Don't start with AI. Fix what AI sees.
KOLSHEE first creates a connected, permissioned record of transactions, inventory, purchasing, and fulfillment. Forecasting and AI become useful only after that record is reliable.
Stores on the same lane buy together — better unit cost, fewer empty trucks.
On-time rate, fill rate, defect rate, dispute trail — visible network-wide.
Surface SKUs the network is looking for but no one is selling on the lane.
Lane-level price benchmarks for inputs and consumer baskets.
Match end-of-day surplus across stores and donate or redirect.
A later-stage capability, activated after operating data is sufficiently reliable and representative.
Illustrative signals — what the network will surface.
Kolshee is pre-launch. These examples show the kind of signal the coordination layer is designed to produce once stores are operating — they are not reported results.
Seven stores buying tahini together on one lane could cut input cost by roughly a fifth.
A corridor-wide spike in demand for a seasonal SKU would reach suppliers the same week.
A supplier drifting below the network on-time average gets flagged before the invoices arrive.