Scan to Action In Minutes: Scan an aisle in seconds and an entire store in minutes—going from scan to action while reps are still in-store

Lower Labor Costs: Automate the bulk (up to 70%) of reps' previously-manual, tedious tasks and save over 80% of time spent in-store

Faster time to value: models begin learning as soon as shelf images are captured—onboarding drops from months to weeks.

Greater Scalability: Enables rapid rollout across new categories, markets, and retailers without retraining bottlenecks

Take pictures of the aisle, side-stepping with each photo, and stitching images together from multiple photos; requiring longer process time in-store

More human effort required to intervene and verify data before getting accurate results

Require large, annotated image datasets before product recognition can begin with each new product manually labeled and fed into the model

Each new category requires a new set of reference images for adding new products and retraining the model before product recognition can happen in new markets or with new products