Apify builder
Valentin Yslas Address-level storm intelligence: machine-learning storm forecasts, model-based weather and reported storm history for any U.S. address.
Leaderboard position
out of 10,354
New actors
-
Total actors
#2,745
↓ -894
Monthly users
#3,257
↓ -949
Total users
#4,742
↓ -1,020
Monthly runs
#3,013
↑ +3,295
Total runs
#5,789
↑ +672
Portfolio stats
New actors
0
↓ -5
Total actors
5
+0
Monthly users
5
+0
Total users
10
+0
Monthly runs
141
↑ +129
Total runs
192
↑ +149
New actors
Each bar shows one day in the last 30 days.
Portfolio concentration
4 of 5 actors cover 80%+ of monthly users.
Per-peril machine-learning models identify addresses in the forecast footprint of storms over the next 24 hours to 10 days, across 10 perils. Each address row carries structure replacement value, occupants, storm type, forecast time, a risk label and prior storm-hit count. Could happen, not will.
20%
users
Send a street address or coordinates, get a model-derived forecast 1 to 48 hours ahead as flat time-series rows for CSV or Excel. Forecasts are probabilistic, not guarantees.
20%
users
Send a street address or coordinates, get model-derived current conditions and active severe-weather alerts as one flat row per location. Includes a short-term rain outlook and a detailed multi-field pull. Flat rows for CSV or Excel.
20%
users
Send counties and a peril; get street addresses where storms were reported and radar-detected, with date, type and severity. Address-level matching, severity filters in real units, reproducible via reference_id. Planned $0.05 per address. Not a damage assessment.
20%
users
ActorHunt MCP
ActorHunt gives paid members MCP access to keyword data, actor evidence, and Store movement for deciding what to build next.
