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Product recognition, without the barcode
Instant identification of fresh produce and unpackaged items on the scales, scanners and self-checkouts already in your stores.
Fresh is where grocery makes its margin and loses its throughput. Every loose item needs a human to identify it, find the code and get it right. Edgify removes that step entirely.
What it does
- Identifies loose produce automatically, including through plastic bags
- Removes the PLU lookup, the code sheet and the scrolling picker
- Cuts mislabelling and false selections that quietly erode fresh margin
- Runs on existing self-service scales, scanners, SCO and staffed lanes
Results in production
| Measure | Result |
|---|---|
| Checkout speed on fresh items | 5x faster |
| Mislabelling | 69% reduction |
| Recognition accuracy | 99% |
| Real-world training samples | 400M+ |
Why it works where others haven't
Produce recognition demos well and deploys badly. A model trained on clean catalogue imagery meets a real store and fails on the things that make stores real: condensation on the bag, a half-empty display, an unfamiliar local variety, overhead lighting that changes through the day.
Edgify trains on the device, in that store, on that store's items. The model that runs in a Tel Aviv branch is not the model that runs in a Manchester one, and neither of them needed an annotation contract to get there.
Beyond the checkout
The same recognition layer runs in back-of-house for waste tracking, in fulfilment for weighing and packing accuracy, and across food service for operational verification. One model estate, several P&L lines.
See your store through Edgify's eyes
A 12-week pilot on the hardware you already own. Measurable ROI, from day one.
Frequently asked questions
How does Edgify recognise products without barcodes?
Cameras on the scale, scanner or self-checkout capture the item and an on-device computer vision model identifies it in real time, including loose produce and items inside plastic bags. No cloud connection is needed.
Which devices does product recognition run on?
Self-service scales, scanner-scales, self-checkouts and staffed lanes from the major hardware manufacturers. It runs on the devices' existing compute, certified across 40+ OEMs.
Does the model improve over time?
Yes. Every confirmed transaction becomes a labelled training example, and devices learn locally. Model improvements are shared across the estate while raw images stay in the store.