Retail
Shelves and queues out front, supplier paper at the back door.
Retail’s data problem is split across the store: the floor is visual, the back door is paper. Raru Vision reads the first; Dodo reads the second.
What we solve
Shelves that report themselves
Raru Vision · scene understandingStock levels, empty facings, misplaced products, and planogram drift — read per fixture, per pass, all day, from the cameras you already own.
Know the store’s rhythm
Raru Vision · scene understandingFootfall by zone, billing-queue build-up, and dwell at fixtures — counted as anonymous totals, so ops decisions run on numbers instead of walk-throughs.
Loss prevention with context
Raru Vision · scene understandingPilferage-risk moments — items leaving shelves outside a checkout path, unattended zones, after-hours movement — flagged for a human to review, with the clip attached.
The back door runs on paper
Dodo · document understandingGRNs, supplier invoices, and delivery challans arrive with every truck. Dodo turns them into structured records at the receiving desk, so inventory reflects reality the same hour.
GRN ↔ invoice ↔ PO, matched
Dodo · document understandingThree documents describe every delivery, and they rarely agree. Extract all three into one shape and the mismatches surface themselves — before payment, not after.
Bring us your version of these problems. We’ll show you the record it becomes.
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