

Auto-assigning service tickets to the nearest technician

Auto-assigning service tickets to the nearest technician
Most field-service dispatch tools still rely on a human reading a map. A ticket comes in, a dispatcher eyeballs a board, picks the technician who looks closest, and rings their radio. That works up to about forty concurrent technicians and a quiet afternoon. It does not work at scale, and it breaks entirely when the dispatcher goes to lunch. This post replaces the human-with-a-map with a two-call...

A Dispatch Console That Handles 5,000 Stops a Day

A Dispatch Console That Handles 5,000 Stops a Day
Last-mile dispatch becomes a different problem at the 5,000-stops-per-day mark. Up to a few hundred stops, a dispatcher can solve the truck-loading puzzle in a spreadsheet and a route planner that runs once at 06:00. Past a few thousand, the puzzle is no longer about the optimal route — it is about responding to the reality that thirty stops were added at 09:30, four trucks broke down at 11:00, th...

Per-Policy Roof and Terrain Snapshots Without Satellite Licenses

Per-Policy Roof and Terrain Snapshots Without Satellite Licenses
Insurance underwriting needs an honest look at the property before it binds a policy. A spreadsheet row that reads *single-family residence, 2,100 sq ft, built 1978, replacement cost $410k* is not enough. The roof could be a 25-year-old shingle one storm away from total loss. The lot could be a 12° slope that turns every downpour into a mudslide. The neighbouring structure could be a detached gara...

Adding Elevation to Property Data — One API Call per Address

Adding Elevation to Property Data — One API Call per Address
A real-estate listing shows price, beds, square feet, schools, photos — and ignores the single number that explains half the flood story, half the view story, and most of the walkability story. That number is elevation . This post walks through enriching every address in a property catalog with elevation in one API call per address (or 500 per call), globally, in production. No GIS team required,...

Add IP Geolocation to Your Geocoding Stack in One Call

Add IP Geolocation to Your Geocoding Stack in One Call
If you already use CSV2GEO for address geocoding, you can now resolve IP addresses too — country, region, city, postal code, ASN, and county — through the same API key, in one call, with sub-millisecond response times. The endpoint is GET /api/v1/ip?ip=… . It's bundled into every plan including Free. No separate billing, no separate vendor. How accurate is IP geolocation, really? Honest answer:...

Reverse Geocoding Accuracy: How Far Off Are Your Results?

Reverse Geocoding Accuracy: How Far Off Are Your Results?
A coordinate goes into a reverse geocoder; an address comes out. The address is *somewhere near* the coordinate — but how near? The answer depends on the coordinate's source, the geocoder's index density in that area, and what "match" means in this dataset. In some neighborhoods you get the right house; in others the geocoder returns "the nearest known address," which might be 80 meters away. This...

Observability for Geocoding Pipelines: The Metrics That Matter

Observability for Geocoding Pipelines: The Metrics That Matter
A geocoding pipeline that "feels fine" can be silently degrading for weeks before someone notices. Match rates drift from 95% to 88% over a quarter; cache hit rate quietly falls because a key normalization regression made cache lookups miss; the API bill creeps up 20% and nobody connects it to the new product feature that doubled query volume. None of these get spotted without metrics. This post i...

Batch vs Realtime Geocoding: Picking the Right Default

Batch vs Realtime Geocoding: Picking the Right Default
The two patterns for getting addresses geocoded look completely different and someone always asks "which one do we need?" The honest answer is "you probably need both, but only one matters for the immediate decision." This post is the practical version: when batch makes sense, when realtime makes sense, the cost math, and the architecture that lets a single pipeline serve both modes without a rewr...