Dahlia AI: Excess Inventory Demand Forecasting | Pollen

Dahlia

Demand intelligence for deciding what should move before value decays.

Dahlia models inventory aging, shelf-life urgency, buyer demand, category fit, and channel confidence so Lily can prioritize recovery routes before disposal dependency rises.

STR
BUYER FIT
AGING
URGENCY
CHANNEL FIT

DAHLIA DEMAND MAP

WEEKLY SIGNAL

74%
controlled recovery fit

P1 FRESH
P2 ACTIVE
P3 URGENT
DISTRIBUTOR
EMPLOYEE
EXPORT
CATALOG

WHAT DAHLIA ACTUALLY DOES

01
Scores recovery urgency.
Reads shelf-life, batch, quantity, category, and market context to identify stock that needs routing before value collapses.

02
Finds where demand exists.
Compares buyer history, similar buyers, channel behavior, active demand, and regional fit to avoid generic buyer blasts.

03
Feeds Lily and Sage.
Turns demand into ranked buyer segments and channel signals so Sage can allocate catalogs, campaigns, auctions, or store workflows.

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