AERI
AERI
INTELLIGENCE FOR THE OPEN SKY
AERI fuses phones, sensors, and satellites into an adaptive mesh — achieving accurate 3‑hour awareness where radar coverage is weakest.
To democratize severe‑weather intelligence by turning everyday devices into a living atmospheric network that self‑calibrates and warns faster.
Apple‑style minimal fusion of device barometers and environmental signals with satellite & public data.
Regional models continuously correct bias using outcomes — adapting every few hours.
Each observation strengthens nearby forecasts — a feedback loop that never sleeps.
The 3‑hour horizon captures coherent atmospheric changes while enabling rapid feedback and recalibration.
Thermal gradients and pressure waves remain structured over ~3 hours before chaos dominates.
Every forecast is validated against device & satellite readings; bias maps update continuously.
Thousands of micro‑models learn local behavior — improving rural & open‑land performance.
Mobile, radar, and GOES cloud‑top signals confirm storm evolution and reduce false alarms.
| Capability | AERI | Traditional Apps |
|---|---|---|
| Data Inputs | Mobile sensors + satellites + public feeds | Radar + satellites only |
| Update Cycle | Self‑correcting within ~3h | 6–12 hour refresh |
| Rural/Open‑Land Accuracy | Improves via regional learning | Limited due to radar gap |
| Personalization | Local micro‑models by region | Broad regional averages |
| Research Access | Open collaboration & methods | Closed pipelines |
| Adaptivity | Feedback from outcomes | Mostly static models |
Live visualization, local risk pills, and smart alerts tuned for your area.
Research‑grade storm timelines and impact analysis for scientists and responders.
Bilingual voice alerts with quiet‑hours logic — online and locally.
Our adaptive AI backend — regional calibration, validation, and open research.
Extend awareness into radar blind spots using device sensors and public data fused with physics.
Study pre‑formation signals and micro‑pressure anomalies to detect potential earlier and reduce noise.
Analyze motion vectors to anticipate likely paths — informing faster, localized alerts.
Design alert pathways for individuals during power/cellular outages — including offline caching & alternative channels.