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Case study · Retail execution · Computer vision

Shelf
Intelligence

AI that reads the store shelf from field photos: which products are present, whether prices match the plan, and what competitors are doing in the same aisle.

EngagementRolling out in the field
InputPhotos from field team routes
ApproachVision models · Planogram checks
StatusActive build
Built for
Tika Carozzi VSPT CCU

The problem

Once products leave the warehouse, brands lose sight of the shelf. Prices drift from what was agreed, products go missing from the gondola, and competitor promotions appear without warning. Traditional audits are slow, sampled and arrive too late to act on.

What it does

The system works the way field operations already work: people on routes photograph the shelves they visit. From each photo, AI does the auditing.

  • Detects whether products are actually on the shelf, and where on the gondola they sit.
  • Reads the displayed prices and flags deviations against the intended price list.
  • Identifies competitor products in the same aisle, their pricing and their promotions.
  • Turns every deviation into a concrete finding the right person can act on.

How it runs in the field

No new hardware and no separate audit visits. Capture is part of the route the field team already walks: take the photo, move on. Analysis happens automatically, so what used to be a clipboard exercise becomes a continuous, store-by-store picture of how execution actually looks.

What it watches
Price accuracyDisplayed prices checked against the plan, deviations flagged per store.
Shelf presenceProducts found, missing or misplaced on the gondola.
Competitive watchCompetitor products, prices and promotions in the same aisle.

In active development with field teams now.

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