Forecast the pour before the first drink is served.
PourCaster helps event operators forecast alcohol demand, track live inventory burn, and learn from actual consumption — so teams reduce runouts, overbuying, and last-minute product chaos.
Inventory watch
Plan → Track → Learn
PourCaster is built around the actual event workflow: forecast the buy, monitor inventory during the event, then turn actual consumption into a better baseline for next time.
Plan the order
Use last-year attendance, ticket velocity, consumed volume, weather, sales hours, and AI intensity prediction to build a defensible buy recommendation.
Track live burn
During the event, count what remains and what back-stock was released. PourCaster calculates consumption, runout risk, and whether to hold or release stock.
Learn from actuals
After the event, compare forecast vs actual and generate a cleaner next-year baseline instead of starting over with gut feel.
No more guessing whether the crowd is “light,” “average,” or “heavy.” PourCaster predicts intensity from real event signals and still gives operators an override.
Signals PourCaster considers
Built around real event use cases.
PourCaster is for operators who need a practical beverage forecast, not a generic party calculator.
During the event, count what’s left. PourCaster calculates what was used.
Staff do not need to estimate consumed volume in the middle of a busy event. They enter remaining inventory and back-stock releases; PourCaster turns that into burn pace, projected runout, and projected leftover.
Inventory-based tracking
Beer gallons, 9L cases, loose bottles, released back-stock, and remaining counts become calculated consumption.
Runout risk alerts
Green/yellow/red status shows what is on pace, what is running hot, and what needs back-stock attention.
Next-year baseline
Actuals feed a variance report and recommended future baseline instead of relying on memory.
Ready to forecast your next event?
We’re preparing PourCaster for App Store rollout and client-specific event planning pilots.