See the danger before the spark.

PRECEDE scores wildfire danger across 4,846 monitored points in 11 western states, refreshed around the clock over a rolling ~7-day horizon, so the people who protect communities can act before a fire starts, not after.

The problem

Satellites tell you a fire has already started. They cannot tell you that this afternoon, in a particular stretch of country, conditions have lined up so that whatever ignites will be hard to stop. That gap, the hours before a spark, is where lives and homes are saved or lost. PRECEDE was built to close it.

What PRECEDE does

PRECEDE watches the fire-prone parts of 11 western states from 4,846 monitored points and scores wildfire danger continuously, well below the level of a whole county. Each point gets a single risk number, refreshed around the clock and looking ahead over a rolling ~7-day horizon, that reflects whether conditions have entered the zone where a fire would likely turn destructive. Every point also carries building-exposure context from a 26.7-million-building census. When a point crosses that line, it surfaces as an alert someone can act on. The output is simple on purpose: a score, a place, a trend, and a clear threshold for when to pay attention.

Source: Microsoft US Building Footprints

Knowing when it is safe, too (in development)

A prescribed-burn timing feature, reading the same conditions to flag when it may be safer to put fire on the ground on purpose, is in development. It is not part of the validated core, it is not available today, and we make no capability or performance claim for it yet. Knowing when not to burn is as valuable as knowing when you can.

How we hold ourselves accountable

We hold ourselves to a written, pre-committed standard. Our 2026 season-end validation was preregistered and frozen on 2026-08-27, with its pass/fail rules fixed before any 2026 outcome was known, so the test cannot be changed after the fact to fit the result. Full technical documentation is available on request, without us revealing how the model works internally.