The Grape Was Framed

Why Slip-and-Fall Investigations Need to Look Beyond the Moment of Impact

A customer goes down in the produce aisle. The footage is clear. The grape is right there.

The General Liability report practically writes itself. Except sometimes the grape didn’t fall. Sometimes it was placed. That’s the premise behind a real investigation where facial recognition technology — typically deployed for organized retail crime — cracked open a coordinated fraud scheme that standard incident footage never could have revealed. The case didn’t turn on what the aisle cameras captured.

It turned on a pattern: two people arriving together, familiar faces appearing across multiple stores, timestamps that connected a parking lot to a produce aisle that bad actors were counting on staying disconnected.

The result wasn’t just a closed claim. It was a reframe of how the technology works — and what it’s actually capable of. Forward-thinking retailers are discovering that face matching isn’t just a shoplifting tool. It’s an investigative layer that runs underneath theft, fraud, safety, liability, and vendor cases alike.

And in a controlled study, an investigator using the technology identified a suspect across 19 stores in ten minutes. Their unassisted counterpart spent fifteen hours and found eight fewer locations. The pattern was always there. Most investigators just couldn’t see it from where they were standing.

Read the full whitepaper here to see how the investigation unfolded — and what it means for how your team approaches general liability claims.

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