About
We predict the world we live in.
Flowershift started as an AI and machine learning studio. Now we build machine intelligence for the physical world.
Screenshot: About
Our story
From research lab to the paddock.
Placeholder: a couple of paragraphs on where Flowershift came from, the problem with generic forecasts, and why we think hyperlocal weather should be easy to use.
Placeholder: how the models work in plain language, and what makes the forecasts better for a specific location.
What we value
How we work.
Accuracy first
We measure our forecasts against what actually happened and publish the results.
Fast and simple
The answer you need in a glance, not a wall of charts.
Real support
A local team who answer your email, not a chatbot loop.
The team
The people behind the forecast.
A small team of data scientists, engineers and weather nerds.
Team Member
Founder & CEO
Placeholder bio: background, why they started Flowershift, and what they work on day to day.
Team Member
Head of Data Science
Placeholder bio: builds and tests the forecast models, and keeps them honest against what actually happened.
Team Member
Lead Engineer
Placeholder bio: runs the platform, the apps and the API.
Team Member
Customer Success
Placeholder bio: helps customers set up locations and alerts, and runs onboarding for Enterprise teams.
Forecast notes from the team
- Frost
Why this spring’s frost risk sits late in the season
· 6 min read
- Rain & flood
Reading a nowcast: what the next two hours can and can’t tell you
· 5 min read
- Seasonal outlook
El Niño, La Niña and your planting calendar
· 8 min read
- Research
How our models learn from forty years of observations
· 10 min read
- Wind
Gust fronts: the wind change that catches crane crews out
· 4 min read
- Heat & fire
Planning outdoor work around wet-bulb temperature
· 7 min read
- Storms
Spring storm season: hail, lightning and the events calendar
· 5 min read
Stop guessing the weather.
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